Mathematical Statistics with Resampling and R Reviewed By Manoj Rengarajan of Bookpleasures.com

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Manoj Rengarajan

**Reviewer Manoj
Rengarajan **holds a Master of Financial Engineering - University of
California, Berkeley and he works in the investment management
industry and specializes in providing economic and investment outlook
and strategy for global equity and government bond markets. He has an
educational background in financial engineering, business, and
engineering, and professional interests include business,
finance, economics, technology and related areas.

By Manoj Rengarajan

Published on February 10, 2012

Publisher: Wiley Blackwell

Authors: Laura Chihara & Tim Hesterberg

ISBN: 978-1-118-02985-5

Publisher: Wiley Blackwell

ISBN: 978-1-118-02985-5

Follow Here To Purchase Mathematical Statistics with Resampling and R

Publisher: Wiley
Blackwell

Authors: Laura Chihara & Tim Hesterberg

ISBN:
978-1-118-02985-5

Students of statistics often find concepts
such as sampling distributions, p-values, hypothesis test and
confidence interval a bit difficult to internalize. Mathematical
Statistics with Resampling and R aims to demonstrate these concepts
by resampling and to apply resampling techniques to mathematical
statistics.

The book is targeted towards upper undergraduate
and graduate students. The emphasis of the book is on modern
resampling techniques including permutation tests and bootstrapping.
These techniques are introduced even before classical inference
methods are discussed which helps in understanding basic statistical
concepts better.

There is a discussion of several statistical
case studies followed by discussion of basic concepts and
definitions. Elementary data analysis is covered with an emphasis on
plots, summary statistics, box plots and empirical cumulative
distributive functions.

The book aims for a balance between
theory, computing and application. There are numerous case studies
from diverse fields through the book which illustrate real world
applications of the techniques. Apart from covering the theory
clearly, the book employs “R notes” to show a series of examples
in R on implementing the concepts.

While discussing hypothesis
testing, the book covers assumptions and implementation issues with
permutation tests, test of homogeneity, and goodness of fit
with all parameters are known and when some are estimated. The
section on sampling distribution discusses computation/estimation,
central limit theorem, and central limit theorem for binomial
data.

Bootstrapping section deals with estimating the
bootstrapping function, how well the bootstrapping distribution
approximates the sampling distribution, and two sample bootstraps.
The section ends with practical issues such as bias and
accuracy of bootstrapping distributions.

Estimation discusses
a very general method using maximum likelihood estimate both for
discrete and continuous cases, method of moments, and properties of
estimators. R Note is used to demonstrate implementation of the
concepts.

Classical inference is covered in the latter part of
the book including standard topics like confidence intervals for
means and proportions, bootstrap, hypothesis testing for means and
proportions, type 1 & type 2 errors, and likelihood ratio tests.
The section on regression covers least square regression, simple
linear model, and resampling correlation and regression.

The
book concludes with additional topics including smoothed
bootstrapping, parametric bootstrap, stretched sampling, importance
sampling, and Markov Chain integration. Review of probability and
probability distributions collects definitions and theorems of
important probability distributions as background.

Mathematical
Statistics with Resampling and R is a great resource for intermediate
and advanced statistics students who want to achieve an in depth
understanding of re-sampling techniques backed by practical
implementation.