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Bootstrap Methods - With Applications in R (Hardcover, 1st ed. 2021)
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Bootstrap Methods - With Applications in R (Hardcover, 1st ed. 2021)
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This book provides a compact introduction to the bootstrap method.
In addition to classical results on point estimation and test
theory, multivariate linear regression models and generalized
linear models are covered in detail. Special attention is given to
the use of bootstrap procedures to perform goodness-of-fit tests to
validate model or distributional assumptions. In some cases, new
methods are presented here for the first time. The text is
motivated by practical examples and the implementations of the
corresponding algorithms are always given directly in R in a
comprehensible form. Overall, R is given great importance
throughout. Each chapter includes a section of exercises and, for
the more mathematically inclined readers, concludes with rigorous
proofs. The intended audience is graduate students who already have
a prior knowledge of probability theory and mathematical
statistics.
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