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So missing values due to the participant are eliminated by this type of questionnaire, though this method may not be permitted by an ethics board overseeing the research. In survey research, it is common to make multiple efforts to contact each individual in the sample, often sending letters to attempt to persuade those who have decided not to participate to change their minds. However, such techniques can either help or hurt in terms of reducing the negative inferential effects of missing data, because the kind of people who are willing to be persuaded to participate after initially refusing or not being home are likely to be significantly different from the kinds of people who will still refuse or remain unreachable after additional effort.
228:, and some scholars now recommend 20 to 100 or more. Any multiply-imputed data analysis must be repeated for each of the imputed data sets and, in some cases, the relevant statistics must be combined in a relatively complicated way. Multiple imputation is not conducted in specific disciplines, as there is a lack of training or misconceptions about them. Methods such as listwise deletion have been used to impute data but it has been found to introduce additional bias. There is a beginner guide that provides a step-by-step instruction how to impute data.
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study of the relation between IQ and income, if participants with an above-average IQ tend to skip the question âWhat is your salary?â, analyses that do not take into account this missing at random (MAR pattern (see below)) may falsely fail to find a positive association between IQ and salary. Because of these problems, methodologists routinely advise researchers to design studies to minimize the occurrence of missing values. Graphical models can be used to describe the missing data mechanism in detail.
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to fill in a depression survey but this has nothing to do with their level of depression, after accounting for maleness. Depending on the analysis method, these data can still induce parameter bias in analyses due to the contingent emptiness of cells (male, very high depression may have zero entries). However, if the parameter is estimated with Full
Information Maximum Likelihood, MAR will provide asymptotically unbiased estimates.
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186:âby directly applying methods unaffected by the missing values. One systematic review addressing the prevention and handling of missing data for patient-centered outcomes research identified 10 standards as necessary for the prevention and handling of missing data. These include standards for study design, study conduct, analysis, and reporting.
166:, do not usually take into account the structure of the missing data and so development of new formulations is needed to deal with structured missingness appropriately or effectively. Finally, characterising structured missingness within the classical framework of MCAR, MAR, and MNAR is a work in progress.
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occurs when the missingness is not random, but where missingness can be fully accounted for by variables where there is complete information. Since MAR is an assumption that is impossible to verify statistically, we must rely on its substantive reasonableness. An example is that males are less likely
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Understanding the reasons why data are missing is important for handling the remaining data correctly. If values are missing completely at random, the data sample is likely still representative of the population. But if the values are missing systematically, analysis may be biased. For example, in a
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In some practical application, the experimenters can control the level of missingness, and prevent missing values before gathering the data. For example, in computer questionnaires, it is often not possible to skip a question. A question has to be answered, otherwise one cannot continue to the next.
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The graph shows the probability distributions of the estimations of the expected intensity of depression in the population. The number of cases is 60. Let the true population be a standardised normal distribution and the non-response probability be a logistic function of the intensity of depression.
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the missing data. Rubin (1987) argued that repeating imputation even a few times (5 or less) enormously improves the quality of estimation. For many practical purposes, 2 or 3 imputations capture most of the relative efficiency that could be captured with a larger number of imputations. However, a
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The presence of structured missingness may be a hindrance to make effective use of data at scale, including through both classical statistical and current machine learning methods. For example, there might be bias inherent in the reasons why some data might be missing in patterns, which might have
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Structured missingness commonly arises when combining information from multiple studies, each of which may vary in its design and measurement set and therefore only contain a subset of variables from the union of measurement modalities. In these situations, missing values may relate to the various
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In the case of MCAR, the missingness of data is unrelated to any study variable: thus, the participants with completely observed data are in effect a random sample of all the participants assigned a particular intervention. With MCAR, the random assignment of treatments is assumed to be preserved,
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sampling methodologies used to collect the data or reflect characteristics of the wider population of interest, and so may impart useful information. For instance, in a health context, structured missingness has been observed as a consequence of linking clinical, genomic and imaging data.
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Missing data can also arise in subtle ways that are not well accounted for in classical theory. An increasingly encountered problem arises in which data may not be MAR but missing values exhibit an association or structure, either explicitly or implicitly. Such missingness has been described as
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if the events that lead to any particular data-item being missing are independent both of observable variables and of unobservable parameters of interest, and occur entirely at random. When data are MCAR, the analysis performed on the data is unbiased; however, data are rarely MCAR.
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Woods, Adrienne D.; Gerasimova, Daria; Van Dusen, Ben; Nissen, Jayson; Bainter, Sierra; Uzdavines, Alex; Davis-Kean, Pamela E.; Halvorson, Max; King, Kevin M.; Logan, Jessica A. R.; Xu, Menglin; Vasilev, Martin R.; Clay, James M.; Moreau, David; Joyal-Desmarais, Keven (2023-02-23).
70:
because governments or private entities choose not to, or fail to, report critical statistics, or because the information is not available. Sometimes missing values are caused by the researcherâfor example, when data collection is done improperly or mistakes are made in data entry.
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Mitra, Robin; McGough, Sarah F.; Chakraborti, Tapabrata; Holmes, Chris; Copping, Ryan; Hagenbuch, Niels; Biedermann, Stefanie; Noonan, Jack; Lehmann, Brieuc; Shenvi, Aditi; Doan, Xuan Vinh; Leslie, David; Bianconi, Ginestra; Sanchez-Garcia, Ruben; Davies, Alisha (2023-01-25).
54:
is a type of missingness that can occur in longitudinal studiesâfor instance studying development where a measurement is repeated after a certain period of time. Missingness occurs when participants drop out before the test ends and one or more measurements are missing.
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is an approach in which values of the statistics which would be computed if a complete dataset were available are estimated (imputed), taking into account the pattern of missing data. In this approach, values for individual missing data-items are not usually imputed.
137:(also known as nonignorable nonresponse) is data that is neither MAR nor MCAR (i.e. the value of the variable that's missing is related to the reason it's missing). To extend the previous example, this would occur if men failed to fill in a depression survey
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A special class of problems appears when the probability of the missingness depends on time. For example, in the trauma databases the probability to lose data about the trauma outcome depends on the day after trauma. In these cases various non-stationary
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Missing data can occur because of nonresponse: no information is provided for one or more items or for a whole unit ("subject"). Some items are more likely to generate a nonresponse than others: for example items about private subjects such as income.
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Model based techniques, often using graphs, offer additional tools for testing missing data types (MCAR, MAR, MNAR) and for estimating parameters under missing data conditions. For example, a test for refuting MAR/MCAR reads as follows:
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These forms of missingness take different types, with different impacts on the validity of conclusions from research: Missing completely at random, missing at random, and missing not at random. Missing data can be handled similarly as
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Samuelson and Spirer (1992) discussed how missing and/or distorted data about demographics, law enforcement, and health could be indicators of patterns of human rights violations. They gave several fairly well documented examples.
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In the comparison of two paired samples with missing data, a test statistic that uses all available data without the need for imputation is the partially overlapping samples t-test. This is valid under normality and assuming MCAR
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Missing data reduces the representativeness of the sample and can therefore distort inferences about the population. Generally speaking, there are three main approaches to handle missing data: (1)
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Finally, the estimands that emerge from these techniques are derived in closed form and do not require iterative procedures such as
Expectation Maximization that are susceptible to local optima.
