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This book treats the latest developments in the theory of
order-restricted inference, with special attention to nonparametric
methods and algorithmic aspects. Among the topics treated are
current status and interval censoring models, competing risk
models, and deconvolution. Methods of order restricted inference
are used in computing maximum likelihood estimators and developing
distribution theory for inverse problems of this type. The authors
have been active in developing these tools and present the state of
the art and the open problems in the field. The earlier chapters
provide an introduction to the subject, while the later chapters
are written with graduate students and researchers in mathematical
statistics in mind. Each chapter ends with a set of exercises of
varying difficulty. The theory is illustrated with the analysis of
real-life data, which are mostly medical in nature.
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