Nonparametric Tests for Multiple Regression Under Progressive Censoring.
NORTH CAROLINA UNIV AT CHAPEL HILL DEPT OF BIOSTATISTICS
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For continuous observations from time-sequential studies, suitable Cramer-von Mises and Kolmogorov-Smirnov type nonparametric statistics based on linear rank statistics for testing hypotheses on some multiple regression models are proposed and studied. Asymptotic theory of these tests is provided for both the null and local alternative hypotheses situations and is based on the weak convergence of suitable rank order processes on the D0,1 space to certain functions of Brownian Motions. Bahadur efficiency results are also presented. Empirical values of the percentile points of the null distributions of the proposed test statistics, obtained through simulation studies, are also provided. Author
- Statistics and Probability