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Comparison Of Two Samples By A Nonparametric Likelihood-Ratio Test, William H. Barton
Comparison Of Two Samples By A Nonparametric Likelihood-Ratio Test, William H. Barton
University of Kentucky Doctoral Dissertations
In this dissertation we present a novel computational method, as well as its software implementation, to compare two samples by a nonparametric likelihood-ratio test. The basis of the comparison is a mean-type hypothesis. The software is written in the R-language [4]. The two samples are assumed to be independent. Their distributions, which are assumed to be unknown, may be discrete or continuous. The samples may be uncensored, right-censored, left-censored, or doubly-censored. Two software programs are offered. The first program covers the case of a single mean-type hypothesis. The second program covers the case of multiple mean-type hypotheses. For the first …