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2401:'s assumed shape, and can be shown to be biased. A simple improvement for such applications, named centered isotonic regression (CIR), was developed by Oron and Flournoy and shown to substantially reduce estimation error for both dose-response and dose-finding applications. Both CIR and the standard isotonic regression for the univariate, simply ordered case, are implemented in the R package "cir". This package also provides analytical confidence-interval estimates.
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484:. For example, one might use it to fit an isotonic curve to the means of some set of experimental results when an increase in those means according to some particular ordering is expected. A benefit of isotonic regression is that it is not constrained by any functional form, such as the linearity imposed by
2323:{\displaystyle f(x)={\begin{cases}{\hat {y}}_{1}&{\text{if }}x\leq x_{1}\\{\hat {y}}_{i}+{\frac {x-x_{i}}{x_{i+1}-x_{i}}}({\hat {y}}_{i+1}-{\hat {y}}_{i})&{\text{if }}x_{i}\leq x\leq x_{i+1}\\{\hat {y}}_{n}&{\text{if }}x\geq x_{n}\end{cases}}}
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has been applied to estimating continuous dose-response relationships in fields such as anesthesiology and toxicology. Narrowly speaking, isotonic regression only provides point estimates at observed values of
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As this article's first figure shows, in the presence of monotonicity violations the resulting interpolated curve will have flat (constant) intervals. In dose-response applications it is usually known that
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Estimation of the complete dose-response curve without any additional assumptions is usually done via linear interpolation between the point estimates.
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identification problem, and proposed a primal algorithm. These two algorithms can be seen as each other's dual, and both have a
2457:"Predicting good probabilities with supervised learning | Proceedings of the 22nd international conference on Machine learning"
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An example of isotonic regression (solid red line) compared to linear regression on the same data, both fit to minimize the
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between points. Isotonic regression is used iteratively to fit ideal distances to preserve relative dissimilarity order.
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Shively, T.S., Sager, T.W., Walker, S.G. (2009). "A Bayesian approach to non-parametric monotone function estimation".
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Oron, AP; Flournoy, N (2017). "Centered
Isotonic Regression: Point and Interval Estimation for Dose-Response Studies".
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Stylianou, MP; Flournoy, N (2002). "Dose finding using the biased coin up-and-down design and isotonic regression".
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is the technique of fitting a free-form line to a sequence of observations such that the fitted line is
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Statistical inference under order restrictions; the theory and application of isotonic regression
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for data points is sought such that order of distances between points in the embedding matches
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2571:"Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods"
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To complete the isotonic regression task, we may then choose any non-decreasing function
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1436:). Problems of this form may be solved by generic quadratic programming techniques.
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2874:; Mentz, G. (2001). "Isotonic regression: Another look at the changepoint problem".
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472:(or non-increasing) everywhere, and lies as close to the observations as possible.
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Pedregosa, Fabian; et al. (2011). "Scikit-learn:Machine learning in Python".
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2018:, as illustrated in the figure, yielding a continuous piecewise linear function:
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1290:{\displaystyle {\hat {y}}_{i}\leq {\hat {y}}_{j}{\text{ for all }}(i,j)\in E}
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Software for computing isotone (monotonic) regression has been developed for
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Barlow, R. E.; Bartholomew, D. J.; Bremner, J. M.; Brunk, H. D. (1972).
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2682:"Active set algorithms for isotonic regression; A unifying framework"
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2741:
1206:{\displaystyle \min \sum _{i=1}^{n}w_{i}({\hat {y}}_{i}-y_{i})^{2}}
2654:
2421:(1964). "Nonmetric Multidimensional Scaling: A numerical method".
5143:
4844:
1857:{\displaystyle \min _{f}\sum _{i=1}^{n}w_{i}(f(x_{i})-y_{i})^{2}}
517:
Isotonic regression for the simply ordered case with univariate
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1631:. Conversely, Best and Chakravarti studied the problem as an
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specifies the partial ordering of the observed inputs
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Robertson, T.; Wright, F. T.; Dykstra, R. L. (1988).
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1548:{\displaystyle x_{1}\leq x_{2}\leq \cdots \leq x_{n}}
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1107:{\displaystyle {\hat {y}}_{1},\ldots ,{\hat {y}}_{n}}
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Autoregressive conditional heteroskedasticity (ARCH)
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1963:
would be to interpolate linearly between the points
2569:Leeuw, Jan de; Hornik, Kurt; Mair, Patrick (2009).
667:{\displaystyle (x_{1},y_{1}),\ldots ,(x_{n},y_{n})}
585:
488:, as long as the function is monotonic increasing.
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2816:Journal of the Royal Statistical Society, Series B
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1397:(and may be regarded as the set of edges of some
999:{\displaystyle {\hat {y}}_{i}\leq {\hat {y}}_{j}}
5222:
2680:Best, Michael J.; Chakravarti, Nilotpal (1990).
1769:
1124:
4355:Multivariate adaptive regression splines (MARS)
2726:
2679:
2568:
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1758:for all i. Any such function obviously solves
1496:that the observations have been sorted so that
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2673:
2610:Xu, Zhipeng; Sun, Chenkai; Karunakaran, Aman.
