Weighted U-statistics for likelihood-ratio ordering of bivariate data

Sangita Kulathinal () and Isha Dewan
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Sangita Kulathinal: University of Helsinki
Isha Dewan: Indian Statistical Institute

Statistical Papers, 2023, vol. 64, issue 2, No 14, 705-735

Abstract: Abstract Characterisation of marginal distribution and density functions is of interest where data on a pair of random variables (X, Y) are observed. Stochastic orderings between (X, Y) have been studied in statistics and economics. Likelihood-ratio ordering is useful in understanding the behaviour of the random variables. In this article, tests based on U-statistics are proposed to test for equality of marginal density functions against the alternative of likelihood-ratio ordered when (X, Y) are dependent. The tests can be used when the data are either completely observed or subjected to independent univariate right censoring. The asymptotic variances of these tests are complicated and hence, are estimated using jackknife variance estimators. Validity of the jackknife variance estimators in statistical inference based on the proposed tests is demonstrated using simulation studies. The test for uncensored setting has desired size and good power for small sample. The performance of the tests for censored case depends on the sample size, proportion of censoring and the measure of dependence between X and Y. The tests are illustrated on three real data sets chosen in order to bring out various aspects of the tests.

Keywords: U-statistics; Censored data; Bivariate distributions; Likelihood-ratio ordering; Kaplan-Meier estimator; Inverse probability of censoring weights (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00362-022-01332-w

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