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Different model structures may yield different estimands and different procedures of estimation whenever consistent estimation is possible. The preceding estimand calls for first estimating
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Spatial and temporal Trend
Analysis of Long Term rainfall records in data-poor catchments with missing data, a case study of Lower Shire floodplain in Malawi for the period 1953â2010
2153:
Zarate LE, Nogueira BM, Santos TR, Song MA (2006). "Techniques for
Missing Value Recovering in Imbalanced Databases: Application in a marketing database with massive missing data".
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In many cases model based techniques permit the model structure to undergo refutation tests. Any model which implies the independence between a partially observed variable
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Li, Tianjing; Hutfless, Susan; Scharfstein, Daniel O.; Daniels, Michael J.; Hogan, Joseph W.; Little, Roderick J.A.; Roy, Jason A.; Law, Andrew H.; Dickersin, Kay (2014).
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When data falls into MNAR category techniques are available for consistently estimating parameters when certain conditions hold in the model. For example, if
1415:"Standards should be applied in the prevention and handling of missing data for patient-centered outcomes research: a systematic review and expert consensus"
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Methods which take full account of all information available, without the distortion resulting from using imputed values as if they were actually observed:
1493:
Graham J.W.; Olchowski A.E.; Gilreath T.D. (2007). "How Many
Imputations Are Really Needed? Some Practical Clarifications of Multiple Imputation Theory".
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to missingness. An analysis is robust when we are confident that mild to moderate violations of the technique's key assumptions will produce little or no
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The conclusion is: The more data is missing (MNAR), the more biased are the estimations. We underestimate the intensity of depression in the population.
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In situations where missing values are likely to occur, the researcher is often advised on planning to use methods of data analysis methods that are
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Potthoff, R.F.; Tudor, G.E.; Pieper, K.S.; Hasselblad, V. (2006). "Can one assess whether missing data are missing at random in medical studies?".
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Mohan, K.; Van den Broeck, G.; Choi, A.; Pearl, J. (2014). "An
Efficient Method for Bayesian Network Parameter Learning from Incomplete Data".
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implications in predictive fairness for machine learning models. Furthermore, established methods for dealing with missing data, such as
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Van den Broeck J, Cunningham SA, Eeckels R, Herbst K (2005), "Data cleaning: detecting, diagnosing, and editing data abnormalities",
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46:. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data.
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1670:"Test Statistics for the Comparison of Means for Two Samples That Include Both Paired and Independent Observations"
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Messner SF (1992). "Exploring the
Consequences of Erratic Data Reporting for Cross-National Research on Homicide".
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Molenberghs, Geert; Fitzmaurice, Garrett; Kenward, Michael G.; Tsiatis, Anastasios; Verbeke, Geert, eds. (2015),
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645:{\displaystyle {\begin{aligned}P(X,Y)&=P(X|Y)P(Y)\\&=P(X|Y,R_{x}=0,R_{y}=0)P(Y|R_{y}=0)\end{aligned}}}
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435:(Remark: These tests are necessary for variable-based MAR which is a slight variation of event-based MAR.)
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Mohan, Karthika; Pearl, Judea; Tian, Jin (2013). "Graphical Models for
Inference with Missing Data".
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is a method of constructing new data points within the range of a discrete set of known data points.
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Methods which involve reducing the data available to a dataset having no missing values include:
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van Ginkel, Joost R.; Linting, Marielle; Rippe, Ralph C. A.; van der Voort, Anja (2020-05-03).
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Jackson, James; Mitra, Robin; Hagenbuch, Niels; McGough, Sarah; Harbron, Chris (2023-07-05),
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432:. Failure to satisfy this condition indicates that the problem belongs to the MNAR category.
1911:"Handling missing data in large healthcare dataset: A case study of unknown trauma outcomes"
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1296:"Chapter 3. Use of Incomplete and Distorted Data in Inference About Human Rights Violations"
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Mohan, Karthika; Pearl, Judea (2014). "On the testability of models with missing data".
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Recoverability and
Testability of Missing data: Introduction and Summary of Results
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Nursing
Research: Generating and Assessing Evidence for Nursing Practice, 9th ed
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1208:. Philadelphia, USA: Wolters Klower Health, Lippincott Williams & Wilkins.
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182:âwhere samples with invalid data are discarded from further analysis and (3)
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IEEE International Conference on Systems, Man and Cybernetics, 2006. SMC '06
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Reducing Survey Nonresponse: Lessons Learned from the European Social Survey
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1629:"Best practices for addressing missing data through multiple imputation"
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techniques are not robust to missingness, and require to "fill in", or
20:
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but that is usually an unrealistically strong assumption in practice.
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Presented at Causal Modeling and Machine Learning Workshop, ICML-2014
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too-small number of imputations can lead to a substantial loss of
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Hand, David J.; AdĂšr, Herman J.; Mellenbergh, Gideon J. (2008).
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201:, or distortion in the conclusions drawn about the population.
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1909:
Mirkes, E.M.; Coats, T.J.; Levesley, J.; Gorban, A.N. (2016).
1625:
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2229:
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1338:
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1883:(Technical report). UCLA Computer Science Department, R-417.
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721:
denote the observed portions of their respective variables.
2320:
198:
178:âwhere values are filled in the place of missing data, (2)
32:
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169:
1908:
2152:
1733:"Max-margin Classification of Data with Absent Features"
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1700:
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Samuelson, Douglas A.; Spirer, Herbert F. (1992-12-31),
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475:
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Autoregressive conditional heteroskedasticity (ARCH)
1864:
1302:, University of Pennsylvania Press, pp. 62â78,
1142:
Advances in Neural Information Processing Systems 26
937:
can be submitted to the following refutation test:
1110:
424:should be independent on the missingness status of
3604:
989:
929:
902:
879:and the missingness indicator of another variable
864:
827:
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644:
410:{\displaystyle X\perp \!\!\!\perp R_{y}|(R_{x},Z)}
409:
952:
951:
950:
366:
365:
364:
250:In the mathematical field of numerical analysis,
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2186:London School of Hygiene & Tropical Medicine
2029:
2005:
1982:Acock AC (2005), "Working with missing values",
1668:Derrick, B; Russ, B; Toher, D; White, P (2017).
1468:; Billiet, J.; Koch, A.; Fitzgerald, R. (2010).