2417:
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2858:: CS1 maint: multiple names: authors list (
2609:
1610:
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510:to calibrate the predicted probabilities of
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2372:. The flat intervals are incompatible with
1363:{\displaystyle E=\{(i,j):x_{i}\leq x_{j}\}}
921:{\displaystyle {\hat {y}}_{i}\approx y_{i}}
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2917:
2903:
2720:
2489:
1616:{\displaystyle E=\{(i,i+1):1\leq i<n\}}
674:be a given set of observations, where the
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2827:
2740:
2653:
2639:
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1949:
1627:for solving the quadratic program is the
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695:
2729:Statistics in Biopharmaceutical Research
2621:. R Foundation for Statistical Computing
2550:. R Foundation for Statistical Computing
480:Isotonic regression has applications in
18:
1751:{\displaystyle f(x_{i})={\hat {y}}_{i}}
5223:
4881:KaplanâMeier estimator (product limit)
2778:Order restricted statistical inference
2011:{\displaystyle (x_{i},{\hat {y}}_{i})}
4954:
4521:
4268:
3567:
3337:
2954:
2898:
2537:
2535:
1956:{\displaystyle x_{i}\in \mathbb {R} }
875:Isotonic regression seeks a weighted
702:{\displaystyle y_{i}\in \mathbb {R} }
5191:
4891:Accelerated failure time (AFT) model
2642:Journal of Machine Learning Research
740:. For generality, each observation
506:Isotonic regression is also used in
5203:
4486:Analysis of variance (ANOVA, anova)
3338:
13:
4581:CochranâMantelâHaenszel statistics
3207:Pearson product-moment correlation
2769:
2532:
14:
5252:
5236:Nonparametric Bayesian statistics
1629:pool adjacent violators algorithm
948:, subject to the constraint that
491:Another application is nonmetric
5202:
5190:
5178:
5165:
5164:
4955:
2838:10.1111/j.1467-9868.2008.00677.x
2541:
2510:10.1111/j.0006-341x.2002.00171.x
586:Problem statement and algorithms
404:
4840:Least-squares spectral analysis
2575:Journal of Statistical Software
1888:and can be used to predict the
1439:In the usual setting where the
1039:{\displaystyle x_{i}\leq x_{j}}
475:
352:Least-squares spectral analysis
290:Generalized estimating equation
110:Multinomial logistic regression
85:Vector generalized linear model
3821:Mean-unbiased minimum-variance
2924:
2633:
2603:
2562:
2449:
2411:
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2368:is not only monotone but also
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1:
5134:Geographic information system
4350:Simultaneous equations models
2751:10.1080/19466315.2017.1286256
2404:
779:{\displaystyle (x_{i},y_{i})}
171:Nonlinear mixed-effects model
4317:Coefficient of determination
3928:Uniformly most powerful test
2334:Centered isotonic regression
1485:{\displaystyle \mathbb {R} }
1429:{\displaystyle 1,2,\ldots n}
1046:. This gives the following
508:probabilistic classification
7:
4886:Proportional hazards models
4830:Spectral density estimation
4812:Vector autoregression (VAR)
4246:Maximum posterior estimator
3478:Randomized controlled trial
812:{\displaystyle w_{i}\geq 0}
512:supervised machine learning
373:Mean and predicted response
10:
5257:
4646:Multivariate distributions
3066:Average absolute deviation
2612:"Package UniIsoRegression"
1623:. In this case, a simple
495:, where a low-dimensional
166:Linear mixed-effects model
16:Type of numerical analysis
5160:
5114:
5051:
5004:
4967:
4963:
4950:
4922:
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4871:
4862:
4820:
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4728:
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4634:Structural equation model
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3970:Score/Lagrange multiplier
3955:
3908:
3853:
3779:
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3522:
3496:
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3403:
3385:Sample size determination
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3166:
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3104:
3056:
2976:
2967:
2963:
2950:
2932:
1908:values for new values of
332:Least absolute deviations
5231:Nonparametric regression
5129:Environmental statistics
4651:Elliptical distributions
4444:Generalized linear model
4373:Simple linear regression
4143:HodgesâLehmann estimator
3600:Probability distribution
3509:Stochastic approximation
3071:Coefficient of variation
2686:Mathematical Programming
1928:. A common choice when
1668:on already sorted data.
1637:computational complexity
493:multidimensional scaling
80:Generalized linear model
4789:Cross-correlation (XCF)
4397:Non-standard predictors
3831:LehmannâScheffĂ© theorem
3504:Adaptive clinical trial
2888:10.1093/biomet/88.3.793
2469:10.1145/1102351.1102430
845:{\displaystyle w_{i}=1}
5185:Mathematics portal
5006:Engineering statistics
4914:NelsonâAalen estimator
4491:Analysis of covariance
4378:Ordinary least squares
4302:Pearson product-moment
3706:Statistical functional
3617:Empirical distribution
3450:Controlled experiments
3179:Frequency distribution
2957:Descriptive statistics
2395:
2362:
2324:
2012:
1957:
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1902:
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1858:
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1399:directed acyclic graph
1391:
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1207:
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1108:
1050:(QP) in the variables
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922:
866:
846:
813:
786:may be given a weight
780:
730:
703:
668:
561:
537:
501:order of dissimilarity
411:Mathematics portal
337:Iteratively reweighted
28:
5101:Population statistics
5043:System identification
4777:Autocorrelation (ACF)
4705:Exponential smoothing
4619:Discriminant analysis
4614:Canonical correlation
4478:Partition of variance
4340:Regression validation
4184:(JonckheereâTerpstra)
4083:Likelihood-ratio test
3772:Frequentist inference
3684:Locationâscale family
3605:Sampling distribution
3570:Statistical inference
3537:Cross-sectional study
3524:Observational studies
3483:Randomized experiment
3312:Stem-and-leaf display
3114:Central limit theorem
2588:10.18637/jss.v032.i05
2396:
2363:
2325:
2013:
1958:
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1459:{\displaystyle x_{i}}
1431:
1392:
1390:{\displaystyle x_{i}}
1365:
1292:
1208:
1127:
1109:
1041:
1001:
943:
923:
867:
847:
814:
781:
738:partially ordered set
731:
729:{\displaystyle x_{i}}
704:
669:
562:
538:
482:statistical inference
368:Regression validation
347:Bayesian multivariate
64:Polynomial regression
22:
5024:Probabilistic design
4609:Principal components
4452:Exponential families
4404:Nonlinear regression
4383:General linear model
4345:Mixed effects models
4335:Errors and residuals
4312:Confounding variable
4214:Bayesian probability
4192:Van der Waerden test
4182:Ordered alternative
3947:Multiple comparisons
3826:RaoâBlackwellization
3789:Estimating equations
3745:Statistical distance
3463:Factorial experiment
2996:Arithmetic-Geometric
2394:{\displaystyle f(x)}
2376:
2361:{\displaystyle f(x)}
2343:
2025:
1967:
1932:
1912:
1892:
1868:
1765:
1704:
1693:{\displaystyle f(x)}
1675:
1661:{\displaystyle O(n)}
1643:
1559:
1500:
1474:
1443:
1405:
1401:(dag) with vertices
1374:
1304:
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952:
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883:
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823:
819:, although commonly
790:
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713:
678:
594:
548:
521:
393:GaussâMarkov theorem
388:Studentized residual
378:Errors and residuals
212:Principal components
182:Nonlinear regression
69:General linear model
5096:Official statistics
5019:Methods engineering
4700:Seasonal adjustment
4468:Poisson regressions
4388:Bayesian regression
4327:Regression analysis
4307:Partial correlation
4279:Regression analysis
3878:Prediction interval
3873:Likelihood interval
3863:Confidence interval
3855:Interval estimation
3816:Unbiased estimators
3634:Model specification
3514:Up-and-down designs
3202:Partial correlation
3158:Index of dispersion
3076:Interquartile range
2799:. New York: Wiley.
