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作者：TSAI, WY
摘要：For data subject to random censorship, it is well known that the assumption of independence between censoring time and failure time cannot be tested nonparametrically. For data subject to random truncation, however, it is possible to test independence of truncation time and failure time. A statistic generalizing Kendall''s tau is proposed for testing the independence assumption, and asymptotic properties under the null hypothesis are derived. A conditional permutation test is also derived for ...
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作者：LEE, SY
摘要：This paper considers the multilevel analysis of structural equation models with unbalanced sampling designs. The analysis is based on the maximum likelihood and the generalized least squares approaches. Basic statistical results for inference such as the asymptotic distribution of the estimators, goodness-of-fit test statistics for the validity of the model and functional constraints are developed. Computationally, the application of the scoring algorithm and the Gauss-Newton algorithm are dis...
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作者：STENGER, H
摘要：The use of simple random sampling together with ratio estimation is of fundamental importance in survey sampling practice. From a theoretical point of view the ratio estimator has been justified by Bayesian methods (Ericson, 1969) and by superpopulation methods (Royall, 1970). But these methods do not yield a rigorous justification of simple random sampling at the same time. We use the minimax principle in an asymptotic version to justify both simple random sampling and ratio estimation.
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作者：MULLER, HG; WANG, JL
摘要：As a nonparametric estimator for the point of the most rapid change of a hazard rate we propose the location of an extremum of a nonparametric estimate of the derivative, or equivalently, of a zero of a nonparametric estimate of the second derivative. Using the kernel method for the nonparametric estimation of derivatives of the hazard rate, the asymptotic local limiting distribution and uniform consistency are applied to prove consistency and to find the limiting distribution of these estimat...