1342:"Learning from data with structured missingness"
1203:
990:{\displaystyle X\perp \!\!\!\perp R_{y}|R_{x}=0}
3690:Multivariate adaptive regression splines (MARS)
2041:
1293:
100:
1707:"Max-margin Classification of incomplete data"
1138:
466:is random. The estimand in this case will be:
2245:
2017:
1815:Modeling and Reasoning with Bayesian Networks
1674:Journal of Modern Applied Statistical Methods
1460:
1458:
462:can still be estimated if the missingness of
351:partially observed, the data should satisfy:
2020:Handling Missing Data in Ranked Set Sampling
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2290:
2252:
2238:
1902:
1873:
1797:
1727:Chechik, Gal; Heitz, Geremy; Elidan, Gal;
1701:Chechik, Gal; Heitz, Geremy; Elidan, Gal;
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1997:
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794:is observed regardless of the status of
761:from complete data and multiplying it by
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148:
1981:
1812:
1764:"Partial Identification in Econometrics"
1737:The Journal of Machine Learning Research
1155:
1134:
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129:
90:
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1830:Statistical Methods in Medical Research
1800:Proceedings of AISTAT-2014, Forthcoming
1785:10.1146/annurev.economics.050708.143401
1199:
1197:
442:explains the reason for missingness in
170:Techniques of dealing with missing data
4558:
4216:KaplanâMeier estimator (product limit)
2096:Semiparametric Theory and Missing Data
2084:Statistical Analysis with Missing Data
1874:Pearl, Judea; Mohan, Karthika (2013).
1277:Statistical Analysis with Missing Data
1221:"On Biostatistics and Clinical Trials"
1097:
58:Data often are missing in research in
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2289:
2233:
1761:
1714:Neural Information Processing Systems
1334:
1332:
1149:
1129:
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4226:Accelerated failure time (AFT) model
2184:, Department of Medical Statistics,
2056:Handbook of Missing Data Methodology
1218:
1194:
117:
4538:
3821:Analysis of variance (ANOVA, anova)
2673:
2157:. Vol. 3. pp. 2658â2664.
1661:
1613:Flexible imputation of missing data
1071:Journal of Quantitative Criminology
261:
107:missing completely at random (MCAR)
13:
3916:CochranâMantelâHaenszel statistics
2542:Pearson product-moment correlation
1975:
1329:
1158:Scandinavian Journal of Statistics
1024:Expectationâmaximization algorithm
420:In words, the observed portion of
294:expectation-maximization algorithm
233:expectation-maximization algorithm
14:
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2170:
2068:Missing Data Analysis in Practice
2018:Bouza-Herrera, Carlos N. (2013),
1915:Computers in Biology and Medicine
1552:Journal of Personality Assessment
4537:
4525:
4513:
4500:
4499:
4290:
2220:PROC MI and PROC MIANALYZE - SAS
2066:Raghunathan, Trivellore (2016),
1999:10.1111/j.1741-3737.2005.00191.x
1967:from the original on 2016-08-05.
1945:10.1016/j.compbiomed.2016.06.004
1419:Journal of Clinical Epidemiology
280:
239:
4175:Least-squares spectral analysis
2094:Tsiatis, Anastasios A. (2006),
1887:
1821:
1806:
1791:
1755:
1731:; Koller, Daphne (2008-06-01).
1720:
1705:; Koller, Daphne (2008-06-01).
1694:
1619:
1604:
1539:
1486:
1406:
1382:
1253:from the original on 2015-09-10
450:itself has missing values, the
3156:Mean-unbiased minimum-variance
2259:
1985:Journal of Marriage and Family
1431:10.1016/j.jclinepi.2013.08.013
1287:
1264:
1239:
1212:
1062:
967:
859:
852:
845:
822:
815:
808:
790:estimated from cases in which
777:
771:
748:
741:
734:
635:
615:
608:
602:
557:
550:
534:
528:
522:
515:
508:
495:
483:
452:joint probability distribution
428:conditional on every value of
404:
385:
381:
141:of their level of depression.
1:
4469:Geographic information system
3685:Simultaneous equations models
2175:
2032:Applied Missing Data Analysis
1817:. Cambridge University Press.
1564:10.1080/00223891.2018.1530680
1055:
1039:Inverse probability weighting
204:
3652:Coefficient of determination
3263:Uniformly most powerful test
2120:10.1371/journal.pmed.0020267
1633:Infant and Child Development
135:Missing not at random (MNAR)
101:Missing completely at random
7:
4221:Proportional hazards models
4165:Spectral density estimation
4147:Vector autoregression (VAR)
3581:Maximum posterior estimator
2813:Randomized controlled trial
2208:
1472:. Oxford: Wiley-Blackwell.
1346:Nature Machine Intelligence
1300:Human Rights and Statistics
1011:
307:Discriminative approaches:
154:âstructured missingnessâ.
10:
4587:
3981:Multivariate distributions
2401:Average absolute deviation
1772:Annual Review of Economics
1615:(2nd ed.). CRC Press.
1368:10.1038/s42256-022-00596-z
319:methods may also be used.
243:
208:
4495:
4449:
4386:
4339:
4302:
4298:
4285:
4257:
4239:
4206:
4197:
4155:
4102:
4063:
4012:
4003:
3969:Structural equation model
3924:
3881:
3877:
3852:
3811:
3777:
3731:
3698:
3660:
3627:
3623:
3599:
3539:
3448:
3367:
3331:
3322:
3305:Score/Lagrange multiplier
3290:
3243:
3188:
3114:
3105:
2915:
2911:
2898:
2857:
2831:
2783:
2738:
2720:Sample size determination
2685:
2681:
2668:
2572:
2527:
2501:
2483:
2439:
2391:
2311:
2302:
2298:
2285:
2267:
2163:10.1109/ICSMC.2006.385265
2030:Enders, Craig K. (2010),
2006:Allison, Paul D. (2001),
1842:10.1191/0962280206sm448oa
1687:10.22237/jmasm/1493597280
1517:10.1007/s11121-007-0070-9
1308:10.9783/9781512802863-006
1204:Polit DF Beck CT (2012).
105:Values in a data set are
4464:Environmental statistics
3986:Elliptical distributions
3779:Generalized linear model
3708:Simple linear regression
3478:HodgesâLehmann estimator
2935:Probability distribution
2844:Stochastic approximation
2406:Coefficient of variation
2078:Little, Roderick J. A.;
2042:Graham, John W. (2012),
1813:Darwiche, Adnan (2009).
1271:Little, Roderick J. A.;
331:For any three variables
82:
4124:Cross-correlation (XCF)
3732:Non-standard predictors
3166:LehmannâScheffĂ© theorem
2839:Adaptive clinical trial
2203:Missing values-envision
1611:van Buuren, S. (2018).
714:{\displaystyle R_{y}=0}
681:{\displaystyle R_{x}=0}
289:Generative approaches:
211:Imputation (statistics)
123:Missing at random (MAR)
4571:Statistical data types
4520:Mathematics portal
4341:Engineering statistics
4249:NelsonâAalen estimator
3826:Analysis of covariance
3713:Ordinary least squares
3637:Pearson product-moment
3041:Statistical functional
2952:Empirical distribution
2785:Controlled experiments
2514:Frequency distribution
2292:Descriptive statistics
991:
931:
904:
866:
865:{\displaystyle P(Y|X)}
829:
828:{\displaystyle P(X|Y)}
784:
755:
754:{\displaystyle P(X|Y)}
715:
682:
646:
411:
343:is fully observed and
323:Model-based techniques
317:Partial identification
149:Structured Missingness
97:
4436:Population statistics
4378:System identification
4112:Autocorrelation (ACF)
4040:Exponential smoothing
3954:Discriminant analysis
3949:Canonical correlation
3813:Partition of variance
3675:Regression validation
3519:(JonckheereâTerpstra)
3418:Likelihood-ratio test
3107:Frequentist inference
3019:Locationâscale family
2940:Sampling distribution
2905:Statistical inference
2872:Cross-sectional study
2859:Observational studies
2818:Randomized experiment
2647:Stem-and-leaf display
2449:Central limit theorem
1145:. pp. 1277â1285.