2780:. New York: Wiley.
2664:2011JMLR...12.2825P
1884:being nondecreasing
1625:iterative algorithm
1468:totally ordered set
1263: for all
536:{\displaystyle x,y}
459:isotonic regression
238:Errors-in-variables
105:Logistic regression
95:Binomial regression
40:Regression analysis
34:Part of a series on
5241:Numerical analysis
5116:Spatial statistics
4996:Medical statistics
4896:First hitting time
4850:Whittle likelihood
4501:Degrees of freedom
4496:Multivariate ANOVA
4429:Heteroscedasticity
4241:Bayesian estimator
4206:Bayesian inference
4055:KolmogorovâSmirnov
3940:Randomization test
3910:Testing hypotheses
3883:Tolerance interval
3794:Maximum likelihood
3689:Exponential family
3622:Density estimation
3582:Statistical theory
3542:Natural experiment
3488:Scientific control
3405:Survey methodology
3091:Standard deviation
2698:10.1007/bf01580873
2435:10.1007/BF02289694
2391:
2358:
2320:
2315:
2008:
1953:
1918:
1898:
1874:
1854:
1777:
1748:
1690:
1658:
1613:
1545:
1482:
1456:
1426:
1387:
1360:
1287:
1203:
1104:
1036:
996:
938:
918:
862:
842:
809:
776:
726:
699:
664:
560:{\displaystyle x.}
557:
533:
455:numerical analysis
125:Multinomial probit
29:
25:mean squared error
5218:
5217:
5156:
5155:
5152:
5151:
5091:National accounts
5061:Actuarial science
5053:Social statistics
4946:
4945:
4942:
4941:
4938:
4937:
4873:Survival function
4858:
4857:
4720:Granger causality
4561:Contingency table
4536:Survival analysis
4513:
4512:
4509:
4508:
4365:Linear regression
4260:
4259:
4256:
4255:
4231:Credible interval
4200:
4199:
3983:
3982:
3799:Method of moments
3668:Parametric family
3629:Statistical model
3559:
3558:
3555:
3554:
3473:Random assignment
3395:Statistical power
3329:
3328:
3325:
3324:
3174:Contingency table
3144:
3143:
3011:Generalized/power
2806:978-0-471-04970-8
2787:978-0-471-91787-8
2295:
2281:
2230:
2213:
2185:
2170:
2107:
2075:
2061:
1996:
1921:{\displaystyle x}
1901:{\displaystyle y}
1877:{\displaystyle f}
1768:
1739:
1466:values fall in a
1264:
1252:
1230:
1171:
1095:
1067:
1048:quadratic program
987:
965:
941:{\displaystyle i}
896:
865:{\displaystyle i}
486:linear regression
447:
446:
100:Binary regression
59:Simple regression
54:Linear regression
5248:
5206:
5205:
5194:
5193:
5183:
5182:
5168:
5167:
5071:Crime statistics
4965:
4964:
4952:
4951:
4869:
4868:
4835:Fourier analysis
4822:Frequency domain
4802:
4749:
4715:Structural break
4675:
4674:
4624:Cluster analysis
4571:Log-linear model
4544:
4543:
4519:
4518:
4460:
4434:Homoscedasticity
4290:
4289:
4266:
4265:
4185:
4177:
4169:
4168:(KruskalâWallis)
4153:
4138:
4093:Cross validation
4078:
4060:AndersonâDarling
4007:
3994:
3993:
3965:Likelihood-ratio
3957:Parametric tests
3935:Permutation test
3918:1- & 2-tails
3809:Minimum distance
3781:Point estimation
3777:
3776:
3728:Optimal decision
3679:
3578:
3577:
3565:
3564:
3547:Quasi-experiment
3497:Adaptive designs
3348:
3347:
3335:
3334:
3212:Rank correlation
2974:
2973:
2965:
2964:
2952:
2951:
2919:
2912:
2905:
2896:
2895:
2891:
2863:
2857:
2849:
2831:
2810:
2791:
2763:
2762:
2744:
2724:
2718:
2717:
2692:(1â3): 425â439.
2677:
2668:
2667:
2657:
2637:
2631:
2630:
2628:
2626:
2616:
2607:
2601:
2600:
2590:
2566:
2560:
2559:
2557:
2555:
2539:
2530:
2529:
2493:
2487:
2486:
2484:
2483:
2453:
2447:
2446:
2415:
2400:
2398:
2397:
2392:
2367:
2365:
2364:
2359:
2329:
2327:
2326:
2321:
2319:
2318:
2312:
2311:
2296:
2293:
2289:
2288:
2283:
2282:
2274:
2266:
2265:
2241:
2240:
2231:
2228:
2221:
2220:
2215:
2214:
2206:
2199:
2198:
2187:
2186:
2178:
2171:
2169:
2168:
2167:
2155:
2154:
2138:
2137:
2136:
2120:
2115:
2114:
2109:
2108:
2100:
2092:
2091:
2076:
2073:
2069:
2068:
2063:
2062:
2054:
2017:
2015:
2014:
2009:
2004:
2003:
1998:
1997:
1989:
1982:
1981:
1962:
1960:
1959:
1954:
1952:
1944:
1943:
1927:
1925:
1924:
1919:
1907:
1905:
1904:
1899:
1883:
1881:
1880:
1875:
1863:
1861:
1860:
1855:
1853:
1852:
1843:
1842:
1827:
1826:
1808:
