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Research Article

Composite empirical likelihood for multisample clustered data

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Pages 60-81 | Received 25 Apr 2019, Accepted 01 Apr 2021, Published online: 20 Apr 2021

References

  • Anderson, J. (1979), ‘Multivariate Logistic Compounds’, Biometrika, 66(1), 17–26.
  • ASTM D (2002), Standard practice for establishing allowable properties for visually-graded dimension lumber from in-grade tests of full-size specimens. American Society for Testing and Materials, West Conshohocken, PA, http://www.astm.org.
  • Cao, R., and Keilegom, I.V. (2006), ‘Empirical Likelihood Tests for Two-Sample Problems Via Nonparametric Density Estimation’, The Canadian Journal of Statistics, 34, 61–77.
  • Chandler, R.E., and Bate, S. (2007), ‘Inference for Clustered Data Using the Independence Loglikelihood’, Biometrika, 94(1), 167–183.
  • Chen, J., and Liu, Y. (2013), ‘Quantile and Quantile-Function Estimations Under Density Ratio Model’, The Annals of Statistics, 41(3), 1669–1692.
  • Datta, S., and Satten, G.A. (2005), ‘Rank-sum Tests for Clustered Data’, Journal of the American Statistical Association, 100(471), 908–915.
  • Diggle, P., Heagerty, P., Liang, K.-Y., and Zeger, S. (2002), Analysis of Longitudinal Data (2nd ed.), Oxford: Oxford University Press.
  • Divine, G.W., Norton, H.J., Barón, A.E., and Juarez-Colunga, E. (2018), ‘The Wilcoxon-Mann-Whitney Procedure Fails as a Test of Medians’, The American Statistician, 72(3), 278–286.
  • Efron, B. (1979), ‘Bootstrap Methods: Another Look at the Jackknife’, The Annals of Statistics, 7(1), 1–26.
  • Galbraith, S., Daniel, J.A., and Vissel, B. (2010), ‘A Study of Clustered Data and Approaches to Its Analysis’, Journal of Neuroscience, 30(32), 10601–10608.
  • Ho, Y.H., and Lee, S.M. (2005), ‘Iterated Smoothed Bootstrap Confidence Intervals for Population Quantiles’, The Annals of Statistics, 33(1), 437–462.
  • Ignacio Lopez-de Ullibarri, I., Janssen, P., and Cao, R. (2012), ‘Continuous Covariate Frailty Models for Censored and Truncated Clustered Data’, Journal of Statistical Planning and Inference, 142, 1864–1877.
  • Keziou, A., and Leoni-Aubin, S. (2008), ‘On Empirical Likelihood for Semiparametric Two-Sample Density Ratio Models’, Journal of Statistical Planning and Inference, 138(4), 915–928.
  • Kruskal, W.H. (1952), ‘A Nonparametric Test for the Several Sample Problem’, The Annals of Mathematical Statistics, 23(4), 525–540.
  • Lahiri, S.N., Das, U., and Nordman, D.J. (2019), ‘Empirical Likelihood for a Long Range Dependent Process Subordinated to a Gaussian Process’, Journal of Time Series Analysis, 40(4), 447–466.
  • Li, Z., Xu, X., and Shen, J. (2017), ‘Semiparametric Bayesian Analysis of Accelerated Failure Time Models with Cluster Structures’, Statistics in Medicine, 36(25), 3976–3989.
  • Lindsay, B.G. (1988), ‘Composite Likelihood Methods’, Contemporary Mathematics, 80(1), 221–39.
  • Loh, W.-Y. (1991), ‘Bootstrap Calibration for Confidence Interval Construction and Selection’, Statistica Sinica, 1(2), 477–491.
  • Lohr, S. (2009), Sampling: Design and Analysis, Pacific Grove: Duxbury Press.
  • Matteson, D.S., and James, N.A. (2014), ‘A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data’, Journal of the American Statistical Association, 109(505), 334–345.
  • Nadarajah, S., and Gupta, A.K. (2006), ‘Some Bivariate Gamma Distributions’, Applied Mathematics Letters, 19(8), 767–774.
  • Nevalainen, J., Larocque, D., Oja, H., and Pörsti, I. (2010), ‘Nonparametric Analysis of Clustered Multivariate Data’, Journal of the American Statistical Association, 105(490), 864–872.
  • Owen, A. (2001), Empirical Likelihood, New York: Chapman & Hall/CRC.
  • Ozturk, O., and Turkmen, A. (2016), ‘Quantile Inference Based on Clustered Data’, Metrika, 79(7), 867–893.
  • Qin, J., and Zhang, B. (1997), ‘A Goodness-of-Fit Test for Logistic Regression Models Based on Case-Control Data’, Biometrika, 84(3), 609–618.
  • Rosner, B., Glynn, R.J., and Lee, M.-L. (2003), ‘Incorporation of Clustering Effects for the Wilcoxon Rank Sum Test: A Large-Sample Approach’, Biometrics, 59(4), 1089–1098.
  • Rosner, B., Glynn, R.J., and Lee, M.-L. (2006), ‘The Wilcoxon Signed Rank Test for Paired Comparisons of Clustered Data’, Biometrics, 62(1), 185–192.
  • Shao, J., and Tu, D. (1995), The Jackknife and Bootstrap, New York: Springer.
  • Tsao, M., and Wu, F. (2015), ‘Two-sample Extended Empirical Likelihood for Estimating Equations’, Journal of Multivariate Analysis, 142, 1–15.
  • Varin, C., Reid, N., and Firth, D. (2011), ‘An Overview of Composite Likelihood Methods’, Statistica Sinica, 21(1), 5–42.
  • Verrill, S., Kretschmann, D.E., and Evans, J.W. (2015), Simulations of strength property monitoring tests. Unpublished manuscript. Forest Products Laboratory, Madison, Wisconsin. Available at http://www1.fpl.fs.fed.us/monit.pdf.
  • Wu, C.J., and Hamada, M.S. (2011), Experiments: Planning, Analysis, and Optimization (2nd ed.), Inc. Hoboken, New Jersey: John Wiley & Sons.
  • Zeger, S.L., and Liang, K.-Y. (1986), ‘Longitudinal Data Analysis for Discrete and Continuous Outcomes’, Biometrics, 42(1), 121–130.

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