992:
932:
930:{\displaystyle R_{x}}
905:
903:{\displaystyle R_{y}}
867:
830:
785:
756:
716:
683:
647:
412:
130:Missing not at random
94:
4359:Probabilistic design
3944:Principal components
3787:Exponential families
3739:Nonlinear regression
3718:General linear model
3680:Mixed effects models
3670:Errors and residuals
3647:Confounding variable
3549:Bayesian probability
3527:Van der Waerden test
3517:Ordered alternative
3282:Multiple comparisons
3161:RaoâBlackwellization
3124:Estimating equations
3080:Statistical distance
2798:Factorial experiment
2331:Arithmetic-Geometric
1762:Tamer, Elie (2010).
1495:Preventative Science
1008:models are applied.
941:
914:
887:
839:
802:
783:{\displaystyle P(Y)}
765:
728:
692:
659:
473:
355:
4431:Official statistics
4354:Methods engineering
4035:Seasonal adjustment
3803:Poisson regressions
3723:Bayesian regression
3662:Regression analysis
3642:Partial correlation
3614:Regression analysis
3213:Prediction interval
3208:Likelihood interval
3198:Confidence interval
3190:Interval estimation
3151:Unbiased estimators
2969:Model specification
2849:Up-and-down designs
2537:Partial correlation
2493:Index of dispersion
2411:Interquartile range
1937:2016arXiv160400627M
1219:Deng (2012-10-05).
16:Statistical concept
4451:Spatial statistics
4331:Medical statistics
4231:First hitting time
4185:Whittle likelihood
3836:Degrees of freedom
3831:Multivariate ANOVA
3764:Heteroscedasticity
3576:Bayesian estimator
3541:Bayesian inference
3390:KolmogorovâSmirnov
3275:Randomization test
3245:Testing hypotheses
3218:Tolerance interval
3129:Maximum likelihood
3024:Exponential family
2957:Density estimation
2917:Statistical theory
2877:Natural experiment
2823:Scientific control
2740:Survey methodology
2426:Standard deviation
2072:Chapman & Hall
2060:Chapman & Hall
1180:10.1111/sjos.12110
1083:10.1007/bf01066742
1034:Indicator variable
987:
927:
910:), conditional on
900:
862:
825:
780:
751:
711:
678:
642:
640:
407:
300:maximum likelihood
273:/casewise deletion
98:
38:is stored for the
4553:
4552:
4491:
4490:
4487:
4486:
4426:National accounts
4396:Actuarial science
4388:Social statistics
4281:
4280:
4277:
4276:
4273:
4272:
4208:Survival function
4193:
4192:
4055:Granger causality
3896:Contingency table
3871:Survival analysis
3848:
3847:
3844:
3843:
3700:Linear regression
3595:
3594:
3591:
3590:
3566:Credible interval
3535:
3534:
3318:
3317:
3134:Method of moments
3003:Parametric family
2964:Statistical model
2894:
2893:
2890:
2889:
2808:Random assignment
2730:Statistical power
2664:
2663:
2660:
2659:
2509:Contingency table
2479:
2478:
2346:Generalized/power
1479:978-0-470-51669-0
1122:978-90-79418-01-5
1049:Matrix completion
298:full information
276:Pairwise deletion
271:Listwise deletion
226:statistical power
118:Missing at random
68:political science
4578:
4541:
4540:
4529:
4528:
4518:
4517:
4503:
4502:
4406:Crime statistics
4300:
4299:
4287:
4286:
4204:
4203:
4170:Fourier analysis
4157:Frequency domain
4137:
4084:
4050:Structural break
4010:
4009:
3959:Cluster analysis
3906:Log-linear model
3879:
3878:
3854:
3853:
3795:
3769:Homoscedasticity
3625:
3624:
3601:
3600:
3520:
3512:
3504:
3503:(KruskalâWallis)
3488:
3473:
3428:Cross validation
3413:
3395:AndersonâDarling
3342:
3329:
3328:
3300:Likelihood-ratio
3292:Parametric tests
3270:Permutation test
3253:1- & 2-tails
3144:Minimum distance
3116:Point estimation
3112:
3111:
3063:Optimal decision
3014:
2913:
2912:
2900:
2899:
2882:Quasi-experiment
2832:Adaptive designs
2683:
2682:
2670:
2669:
2547:Rank correlation
2309:
2308:
2300:
2299:
2287:
2286:
2254:
2247:
2240:
2231:
2230:
2166:
2149:
2132:
2122:
2099:
2090:
2086:(2nd ed.),
2080:Rubin, Donald B.
2074:
2062:
2050:
2038:
2026:
2014:
2002:
2001:
1969:
1968:
1930:
1906:
1900:
1899:
1891:
1885:
1884:
1882:
1871:
1862:
1861:
1825:
1819:
1818:
1810:
1804:
1803:
1795:
1789:
1788:
1768:
1759:
1753:
1752:
1724:
1718:
1717:
1711:
1698:
1692:
1691:
1689:
1665:
1659:
1658:
1648:
1646:10.1002/icd.2407
1623:
1617:
1616:
1608:
1602:
1601:
1575:
1543:
1537:
1536:
1510:
1490:
1484:
1483:
1462:
1453:
1452:
1442:
1410:
1404:
1403:
1402:
1386:
1380:
1379:
1361:
1336:
1327:
1326:
1325:
1324:
1291:
1285:
1283:
1279:(2nd ed.),
1273:Rubin, Donald B.
