Mathematical Statistics with Applications in R

Mathematical Statistics with Applications in R
Author :
Publisher : Elsevier
Total Pages : 825
Release :
ISBN-10 : 9780124171329
ISBN-13 : 012417132X
Rating : 4/5 (32X Downloads)

Book Synopsis Mathematical Statistics with Applications in R by : Kandethody M. Ramachandran

Download or read book Mathematical Statistics with Applications in R written by Kandethody M. Ramachandran and published by Elsevier. This book was released on 2014-09-14 with total page 825 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner.This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students.Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. - Step-by-step procedure to solve real problems, making the topic more accessible - Exercises blend theory and modern applications - Practical, real-world chapter projects - Provides an optional section in each chapter on using Minitab, SPSS and SAS commands - Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods


Mathematical Statistics with Applications in R Related Books

Mathematical Statistics with Applications in R
Language: en
Pages: 825
Authors: Kandethody M. Ramachandran
Categories: Mathematics
Type: BOOK - Published: 2014-09-14 - Publisher: Elsevier

DOWNLOAD EBOOK

Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applicati
Mathematical Statistics with Resampling and R
Language: en
Pages: 557
Authors: Laura M. Chihara
Categories: Mathematics
Type: BOOK - Published: 2018-09-17 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

This thoroughly updated second edition combines the latest software applications with the benefits of modern resampling techniques Resampling helps students und
Probability and Mathematical Statistics
Language: en
Pages: 720
Authors: Mary C. Meyer
Categories: Mathematics
Type: BOOK - Published: 2019-06-24 - Publisher: SIAM

DOWNLOAD EBOOK

This book develops the theory of probability and mathematical statistics with the goal of analyzing real-world data. Throughout the text, the R package is used
Advanced Statistics with Applications in R
Language: en
Pages: 880
Authors: Eugene Demidenko
Categories: Mathematics
Type: BOOK - Published: 2019-11-12 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

Advanced Statistics with Applications in R fills the gap between several excellent theoretical statistics textbooks and many applied statistics books where teac
Modern Mathematical Statistics with Applications
Language: en
Pages: 981
Authors: Jay L. Devore
Categories: Mathematics
Type: BOOK - Published: 2021-04-29 - Publisher: Springer Nature

DOWNLOAD EBOOK

This 3rd edition of Modern Mathematical Statistics with Applications tries to strike a balance between mathematical foundations and statistical practice. The bo