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Authors Badonavicius and Nikulin have developed a large and important class of survival analysis models that generalize most of the existing models. In a unified, systematic presentation that does not get bogged down in technical details, this monograph fully examines those models and explores areas of accelerated life testing usually only touched upon in the literature. In addition to the classical results, the authors devote considerable attention to models with time-varying explanatory variables and to methods of semiparametric estimation. The authors include goodness-of-fit tests for the most important models. This book is valuable as both a high-level textbook and as a professional reference.
This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided.
Examines survival analysis models and explores areas of accelerated life testing usually only touched upon in the literature. This book focuses with time-varying explanatory variables and to methods of semiparametric estimation. It includes goodness-of-fit tests for the important models.
This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided.
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