1268:
1262:
1261:
1259:
1258:
1243:
1237:
1236:
1234:
1232:
1227:on 15 March 2016
1223:. Archived from
1216:
1210:
1209:
1201:
1192:
1191:
1173:
1153:
1147:
1146:
1136:
1127:
1126:
1108:
1095:
1094:
1066:
996:
994:
993:
988:
980:
979:
970:
965:
964:
936:
934:
933:
928:
926:
925:
909:
907:
906:
901:
899:
898:
871:
869:
868:
863:
855:
834:
832:
831:
826:
818:
789:
787:
786:
781:
760:
758:
757:
752:
744:
720:
718:
717:
712:
704:
703:
687:
685:
684:
679:
671:
670:
651:
649:
648:
643:
641:
628:
627:
618:
595:
594:
576:
575:
560:
540:
518:
416:
414:
413:
408:
397:
396:
384:
379:
378:
262:Partial deletion
31:, occur when no
4586:
4585:
4581:
4580:
4579:
4577:
4576:
4575:
4556:
4555:
4554:
4549:
4512:
4483:
4445:
4382:
4368:quality control
4335:
4317:Clinical trials
4294:
4269:
4253:
4241:Hazard function
4235:
4189:
4151:
4135:
4098:
4094:BreuschâGodfrey
4082:
4059:
3999:
3974:Factor analysis
3920:
3901:Graphical model
3873:
3840:
3807:
3793:
3773:
3727:
3694:
3656:
3619:
3618:
3587:
3531:
3518:
3510:
3502:
3486:
3471:
3450:Rank statistics
3444:
3423:Model selection
3411:
3369:Goodness of fit
3363:
3340:
3314:
3286:
3239:
3184:
3173:Median unbiased
3101:
3012:
2945:Order statistic
2907:
2886:
2853:
2827:
2779:
2734:
2677:
2675:Data collection
2656:
2568:
2523:
2497:
2475:
2435:
2387:
2304:Continuous data
2294:
2281:
2263:
2258:
2211:
2178:
2173:
2012:SAGE Publishing
1978:
1976:Further reading
1973:
1972:
1907:
1903:
1892:
1888:
1880:
1872:
1865:
1826:
1822:
1811:
1807:
1796:
1792:
1766:
1760:
1756:
1725:
1721:
1709:
1699:
1695:
1666:
1662:
1624:
1620:
1609:
1605:
1544:
1540:
1508:10.1.1.595.7125
1491:
1487:
1480:
1463:
1456:
1411:
1407:
1387:
1383:
1337:
1330:
1322:
1320:
1318:
1292:
1288:
1269:
1265:
1256:
1254:
1245:
1244:
1240:
1230:
1228:
1217:
1213:
1202:
1195:
1154:
1150:
1137:
1130:
1123:
1109:
1098:
1067:
1063:
1058:
1053:
1044:Latent variable
1014:
975:
971:
966:
960:
956:
942:
939:
938:
921:
917:
915:
912:
911:
894:
890:
888:
885:
884:
851:
840:
837:
836:
814:
803:
800:
799:
766:
763:
762:
740:
729:
726:
725:
699:
695:
693:
690:
689:
666:
662:
660:
657:
656:
639:
638:
623:
619:
614:
590:
586:
571:
567:
556:
538:
537:
514:
498:
476:
474:
471:
470:
392:
388:
380:
374:
370:
356:
353:
352:
325:
283:
264:
248:
242:
213:
207:
172:
151:
132:
120:
103:
85:
17:
12:
11:
5:
4584:
4574:
4573:
4568:
4551:
4550:
4548:
4547:
4535:
4523:
4509:
4496:
4493:
4492:
4489:
4488:
4485:
4484:
4482:
4481:
4476:
4471:
4466:
4461:
4455:
4453:
4447:
4446:
4444:
4443:
4438:
4433:
4428:
4423:
4418:
4413:
4408:
4403:
4398:
4392:
4390:
4384:
4383:
4381:
4380:
4375:
4370:
4361:
4356:
4351:
4345:
4343:
4337:
4336:
4334:
4333:
4328:
4323:
4314:
4312:Bioinformatics
4308:
4306:
4296:
4295:
4283:
4282:
4279:
4278:
4275:
4274:
4271:
4270:
4268:
4267:
4261:
4259:
4255:
4254:
4252:
4251:
4245:
4243:
4237:
4236:
4234:
4233:
4228:
4223:
4218:
4212:
4210:
4201:
4195:
4194:
4191:
4190:
4188:
4187:
4182:
4177:
4172:
4167:
4161:
4159:
4153:
4152:
4150:
4149:
4144:
4139:
4131:
4126:
4121:
4120:
4119:
4117:partial (PACF)
4108:
4106:
4100:
4099:
4097:
4096:
4091:
4086:
4078:
4073:
4067:
4065:
4064:Specific tests
4061:
4060:
4058:
4057:
4052:
4047:
4042:
4037:
4032:
4027:
4022:
4016:
4014:
4007:
4001:
4000:
3998:
3997:
3996:
3995:
3994:
3993:
3978:
3977:
3976:
3966:
3964:Classification
3961:
3956:
3951:
3946:
3941:
3936:
3930:
3928:
3922:
3921:
3919:
3918:
3913:
3911:McNemar's test
3908:
3903:
3898:
3893:
3887:
3885:
3875:
3874:
3850:
3849:
3846:
3845:
3842:
3841:
3839:
3838:
3833:
3828:
3823:
3817:
3815:
3809:
3808:
3806:
3805:
3789:
3783:
3781:
3775:
3774:
3772:
3771:
3766:
3761:
3756:
3751:
3749:Semiparametric
3746:
3741:
3735:
3733:
3729:
3728:
3726:
3725:
3720:
3715:
3710:
3704:
3702:
3696:
3695:
3693:
3692:
3687:
3682:
3677:
3672:
3666:
3664:
3658:
3657:
3655:
3654:
3649:
3644:
3639:
3633:
3631:
3621:
3620:
3617:
3616:
3611:
3605:
3597:
3596:
3593:
3592:
3589:
3588:
3586:
3585:
3584:
3583:
3573:
3568:
3563:
3562:
3561:
3556:
3545:
3543:
3537:
3536:
3533:
3532:
3530:
3529:
3524:
3523:
3522:
3514:
3506:
3490:
3487:(MannâWhitney)
3482:
3481:
3480:
3467:
3466:
3465:
3454:
3452:
3446:
3445:
3443:
3442:
3441:
3440:
3435:
3430:
3420:
3415:
3412:(ShapiroâWilk)
3407:
3402:
3397:
3392:
3387:
3379:
3373:
3371:
3365:
3364:
3362:
3361:
3353:
3344:
3332:
3326:
3324:Specific tests
3320:
3319:
3316:
3315:
3313:
3312:
3307:
3302:
3296:
3294:
3288:
3287:
3285:
3284:
3279:
3278:
3277:
3267:
3266:
3265:
3255:
3249:
3247:
3241:
3240:
3238:
3237:
3236:
3235:
3230:
3220:
3215:
3210:
3205:
3200:
3194:
3192:
3186:
3185:
3183:
3182:
3177:
3176:
3175:
3170:
3169:
3168:
3163:
3148:
3147:
3146:
3141:
3136:
3131:
3120:
3118:
3109:
3103:
3102:
3100:
3099:
3094:
3089:
3088:
3087:
3077:
3072:
3071:
3070:
3060:
3059:
3058:
3053:
3048:
3038:
3033:
3028:
3027:
3026:
3021:
3016:
3000:
2999:
2998:
2993:
2988:
2978:
2977:
2976:
2971:
2961:
2960:
2959:
2949:
2948:
2947:
2937:
2932:
2927:
2921:
2919:
2909:
2908:
2896:
2895:
2892:
2891:
2888:
2887:
2885:
2884:
2879:
2874:
2869:
2863:
2861:
2855:
2854:
2852:
2851:
2846:
2841:
2835:
2833:
2829:
2828:
2826:
2825:
2820:
2815:
2810:
2805:
2800:
2795:
2789:
2787:
2781:
2780:
2778:
2777:
2775:Standard error
2772:
2767:
2762:
2761:
2760:
2755:
2744:
2742:
2736:
2735:
2733:
2732:
2727:
2722:
2717:
2712:
2707:
2705:Optimal design
2702:
2697:
2691:
2689:
2679:
2678:
2666:
2665:
2662:
2661:
2658:
2657:
2655:
2654:
2649:
2644:
2639:
2634:
2629:
2624:
2619:
2614:
2609:
2604:
2599:
2594:
2589:
2584:
2578:
2576:
2570:
2569:
2567:
2566:
2561:
2560:
2559:
2554:
2544:
2539:
2533:
2531:
2525:
2524:
2522:
2521:
2516:
2511:
2505:
2503:
2502:Summary tables
2499:
2498:
2496:
2495:
2489:
2487:
2481:
2480:
2477:
2476:
2474:
2473:
2472:
2471:
2466:
2461:
2451:
2445:
2443:
2437:
2436:
2434:
2433:
2428:
2423:
2418:
2413:
2408:
2403:
2397:
2395:
2389:
2388:
2386:
2385:
2380:
2375:
2374:
2373:
2368:
2363:
2358:
2353:
2348:
2343:
2338:
2336:Contraharmonic
2333:
2328:
2317:
2315:
2306:
2296:
2295:
2283:
2282:
2280:
2279:
2274:
2268:
2265:
2264:
2257:
2256:
2249:
2242:
2234:
2228:
2227:
2222:
2217:
2210:
2207:
2206:
2205:
2200:
2194:
2188:
2177:
2174:
2172:
2171:External links
2169:
2168:
2167:
2150:
2100:
2091:
2075:
2063:
2051:
2039:
2036:Guilford Press
2027:
2015:
2003:
1992:(4): 1012â28,
1977:
1974:
1971:
1970:
1901:
1886:
1863:
1836:(3): 213â234.