1807:
1797:
1792:
1776:
1757:
1755:
1754:
1749:
1747:
1746:
1741:
1740:
1732:
1722:
1721:
1699:
1697:
1696:
1691:
1667:
1665:
1664:
1659:
1622:
1620:
1619:
1614:
1554:
1552:
1551:
1546:
1544:
1543:
1525:
1524:
1512:
1511:
1492:, we may assume
1491:
1489:
1488:
1483:
1481:
1465:
1463:
1462:
1457:
1455:
1454:
1435:
1433:
1432:
1427:
1396:
1394:
1393:
1388:
1386:
1385:
1369:
1367:
1366:
1361:
1356:
1355:
1343:
1342:
1296:
1294:
1293:
1288:
1265:
1262:
1260:
1259:
1254:
1253:
1245:
1238:
1237:
1232:
1231:
1223:
1212:
1210:
1209:
1204:
1202:
1201:
1192:
1191:
1179:
1178:
1173:
1172:
1164:
1157:
1156:
1146:
1141:
1113:
1111:
1110:
1105:
1103:
1102:
1097:
1096:
1088:
1075:
1074:
1069:
1068:
1060:
1045:
1043:
1042:
1037:
1035:
1034:
1022:
1021:
1005:
1003:
1002:
997:
995:
994:
989:
988:
980:
973:
972:
967:
966:
958:
947:
945:
944:
939:
927:
925:
924:
919:
917:
916:
904:
903:
898:
897:
889:
871:
869:
868:
863:
851:
849:
848:
843:
835:
834:
818:
816:
815:
810:
802:
801:
785:
783:
782:
777:
772:
771:
759:
758:
735:
733:
732:
727:
725:
724:
708:
706:
705:
700:
698:
690:
689:
673:
671:
670:
665:
660:
659:
647:
646:
622:
621:
609:
608:
566:
564:
563:
558:
542:
540:
539:
534:
439:
432:
425:
409:
408:
316:Ridge regression
151:Multilevel model
31:
30:
5256:
5255:
5251:
5250:
5249:
5247:
5246:
5245:
5221:
5220:
5219:
5214:
5177:
5148:
5110:
5047:
5033:quality control
5000:
4982:Clinical trials
4959:
4934:
4918:
4906:Hazard function
4900:
4854:
4816:
4800:
4763:
4759:BreuschâGodfrey
4747:
4724:
4664:
4639:Factor analysis
4585:
4566:Graphical model
4538:
4505:
4472:
4458:
4438:
4392:
4359:
4321:
4284:
4283:
4252:
4196:
4183:
4175:
4167:
4151:
4136:
4115:Rank statistics
4109:
4088:Model selection
4076:
4034:Goodness of fit
4028:
4005:
3979:
3951:
3904:
3849:
3838:Median unbiased
3766:
3677:
3610:Order statistic
3572:
3551:
3518:
3492:
3444:
3399:
3342:
3340:Data collection
3321:
3233:
3188:
3162:
3140:
3100:
3052:
2969:Continuous data
2959:
2946:
2928:
2923:
2851:
2850:
2829:10.1.1.338.3846
2807:
2788:
2772:
2770:Further reading
2767:
2766:
2725:
2721:
2678:
2671:
2638:
2634:
2624:
2622:
2614:
2608:
2604:
2567:
2563:
2553:
2551:
2544:"Package 'cir'"
2540:
2533:
2494:
2490:
2481:
2479:
2455:
2454:
2450:
2416:
2412:
2407:
2377:
2374:
2373:
2344:
2341:
2340:
2336:
2314:
2313:
2307:
2303:
2292:
2290:
2284:
2273:
2272:
2271:
2268:
2267:
2255:
2251:
2236:
2232:
2227:
2225:
2216:
2205:
2204:
2203:
2188:
2177:
2176:
2175:
2163:
2159:
2144:
2140:
2139:
2132:
2128:
2121:
2119:
2110:
2099:
2098:
2097:
2094:
2093:
2087:
2083:
2072:
2070:
2064:
2053:
2052:
2051:
2044:
2043:
2026:
2023:
2022:
1999:
1988:
1987:
1986:
1977:
1973:
1968:
1965:
1964:
1948:
1939:
1935:
1933:
1930:
1929:
1913:
1910:
1909:
1893:
1890:
1889:
1869:
1866:
1865:
1848:
1844:
1838:
1834:
1822:
1818:
1803:
1799:
1793:
1782:
1772:
1766:
1763:
1762:
1742:
1731:
1730:
1729:
1717:
1713:
1705:
1702:
1701:
1676:
1673:
1672:
1644:
1641:
1640:
1560:
1557:
1556:
1539:
1535:
1520:
1516:
1507:
1503:
1501:
1498:
1497:
1477:
1475:
1472:
1471:
1450:
1446:
1444:
1441:
1440:
1406:
1403:
1402:
1381:
1377:
1375:
1372:
1371:
1351:
1347:
1338:
1334:
1305:
1302:
1301:
1261:
1255:
1244:
1243:
1242:
1233:
1222:
1221:
1220:
1218:
1215:
1214:
1197:
1193:
1187:
1183:
1174:
1163:
1162:
1161:
1152:
1148:
1142:
1131:
1122:
1119:
1118:
1098:
1087:
1086:
1085:
1070:
1059:
1058:
1057:
1055:
1052:
1051:
1030:
1026:
1017:
1013:
1011:
1008:
1007:
990:
979:
978:
977:
968:
957:
956:
955:
953:
950:
949:
933:
930:
929:
912:
908:
899:
888:
887:
886:
884:
881:
880:
857:
854:
853:
830:
826:
824:
821:
820:
797:
793:
791:
788:
787:
767:
763:
754:
750:
745:
742:
741:
720:
716:
714:
711:
710:
694:
685:
681:
679:
676:
675:
655:
651:
642:
638:
617:
613:
604:
600:
595:
592:
591:
588:
549:
546:
545:
522:
519:
518:
478:
443:
403:
383:Goodness of fit
90:Discrete choice
17:
12:
11:
5:
5254:
5244:
5243:
5238:
5233:
5216:
5215:
5213:
5212:
5200:
5188:
5174:
5161:
5158:
5157:
5154:
5153:
5150:
5149:
5147:
5146:
5141:
5136:
5131:
5126:
5120:
5118:
5112:
5111:
5109:
5108:
5103:
5098:
5093:
5088:
5083:
5078:
5073:
5068:
5063:
5057:
5055:
5049:
5048:
5046:
5045:
5040:
5035:
5026:
5021:
5016:
5010:
5008:
5002:
5001:
4999:
4998:
4993:
4988:
4979:
4977:Bioinformatics
4973:
4971:
4961:
4960:
4948:
4947:
4944:
4943:
4940:
4939:
4936:
4935:
4933:
4932:
4926:
4924:
4920:
4919:
4917:
4916:
4910:
4908:
4902:
4901:
4899:
4898:
4893:
4888:
4883:
4877:
4875:
4866:
4860:
4859:
4856:
4855:
4853:
4852:
4847:
4842:
4837:
4832:
4826:
4824:
4818:
4817:
4815:
4814:
4809:
4804:
4796:
4791:
4786:
4785:
4784:
4782:partial (PACF)
4773:
4771:
4765:
4764:
4762:
4761:
4756:
4751:
4743:
4738:
4732:
4730:
4729:Specific tests
4726:
4725:
4723:
4722:
4717:
4712:
4707:
4702:
4697:
4692:
4687:
4681:
4679:
4672:
4666:
4665:
4663:
4662:
4661:
4660:
4659:
4658:
4643:
4642:
4641:
4631:
4629:Classification
4626:
4621:
4616:
4611:
4606:
4601:
4595:
4593:
4587:
4586:
4584:
4583:
4578:
4576:McNemar's test
4573:
4568:
4563:
4558:
4552:
4550:
4540:
4539:
4515:
4514:
4511:
4510:
4507:
4506:
4504:
4503:
4498:
4493:
4488:
4482:
4480:
4474:
4473:
4471:
4470:
4454:
4448:
4446:
4440:
4439:
4437:
4436:
4431:
4426:
4421:
4416:
4414:Semiparametric
4411:
4406:
4400:
4398:
4394:
4393:
4391:
4390:
4385:
4380:
4375:
4369:
4367:
4361:
4360:
4358:
4357:
4352:
4347:
4342:
4337:
4331:
4329:
4323:
4322:
4320:
4319:
4314:
4309:
4304:
4298:
4296:
4286:
4285:
4282:
4281:
4276:
4270:
4262:
4261:
4258:
4257:
4254:
4253:
4251:
4250:
4249:
4248:
4238:
4233:
4228:
4227:
4226:
4221:
4210:
4208:
4202:
4201:
4198:
4197:
4195:
4194:
4189:
4188:
4187:
4179:
4171:
4155:
4152:(MannâWhitney)
4147:
4146:
4145:
4132:
4131:
4130:
4119:
4117:
4111:
4110:
4108:
4107:
4106:
4105:
4100:
4095:
4085:
4080:
4077:(ShapiroâWilk)
4072:
4067:
4062:
4057:
4052:
4044:
4038:
4036:
4030:
4029:
4027:
4026:
4018:
4009:
3997:
3991:
3989:Specific tests
3985:
3984:
3981:
3980:
3978:
3977:
3972:
3967:
3961:
3959:
3953:
3952:
3950:
3949:
3944:
3943:
3942:
3932:
3931:
3930:
3920:
3914:
3912:
3906:
3905:
3903:
3902:
3901:
3900:
3895:
3885:
3880:
3875:
3870:
3865:
3859:
3857:
3851:
3850:
3848:
3847:
3842:
3841:
3840:
3835:
3834:
3833:
3828:
3813:
3812:
3811:
3806:
3801:
3796:
3785:
3783:
3774:
3768:
3767:
3765:
3764:
3759:
3754:
3753:
3752:
3742:
3737:
3736:
3735:
3725:
3724:
3723:
3718:
3713:
3703:
3698:
3693:
3692:
3691:
3686:
3681:
3665:
3664:
3663:
3658:
3653:
3643:
3642:
3641:
3636:
3626:
3625:
3624:
3614:
3613:
3612:
3602:
3597:
3592:
3586:
3584:
3574:
3573:
3561:
3560:
3557:
3556:
3553:
3552:
3550:
3549:
3544:
3539:
3534:
3528:
3526:
3520:
3519:
3517:
3516:
3511:
3506:
3500:
3498:
3494:
3493:
3491:
3490:
3485:
3480:
3475:
3470:
3465:
3460:
3454:
3452:
3446:
3445:
3443:
3442:
3440:Standard error
3437:
3432:
3427:
3426:
3425:
3420:
3409:
3407:
3401:
3400:
3398:
3397:
3392:
3387:
3382:
3377:
3372:
3370:Optimal design
3367:
3362:
3356:
3354:
3344:
3343:
3331:
3330:
3327:
3326:
3323:
3322:
3320:
3319:
3314:
3309:
3304:
3299:
3294:
3289:
3284:
3279:
3274:
3269:
3264:
3259:
3254:
3249:
3243:
3241:
3235:
3234:
3232:
3231:
3226:
3225:
3224:
3219:
3209:
3204:
3198:
3196:
3190:
3189:
3187:
3186:
3181:
3176:
3170:
3168:
3167:Summary tables
3164:
3163:
3161:
3160:
3154:
3152:
3146:
3145:
3142:
3141:
3139:
3138:
3137:
3136:
3131:
3126:
3116:
3110:
3108:
3102:
3101:
3099:
3098:
3093:
3088:
3083:
3078:
3073:
3068:
3062:
3060:
3054:
3053:
3051:
3050:
3045:
3040:
3039:
3038:
3033:
3028:
3023:
3018:
3013:
3008:
3003:
3001:Contraharmonic
2998:
2993:
2982:
2980:
2971:
2961:
2960:
2948:
2947:
2945:
2944:
2939:
2933:
2930:
2929:
2922:
2921:
2914:
2907:
2899:
2893:
2892:
2882:(3): 793â804.
2864:
2822:(1): 159â175.
2811:
2805:
2792:
2786:
2771:
2768:
2765:
2764:
2735:(3): 258â267.
2719:
2669:
2632:
2602:
2561:
2531:
2504:(1): 171â177.
2488:
2448:
2429:(2): 115â129.
2419:Kruskal, J. B.