1820:
1805:
1790:
1779:(1): 167â195.
1754:
1729:Abbeel, Pieter
1719:
1703:Abbeel, Pieter
1693:
1680:(1): 137â157.
1660:
1618:
1603:
1558:(3): 297â308.
1538:
1501:(3): 208â213.
1485:
1478:
1454:
1405:
1381:
1328:
1316:
1286:
1263:
1238:
1211:
1193:
1164:(2): 361â377.
1148:
1128:
1121:
1096:
1077:(2): 155â173.
1060:
1059:
1057:
1054:
1052:
1051:
1046:
1041:
1036:
1031:
1026:
1021:
1015:
1013:
1010:
986:
983:
978:
974:
969:
963:
959:
955:
949:
946:
924:
920:
897:
893:
861:
858:
854:
850:
847:
844:
835:as opposed to
824:
821:
817:
813:
810:
807:
779:
776:
773:
770:
750:
747:
743:
739:
736:
733:
710:
707:
702:
698:
677:
674:
669:
665:
653:
652:
637:
634:
631:
626:
622:
617:
613:
610:
607:
604:
601:
598:
593:
589:
585:
582:
579:
574:
570:
566:
563:
559:
555:
552:
549:
546:
543:
541:
539:
536:
533:
530:
527:
524:
521:
517:
513:
510:
507:
504:
501:
499:
497:
494:
491:
488:
485:
482:
479:
478:
406:
403:
400:
395:
391:
387:
383:
377:
373:
369:
363:
360:
324:
321:
314:
313:
312:
311:
305:
304:
303:
296:
282:
279:
278:
277:
274:
263:
260:
244:Main article:
241:
238:
209:Main article:
206:
203:
171:
168:
150:
147:
131:
128:
119:
116:
102:
99:
84:
81:
29:missing values
15:
9:
6:
4:
3:
2:
4583:
4572:
4569:
4567:
4564:
4563:
4561:
4546:
4545:
4536:
4534:
4533:
4524:
4522:
4521:
4516:
4510:
4508:
4507:
4498:
4497:
4494:
4480:
4477:
4475:
4474:Geostatistics
4472:
4470:
4467:
4465:
4462:
4460:
4457:
4456:
4454:
4452:
4448:
4442:
4441:Psychometrics
4439:
4437:
4434:
4432:
4429:
4427:
4424:
4422:
4419:
4417:
4414:
4412:
4409:
4407:
4404:
4402:
4399:
4397:
4394:
4393:
4391:
4389:
4385:
4379:
4376:
4374:
4371:
4369:
4365:
4362:
4360:
4357:
4355:
4352:
4350:
4347:
4346:
4344:
4342:
4338:
4332:
4329:
4327:
4324:
4322:
4318:
4315:
4313:
4310:
4309:
4307:
4305:
4304:Biostatistics
4301:
4297:
4293:
4288:
4284:
4266:
4265:Log-rank test
4263:
4262:
4260:
4256:
4250:
4247:
4246:
4244:
4242:
4238:
4232:
4229:
4227:
4224:
4222:
4219:
4217:
4214:
4213:
4211:
4209:
4205:
4202:
4200:
4196:
4186:
4183:
4181:
4178:
4176:
4173:
4171:
4168:
4166:
4163:
4162:
4160:
4158:
4154:
4148:
4145:
4143:
4140:
4138:
4136:(BoxâJenkins)
4132:
4130:
4127:
4125:
4122:
4118:
4115:
4114:
4113:
4110:
4109:
4107:
4105:
4101:
4095:
4092:
4090:
4089:DurbinâWatson
4087:
4085:
4079:
4077:
4074:
4072:
4071:DickeyâFuller
4069:
4068:
4066:
4062:
4056:
4053:
4051:
4048:
4046:
4045:Cointegration
4043:
4041:
4038:
4036:
4033:
4031:
4028:
4026:
4023:
4021:
4020:Decomposition
4018:
4017:
4015:
4011:
4008:
4006:
4002:
3992:
3989:
3988:
3987:
3984:
3983:
3982:
3979:
3975:
3972:
3971:
3970:
3967:
3965:
3962:
3960:
3957:
3955:
3952:
3950:
3947:
3945:
3942:
3940:
3937:
3935:
3932:
3931:
3929:
3927:
3923:
3917:
3914:
3912:
3909:
3907:
3904:
3902:
3899:
3897:
3894:
3892:
3891:Cohen's kappa
3889:
3888:
3886:
3884:
3880:
3876:
3872:
3868:
3864:
3860:
3855:
3851:
3837:
3834:
3832:
3829:
3827:
3824:
3822:
3819:
3818:
3816:
3814:
3810:
3804:
3800:
3796:
3790:
3788:
3785:
3784:
3782:
3780:
3776:
3770:
3767:
3765:
3762:
3760:
3757:
3755:
3752:
3750:
3747:
3745:
3744:Nonparametric
3742:
3740:
3737:
3736:
3734:
3730:
3724:
3721:
3719:
3716:
3714:
3711:
3709:
3706:
3705:
3703:
3701:
3697:
3691:
3688:
3686:
3683:
3681:
3678:
3676:
3673:
3671:
3668:
3667:
3665:
3663:
3659:
3653:
3650:
3648:
3645:
3643:
3640:
3638:
3635:
3634:
3632:
3630:
3626:
3622:
3615:
3612:
3610:
3607:
3606:
3602:
3598:
3582:
3579:
3578:
3577:
3574:
3572:
3569:
3567:
3564:
3560:
3557:
3555:
3552:
3551:
3550:
3547:
3546:
3544:
3542:
3538:
3528:
3525:
3521:
3515:
3513:
3507:
3505:
3499:
3498:
3497:
3494:
3493:Nonparametric
3491:
3489:
3483:
3479:
3476:
3475:
3474:
3468:
3464:
3463:Sample median
3461:
3460:
3459:
3456:
3455:
3453:
3451:
3447:
3439:
3436:
3434:
3431:
3429:
3426:
3425:
3424:
3421:
3419:
3416:
3414:
3408:
3406:
3403:
3401:
3398:
3396:
3393:
3391:
3388:
3386:
3384:
3380:
3378:
3375:
3374:
3372:
3370:
3366:
3360:
3358:
3354:
3352:
3350:
3345:
3343:
3338:
3334:
3333:
3330:
3327:
3325:
3321:
3311:
3308:
3306:
3303:
3301:
3298:
3297:
3295:
3293:
3289:
3283:
3280:
3276:
3273:
3272:
3271:
3268:
3264:
3261:
3260:
3259:
3256:
3254:
3251:
3250:
3248:
3246:
3242:
3234:
3231:
3229:
3226:
3225:
3224:
3221:
3219:
3216:
3214:
3211:
3209:
3206:
3204:
3201:
3199:
3196:
3195:
3193:
3191:
3187:
3181:
3178:
3174:
3171:
3167:
3164:
3162:
3159:
3158:
3157:
3154:
3153:
3152:
3149:
3145:
3142:
3140:
3137:
3135:
3132:
3130:
3127:
3126:
3125:
3122:
3121:
3119:
3117:
3113:
3110:
3108:
3104:
3098:
3095:
3093:
3090:
3086:
3083:
3082:
3081:
3078:
3076:
3073:
3069:
3068:loss function
3066:
3065:
3064:
3061:
3057:
3054:
3052:
3049:
3047:
3044:
3043:
3042:
3039:
3037:
3034:
3032:
3029:
3025:
3022:
3020:
3017:
3015:
3009:
3006:
3005:
3004:
3001:
2997:
2994:
2992:
2989:
2987:
2984:
2983:
2982:
2979:
2975:
2972:
2970:
2967:
2966:
2965:
2962:
2958:
2955:
2954:
2953:
2950:
2946:
2943:
2942:
2941:
2938:
2936:
2933:
2931:
2928:
2926:
2923:
2922:
2920:
2918:
2914:
2910:
2906:
2901:
2897:
2883:
2880:
2878:
2875:
2873:
2870:
2868:
2865:
2864:
2862:
2860:
2856:
2850:
2847:
2845:
2842:
2840:
2837:
2836:
2834:
2830:
2824:
2821:
2819:
2816:
2814:
2811:
2809:
2806:
2804:
2801:
2799:
2796:
2794:
2791:
2790:
2788:
2786:
2782:
2776:
2773:
2771:
2770:Questionnaire
2768:
2766:
2763:
2759:
2756:
2754:
2751:
2750:
2749:
2746:
2745:
2743:
2741:
2737:
2731:
2728:
2726:
2723:
2721:
2718:
2716:
2713:
2711:
2708:
2706:
2703:
2701:
2698:
2696:
2693:
2692:
2690:
2688:
2684:
2680:
2676:
2671:
2667:
2653:
2650:
2648:
2645:
2643:
2640:
2638:
2635:
2633:
2630:
2628:
2625:
2623:
2620:
2618:
2615:
2613:
2610:
2608:
2605:
2603:
2600:
2598:
2597:Control chart
2595:
2593:
2590:
2588:
2585:
2583:
2580:
2579:
2577:
2575:
2571:
2565:
2562:
2558:
2555:
2553:
2550:
2549:
2548:
2545:
2543:
2540:
2538:
2535:
2534:
2532:
2530:
2526:
2520:
2517:
2515:
2512:
2510:
2507:
2506:
2504:
2500:
2494:
2491:
2490:
2488:
2486:
2482:
2470:
2467:
2465:
2462:
2460:
2457:
2456:
2455:
2452:
2450:
2447:
2446:
2444:
2442:
2438:
2432:
2429:
2427:
2424:
2422:
2419:
2417:
2414:
2412:
2409:
2407:
2404:
2402:
2399:
2398:
2396:
2394:
2390:
2384:
2381:
2379:
2376:
2372:
2369:
2367:
2364:
2362:
2359:
2357:
2354:
2352:
2349:
2347:
2344:
2342:
2339:
2337:
2334:
2332:
2329:
2327:
2324:
2323:
2322:
2319:
2318:
2316:
2314:
2310:
2307:
2305:
2301:
2297:
2293:
2288:
2284:
2278:
2275:
2273:
2270:
2269:
2266:
2262:
2255:
2250:
2248:
2243:
2241:
2236:
2235:
2232:
2226:
2223:
2221:
2218:
2216:
2213:
2212:
2204:
2201:
2198:
2197:R-miss-tastic
2195:
2192:
2189:
2187:
2183:
2180:
2179:
2164:
2160:
2156:
2151:
2148:
2144:
2140:
2136:
2131:
2126:
2121:
2116:
2112:
2108:
2107:
2106:PLOS Medicine
2101:
2097:
2092:
2089:
2085:
2081:
2076:
2073:
2069:
2064:
2061:
2057:
2052:
2049:
2045:
2040:
2037:
2033:
2028:
2025:
2021:
2016:
2013:
2009:
2004:
2000:
1995:
1991:
1987:
1986:
1980:
1979:
1966:
1962:
1958:
1954:
1950:
1946:
1942:
1938:
1934:
1929:
1924:
1920:
1916:
1912:
1905:
1897:
1890:
1879:
1878:
1870:
1868:
1859:
1855:
1851:
1847:
1843:
1839:
1835:
1831:
1824:
1816:
1809:
1801:
1794:
1786:
1782:
1778:
1774:
1773:
1765:
1758:
1750:
1746:
1742:
1738:
1734:
1730:
1723:
1715:
1708:
1704:
1697:
1688:
1683:
1679:
1675:
1671:
1664:
1656:
1652:
1647:
1642:
1638:
1634:
1630:
1622:
1614:
1607:
1599:
1595:
1591:
1587:
1583:
1579:
1574:
1569:
1565:
1561:
1557:
1553:
1549:
1542:
1534:
1530:
1526:
1522:
1518:
1514:
1509:
1504:
1500:
1496:
1489:
1481:
1475:
1471:
1467:
1461:
1459:
1450:
1446:
1441:
1436:
1432:
1428:
1424:
1420:
1416:
1409:
1401:
1396:
1392:
1385:
1377:
1373:
1369:
1365:
1360:
1355:
1351:
1347:
1343:
1335:
1333:
1319:
1317:9781512802863
1313:
1309:
1305:
1301:
1297:
1290:
1282:
1278:
1274:
1267:
1252:
1248:
1242:
1226:
1222:
1215:
1207:
1200:
1198:
1189:
1185:
1181:
1177:
1172:
1167:
1163:
1159:
1152:
1144:
1143:
1135:
1133:
1124:
1118:
1114:
1107:
1105:
1103:
1101:
1092:
1088:
1084:
1080:
1076:
1072:
1065:
1061:
1050:
1047:
1045:
1042:
1040:
1037:
1035:
1032:
1030:
1027:
1025:
1022:
1020:
1017:
1016:
1009:
1007:
1001:
998:
984:
981:
976:
972:
961:
957:
953:
947:
944:
922:
918:
895:
891:
882:
878:
873:
856:
848:
842:
819:
811:
805:
797:
793:
774:
768:
745:
737:
731:
722:
708:
705:
700:
696:
675:
672:
667:
663:
632:
629:
624:
620:
611:
605:
599:
596:
591:
587:
583:
580:
577:
572:
568:
564:
561:
553:
547:
544:
542:
531:
525:
519:
511:
505:
502:
500:
492:
489:
486:
480:
469:
468:
467:
465:
461:
457:
453:
449:
445:
441:
436:
433:
431:
427:
423:
418:
401:
398:
393:
389:
375:
371:
367:
361:
358:
350:
346:
342:
338:
334:
329:
320:
318:
309:
308:
306:
301:
297:
295:
291:
290:
288:
287:
286:
281:Full analysis
275:
272:
269:
268:
267:
259:
255:
253:
252:interpolation
247:
246:Interpolation
240:Interpolation
237:
234:
229:
227:
222:
218:
217:data analysis
212:
202:
200:
196:
191:
187:
185:
181:
177:
167:
165:
159:
155:
146:
142:
140:
136:
127:
124:
115:
111:
108:
93:
89:
80:
78:
77:censored data
72:
69:
65:
61:
56:
53:
47:
45:
41:
37:
34:
30:
26:
22:
4566:Missing data
4542:
4530:
4511:
4504:
4416:Econometrics
4366: /
4349:Chemometrics
4326:Epidemiology
4319: /
4292:Applications
4134:ARIMA model
4081:Q-statistic
4030:Stationarity
3926:Multivariate
3869: /
3865: /
3863:Multivariate
3861: /
3801: /
3797: /
3571:Bayes factor
3470:Signed rank
3382:
3356:
3348:
3336:
3031:Completeness
2867:Cohort study
2765:Opinion poll
2700:Missing data
2699:
2687:Study design
2642:Scatter plot
2564:Scatter plot
2557:Spearman's Ï
2519:Grouped data
2182:Missing Data
2154:
2113:(10): e267,
2110:
2104:
2095:
2083:
2067:
2055:
2044:Missing Data
2043:
2031:
2019:
2008:Missing Data
2007:
1989:
1983:
1918:
1914:
1904:
1895:
1889:
1876:
1833:
1829:
1823:
1814:
1808:
1799:
1793:
1776:
1770:
1757:
1740:
1736:
1722:
1713:
1696:
1677:
1673:
1663:
1636:
1632:
1621:
1612:
1606:
1555:
1551:
1541:
1498:
1494:
1488:
1469:
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1349:
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1229:. Retrieved
1225:the original
1214:
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1006:Markov chain
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25:missing data
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4544:WikiProject
4459:Cartography
4421:Jurimetrics
4373:Reliability
4104:Time domain
4083:(LjungâBox)
4005:Time-series
3883:Categorical
3867:Time-series
3859:Categorical
3794:(Bernoulli)
3629:Correlation
3609:Correlation
3405:JarqueâBera
3377:Chi-squared
3139:M-estimator
3092:Asymptotics
3036:Sufficiency
2803:Interaction
2715:Replication
2695:Effect size
2652:Violin plot
2632:Radar chart
2612:Forest plot
2602:Correlogram
2552:Kendall's Ï
1921:: 203â216.
1573:1887/138825
44:observation
4560:Categories
4411:Demography
4129:ARMA model
3934:Regression
3511:(Friedman)
3472:(Wilcoxon)
3410:Normality
3400:Lilliefors
3347:Student's
3223:Resampling
3097:Robustness
3085:divergence
3075:Efficiency
3013:(monotone)
3008:Likelihood
2925:Population
2758:Stratified
2710:Population
2529:Dependence
2485:Count data
2416:Percentile
2393:Dispersion
2326:Arithmetic
2261:Statistics
2176:Background
2098:, Springer
1928:1604.00627
1716:: 233â240.
1400:2307.02650
1359:2304.01429
1323:2022-08-18
1257:2015-08-01
1056:References
1029:Imputation
302:estimation
205:Imputation
176:Imputation
164:imputation
21:statistics
3792:Logistic
3559:posterior
3485:Rank sum
3233:Jackknife
3228:Bootstrap
3046:Bootstrap
2981:Parameter
2930:Statistic
2725:Statistic
2637:Run chart
2622:Pie chart
2617:Histogram
2607:Fan chart
2582:Bar chart
2464:L-moments
2351:Geometric
1749:1532-4435
1655:1522-7227
1582:0022-3891
1503:CiteSeerX
1466:Stoop, I.
1376:2522-5839
1171:1211.2958
1091:133325281
1019:Censoring
954:⊥
948:⊥
368:⊥
362:⊥
64:sociology
60:economics
52:Attrition
4506:Category
4199:Survival
4076:Johansen
3799:Binomial
3754:Isotonic
3341:(normal)
2986:location
2793:Blocking
2748:Sampling
2627:QâQ plot
2592:Box plot
2574:Graphics
2469:Skewness
2459:Kurtosis
2431:Variance
2361:Heronian
2356:Harmonic
2209:Software
2139:16138788
2082:(2002),
2048:Springer
2024:Springer
1965:Archived
1953:27318570
1858:12882831
1850:16768297
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1598:58580667
1590:30657714
1533:24566076
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1449:24262770
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1251:Archived
1188:53642701
1012:See also
184:analysis
180:omission
40:variable
4532:Commons
4479:Kriging
4364:Process
4321:studies
4180:Wavelet
4013:General
3180:Plug-in
2974:L space
2753:Cluster
2454:Moments
2272:Outline
2147:5667073
2130:1198040
1961:5874067
1933:Bibcode
1440:4631258
139:because
4401:Census
3991:Normal
3939:Manova
3759:Robust
3509:2-way
3501:1-way
3339:-test
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2587:Biplot
2378:Median
2371:Lehmer
2313:Center
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1247:"Home"
1231:13 May
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883:(i.e.
655:where
339:where
335:, and
221:impute
195:robust
66:, and
42:in an
4025:Trend
3554:prior
3496:anova
3385:-test
3359:-test
3351:-test
3258:Power
3203:Pivot
2996:shape
2991:scale
2441:Shape
2421:Range
2366:Heinz
2341:Cubic
2277:Index
2215:Mplus
2143:S2CID
2088:Wiley
1957:S2CID
1923:arXiv
1881:(PDF)
1854:S2CID
1767:(PDF)
1710:(PDF)
1594:S2CID
1529:S2CID
1395:arXiv
1354:arXiv
1281:Wiley
1184:S2CID
1166:arXiv
1087:S2CID
215:Some
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2321:Mean
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1949:PMID
1846:PMID
1745:ISSN
1651:ISSN
1586:PMID
1578:ISSN
1521:PMID
1474:ISBN
1445:PMID
1372:ISSN
1312:ISBN
1233:2016
1117:ISBN
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292:The
231:The
199:bias
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2115:doi
1994:doi
1941:doi
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