2409:
2408:
2406:
2403:
2390:
2387:
2384:
2381:
2357:
2354:
2351:
2348:
2335:
2332:
2331:
2330:
2317:
2310:
2306:
2302:
2299:
2291:
2287:
2280:
2277:
2270:
2269:
2264:
2261:
2258:
2254:
2250:
2247:
2244:
2239:
2235:
2226:
2224:
2219:
2212:
2209:
2202:
2197:
2194:
2191:
2184:
2181:
2174:
2166:
2162:
2158:
2153:
2150:
2147:
2143:
2135:
2131:
2127:
2124:
2118:
2113:
2106:
2103:
2096:
2095:
2090:
2086:
2082:
2079:
2071:
2067:
2060:
2057:
2050:
2049:
2047:
2042:
2039:
2036:
2033:
2030:
2007:
2002:
1995:
1992:
1985:
1980:
1976:
1972:
1951:
1947:
1942:
1938:
1917:
1897:
1886:
1885:
1873:
1851:
1847:
1841:
1837:
1833:
1830:
1825:
1821:
1817:
1814:
1811:
1806:
1802:
1796:
1791:
1788:
1785:
1781:
1775:
1771:
1745:
1738:
1735:
1728:
1725:
1720:
1716:
1712:
1709:
1689:
1686:
1683:
1680:
1657:
1654:
1651:
1648:
1612:
1609:
1606:
1603:
1600:
1597:
1594:
1591:
1588:
1585:
1582:
1579:
1576:
1573:
1570:
1567:
1564:
1542:
1538:
1534:
1531:
1528:
1523:
1519:
1515:
1510:
1506:
1480:
1453:
1449:
1425:
1422:
1419:
1416:
1413:
1410:
1384:
1380:
1359:
1354:
1350:
1346:
1341:
1337:
1333:
1330:
1327:
1324:
1321:
1318:
1315:
1312:
1309:
1298:
1297:
1286:
1283:
1280:
1277:
1274:
1271:
1268:
1258:
1251:
1248:
1241:
1236:
1229:
1226:
1200:
1196:
1190:
1186:
1182:
1177:
1170:
1167:
1160:
1155:
1151:
1145:
1140:
1137:
1134:
1130:
1126:
1101:
1094:
1091:
1084:
1081:
1078:
1073:
1066:
1063:
1033:
1029:
1025:
1020:
1016:
993:
986:
983:
976:
971:
964:
961:
937:
915:
911:
907:
902:
895:
892:
861:
841:
838:
833:
829:
808:
805:
800:
796:
775:
770:
766:
762:
757:
753:
749:
723:
719:
697:
693:
688:
684:
663:
658:
654:
650:
645:
641:
637:
634:
631:
628:
625:
620:
616:
612:
607:
603:
599:
587:
584:
556:
553:
532:
529:
526:
477:
474:
470:non-decreasing
445:
444:
442:
441:
434:
427:
419:
416:
415:
414:
413:
398:
397:
396:
395:
390:
385:
380:
375:
370:
362:
361:
357:
356:
355:
354:
349:
344:
339:
334:
326:
325:
324:
323:
318:
313:
308:
303:
295:
294:
293:
292:
287:
282:
277:
269:
268:
267:
266:
261:
256:
248:
247:
243:
242:
241:
240:
232:
231:
230:
229:
224:
219:
214:
209:
204:
199:
194:
192:Semiparametric
189:
184:
176:
175:
174:
173:
168:
163:
161:Random effects
158:
153:
145:
144:
143:
142:
137:
135:Ordered probit
132:
127:
122:
117:
112:
107:
102:
97:
92:
87:
82:
74:
73:
72:
71:
66:
61:
56:
48:
47:
43:
42:
36:
35:
15:
9:
6:
4:
3:
2:
5253:
5242:
5239:
5237:
5234:
5232:
5229:
5228:
5226:
5211:
5210:
5201:
5199:
5198:
5189:
5187:
5186:
5181:
5175:
5173:
5172:
5163:
5162:
5159:
5145:
5142:
5140:
5139:Geostatistics
5137:
5135:
5132:
5130:
5127:
5125:
5122:
5121:
5119:
5117:
5113:
5107:
5106:Psychometrics
5104:
5102:
5099:
5097:
5094:
5092:
5089:
5087:
5084:
5082:
5079:
5077:
5074:
5072:
5069:
5067:
5064:
5062:
5059:
5058:
5056:
5054:
5050:
5044:
5041:
5039:
5036:
5034:
5030:
5027:
5025:
5022:
5020:
5017:
5015:
5012:
5011:
5009:
5007:
5003:
4997:
4994:
4992:
4989:
4987:
4983:
4980:
4978:
4975:
4974:
4972:
4970:
4969:Biostatistics
4966:
4962:
4958:
4953:
4949:
4931:
4930:Log-rank test
4928:
4927:
4925:
4921:
4915:
4912:
4911:
4909:
4907:
4903:
4897:
4894:
4892:
4889:
4887:
4884:
4882:
4879:
4878:
4876:
4874:
4870:
4867:
4865:
4861:
4851:
4848:
4846:
4843:
4841:
4838:
4836:
4833:
4831:
4828:
4827:
4825:
4823:
4819:
4813:
4810:
4808:
4805:
4803:
4801:(BoxâJenkins)
4797:
4795:
4792:
4790:
4787:
4783:
4780:
4779:
4778:
4775:
4774:
4772:
4770:
4766:
4760:
4757:
4755:
4754:DurbinâWatson
4752:
4750:
4744:
4742:
4739:
4737:
4736:DickeyâFuller
4734:
4733:
4731:
4727:
4721:
4718:
4716:
4713:
4711:
4710:Cointegration
4708:
4706:
4703:
4701:
4698:
4696:
4693:
4691:
4688:
4686:
4685:Decomposition
4683:
4682:
4680:
4676:
4673:
4671:
4667:
4657:
4654:
4653:
4652:
4649:
4648:
4647:
4644:
4640:
4637:
4636:
4635:
4632:
4630:
4627:
4625:
4622:
4620:
4617:
4615:
4612:
4610:
4607:
4605:
4602:
4600:
4597:
4596:
4594:
4592:
4588:
4582:
4579:
4577:
4574:
4572:
4569:
4567:
4564:
4562:
4559:
4557:
4556:Cohen's kappa
4554:
4553:
4551:
4549:
4545:
4541:
4537:
4533:
4529:
4525:
4520:
4516:
4502:
4499:
4497:
4494:
4492:
4489:
4487:
4484:
4483:
4481:
4479:
4475:
4469:
4465:
4461:
4455:
4453:
4450:
4449:
4447:
4445:
4441:
4435:
4432:
4430:
4427:
4425:
4422:
4420:
4417:
4415:
4412:
4410:
4409:Nonparametric
4407:
4405:
4402:
4401:
4399:
4395:
4389:
4386:
4384:
4381:
4379:
4376:
4374:
4371:
4370:
4368:
4366:
4362:
4356:
4353:
4351:
4348:
4346:
4343:
4341:
4338:
4336:
4333:
4332:
4330:
4328:
4324:
4318:
4315:
4313:
4310:
4308:
4305:
4303:
4300:
4299:
4297:
4295:
4291:
4287:
4280:
4277:
4275:
4272:
4271:
4267:
4263:
4247:
4244:
4243:
4242:
4239:
4237:
4234:
4232:
4229:
4225:
4222:
4220:
4217:
4216:
4215:
4212:
4211:
4209:
4207:
4203:
4193:
4190:
4186:
4180:
4178:
4172:
4170:
4164:
4163:
4162:
4159:
4158:Nonparametric
4156:
4154:
4148:
4144:
4141:
4140:
4139:
4133:
4129:
4128:Sample median
4126:
4125:
4124:
4121:
4120:
4118:
4116:
4112:
4104:
4101:
4099:
4096:
4094:
4091:
4090:
4089:
4086:
4084:
4081:
4079:
4073:
4071:
4068:
4066:
4063:
4061:
4058:
4056:
4053:
4051:
4049:
4045:
4043:
4040:
4039:
4037:
4035:
4031:
4025:
4023:
4019:
4017:
4015:
4010:
4008:
4003:
3999:
3998:
3995:
3992:
3990:
3986:
3976:
3973:
3971:
3968:
3966:
3963:
3962:
3960:
3958:
3954:
3948:
3945:
3941:
3938:
3937:
3936:
3933:
3929:
3926:
3925:
3924:
3921:
3919:
3916:
3915:
3913:
3911:
3907:
3899:
3896:
3894:
3891:
3890:
3889:
3886:
3884:
3881:
3879:
3876:
3874:
3871:
3869:
3866:
3864:
3861:
3860:
3858:
3856:
3852:
3846:
3843:
3839:
3836:
3832:
3829:
3827:
3824:
3823:
3822:
3819:
3818:
3817:
3814:
3810:
3807:
3805:
3802:
3800:
3797:
3795:
3792:
3791:
3790:
3787:
3786:
3784:
3782:
3778:
3775:
3773:
3769:
3763:
3760:
3758:
3755:
3751:
3748:
3747:
3746:
3743:
3741:
3738:
3734:
3733:loss function
3731:
3730:
3729:
3726:
3722:
3719:
3717:
3714:
3712:
3709:
3708:
3707:
3704:
3702:
3699:
3697:
3694:
3690:
3687:
3685:
3682:
3680:
3674:
3671:
3670:
3669:
3666:
3662:
3659:
3657:
3654:
3652:
3649:
3648:
3647:
3644:
3640:
3637:
3635:
3632:
3631:
3630:
3627:
3623:
3620:
3619:
3618:
3615:
3611:
3608:
3607:
3606:
3603:
3601:
3598:
3596:
3593:
3591:
3588:
3587:
3585:
3583:
3579:
3575:
3571:
3566:
3562:
3548:
3545:
3543:
3540:
3538:
3535:
3533:
3530:
3529:
3527:
3525:
3521:
3515:
3512:
3510:
3507:
3505:
3502:
3501:
3499:
3495:
3489:
3486:
3484:
3481:
3479:
3476:
3474:
3471:
3469:
3466:
3464:
3461:
3459:
3456:
3455:
3453:
3451:
3447:
3441:
3438:
3436:
3435:Questionnaire
3433:
3431:
3428:
3424:
3421:
3419:
3416:
3415:
3414:
3411:
3410:
3408:
3406:
3402:
3396:
3393:
3391:
3388:
3386:
3383:
3381:
3378:
3376:
3373:
3371:
3368:
3366:
3363:
3361:
3358:
3357:
3355:
3353:
3349:
3345:
3341:
3336:
3332:
3318:
3315:
3313:
3310:
3308:
3305:
3303:
3300:
3298:
3295:
3293:
3290:
3288:
3285:
3283:
3280:
3278:
3275:
3273:
3270:
3268:
3265:
3263:
3262:Control chart
3260:
3258:
3255:
3253:
3250:
3248:
3245:
3244:
3242:
3240:
3236:
3230:
3227:
3223:
3220:
3218:
3215:
3214:
3213:
3210:
3208:
3205:
3203:
3200:
3199:
3197:
3195:
3191:
3185:
3182:
3180:
3177:
3175:
3172:
3171:
3169:
3165:
3159:
3156:
3155:
3153:
3151:
3147:
3135:
3132:
3130:
3127:
3125:
3122:
3121:
3120:
3117:
3115:
3112:
3111:
3109:
3107:
3103:
3097:
3094:
3092:
3089:
3087:
3084:
3082:
3079:
3077:
3074:
3072:
3069:
3067:
3064:
3063:
3061:
3059:
3055:
3049:
3046:
3044:
3041:
3037:
3034:
3032:
3029:
3027:
3024:
3022:
3019:
3017:
3014:
3012:
3009:
3007:
3004:
3002:
2999:
2997:
2994:
2992:
2989:
2988:
2987:
2984:
2983:
2981:
2979:
2975:
2972:
2970:
2966:
2962:
2958:
2953:
2949:
2943:
2940:
2938:
2935:
2934:
2931:
2927:
2920:
2915:
2913:
2908:
2906:
2901:
2900:
2897:
2889:
2885:
2881:
2877:
2873:
2872:Woodroofe, M.
2869:
2865:
2861:
2855:
2847:
2843:
2839:
2835:
2830:
2825:
2821:
2817:
2812:
2808:
2802:
2798:
2793:
2789:
2783:
2779:
2774:
2773:
2760:
2756:
2752:
2748:
2743:
2738:
2734:
2730:
2723:
2715:
2711:
2707:
2703:
2699:
2695:
2691:
2687:
2683:
2676:
2674:
2665:
2661:
2656:
2651:
2648:: 2825â2830.
2647:
2643:
2636:
2620:
2613:
2606:
2598:
2594:
2589:
2584:
2580:
2576:
2572:
2565:
2549:
2545:
2542:Oron, Assaf.
2538:
2536:
2527:
2523:
2519:
2515:
2511:
2507:
2503:
2499:
2492:
2478:
2474:
2470:
2466:
2462:
2458:
2452:
2444:
2440:
2436:
2432:
2428:
2424:
2423:Psychometrika
2420:
2414:
2410:
2402:
2385:
2379:
2371:
2352:
2346:
2308:
2304:
2300:
2297:
2285:
2275:
2262:
2259:
2256:
2252:
2248:
2245:
2242:
2237:
2233:
2217:
2207:
2200:
2195:
2192:
2189:
2179:
2164:
2160:
2156:
2151:
2148:
2145:
2141:
2133:
2129:
2125:
2122:
2116:
2111:
2101:
2088:
2084:
2080:
2077:
2065:
2055:
2045:
2040:
2034:
2028:
2021:
2020:
2019:
2000:
1990:
1983:
1978:
1974:
1945:
1940:
1936:
1915:
1895:
1871:
1849:
1839:
1835:
1831:
1823:
1819:
1812:
1804:
1800:
1794:
1789:
1786:
1783:
1779:
1773:
1761:
1760:
1759:
1743:
1733:
1726:
1718:
1714:
1707:
1684:
1678:
1669:
1652:
1646:
1638:
1634:
1630:
1626:
1607:
1604:
1601:
1598:
1595:
1592:
1586:
1583:
1580:
1577:
1574:
1565:
1562:
1540:
1536:
1532:
1529:
1526:
1521:
1517:
1513:
1508:
1504:
1495:
1469:
1451:
1447:
1437:
1423:
1420:
1417:
1414:
1411:
1408:
1400:
1382:
1378:
1352:
1348:
1344:
1339:
1335:
1331:
1325:
1322:
1319:
1310:
1307:
1284:
1281:
1275:
1272:
1269:
1256:
1246:
1239:
1234:
1224:
1198:
1188:
1184:
1180:
1175:
1165:
1153:
1149:
1143:
1138:
1135:
1132:
1128:
1117:
1116:
1115:
1099:
1089:
1082:
1079:
1076:
1071:
1061:
1049:
1031:
1027:
1023:
1018:
1014:
991:
981:
974:
969:
959:
935:
913:
909:
905:
900:
890:
878:
877:least-squares
873:
859:
839:
836:
831:
827:
806:
803:
798:
794:
768:
764:
760:
755:
751:
739:
736:fall in some
721:
717:
691:
686:
682:
656:
652:
648:
643:
639:
632:
629:
626:
618:
614:
610:
605:
601:
583:
581:
577:
573:
568:
554:
551:
530:
527:
524:
515:
513:
509:
504:
502:
498:
494:
489:
487:
483:
473:
471:
467:
465:
460:
456:
452:
440:
435:
433:
428:
426:
421:
420:
418:
417:
412:
407:
402:
401:
400:
399:
394:
391:
389:
386:
384:
381:
379:
376:
374:
371:
369:
366:
365:
364:
363:
359:
358:
353:
350:
348:
345:
343:
340:
338:
335:
333:
330:
329:
328:
327:
322:
319:
317:
314:
312:
309:
307:
304:
302:
299:
298:
297:
296:
291:
288:
286:
283:
281:
278:
276:
273:
272:
271:
270:
265:
262:
260:
257:
255:
254:Least squares
252:
251:
250:
249:
245:
244:
239:
236:
235:
234:
233:
228:
225:
223:
220:
218:
215:
213:
210:
208:
205:
203:
200:
198:
195:
193:
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187:Nonparametric
185:
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159:
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156:Fixed effects
154:
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136:
133:
131:
130:Ordered logit
128:
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5081:Econometrics
5031: /
5014:Chemometrics
4991:Epidemiology
4984: /
4957:Applications
4799:ARIMA model
4746:Q-statistic
4695:Stationarity
4591:Multivariate
4534: /
4530: /
4528:Multivariate
4526: /
4466: /
4462: /
4418:
4236:Bayes factor
4135:Signed rank
4047:
4021:
4013:
4001:
3696:Completeness
3532:Cohort study
3430:Opinion poll
3365:Missing data
3352:Study design
3307:Scatter plot
3229:Scatter plot
3222:Spearman's Ï
3184:Grouped data
2879:
2875:
2854:cite journal
2819:
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311:Non-negative
206:
5209:WikiProject
5124:Cartography
5086:Jurimetrics
5038:Reliability
4769:Time domain
4748:(LjungâBox)
4670:Time-series
4548:Categorical
4532:Time-series
4524:Categorical
4459:(Bernoulli)
4294:Correlation
4274:Correlation
4070:JarqueâBera
4042:Chi-squared
3804:M-estimator
3757:Asymptotics
3701:Sufficiency
3468:Interaction
3380:Replication
3360:Effect size
3317:Violin plot
3297:Radar chart
3277:Forest plot
3267:Correlogram
3217:Kendall's Ï
2581:(5): 1â24.
2554:26 December
1864:subject to
1555:, and take
1213:subject to
321:Regularized
285:Generalized
217:Least angle
115:Mixed logit
5225:Categories
5076:Demography
4794:ARMA model
4599:Regression
4176:(Friedman)
4137:(Wilcoxon)
4075:Normality
4065:Lilliefors
4012:Student's
3888:Resampling
3762:Robustness
3750:divergence
3740:Efficiency
3678:(monotone)
3673:Likelihood
3590:Population
3423:Stratified
3375:Population
3194:Dependence
3150:Count data
3081:Percentile
3058:Dispersion
2991:Arithmetic
2926:Statistics
2876:Biometrika
2742:1701.05964
2625:29 October
2498:Biometrics
2482:2020-07-07
2461:dl.acm.org
2405:References
1700:such that
1633:active set
466:regression
451:statistics
360:Background
264:Non-linear
246:Estimation
4457:Logistic
4224:posterior
4150:Rank sum
3898:Jackknife
3893:Bootstrap
3711:Bootstrap
3646:Parameter
3595:Statistic
3390:Statistic
3302:Run chart
3287:Pie chart
3282:Histogram
3272:Fan chart
3247:Bar chart
3129:L-moments
3016:Geometric
2868:Wu, W. B.
2846:119761196
2824:CiteSeerX
2706:0025-5610
2655:1201.0490
2597:1548-7660
2477:207158152
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1024:≤
1006:whenever
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963:^
906:≈
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497:embedding
464:monotonic
227:Segmented
5171:Category
4864:Survival
4741:Johansen
4464:Binomial
4419:Isotonic
4006:(normal)
3651:location
3458:Blocking
3413:Sampling
3292:QâQ plot
3257:Box plot
3239:Graphics
3134:Skewness
3124:Kurtosis
3096:Variance
3026:Heronian
3021:Harmonic
2759:88521189
2714:31879613
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2443:11709679
2294:if
2229:if
2074:if
1470:such as
928:for all
852:for all
709:and the
514:models.
342:Bayesian
280:Weighted
275:Ordinary
207:Isotonic
202:Quantile
5197:Commons
5144:Kriging
5029:Process
4986:studies
4845:Wavelet
4678:General
3845:Plug-in
3639:L space
3418:Cluster
3119:Moments
2937:Outline
2660:Bibcode
2526:8743090
301:Partial
140:Poisson
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4604:Manova
4424:Robust
4174:2-way
4166:1-way
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4690:Trend
4219:prior
4161:anova
4050:-test
4024:-test
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3923:Power
3868:Pivot
3661:shape
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3106:Shape
3086:Range
3031:Heinz
3006:Cubic
2942:Index
2842:S2CID
2755:S2CID
2737:arXiv
2710:S2CID
2650:arXiv
2615:(PDF)
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2473:S2CID
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576:Stata
306:Total
222:Local
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