Mathematical Statistics With Applications In R
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Mathematical Statistics with Applications
Author: Kandethody M. Ramachandran
language: en
Publisher: Academic Press
Release Date: 2009-03-13
Mathematical Statistics with Applications provides a calculus-based theoretical introduction to mathematical statistics while emphasizing interdisciplinary applications as well as exposure to modern statistical computational and simulation concepts that are not covered in other textbooks. Includes the Jackknife, Bootstrap methods, the EM algorithms and Markov chain Monte Carlo methods. Prior probability or statistics knowledge is not required. 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
Mathematical Statistics with Applications in R
Author: Kandethody M. Ramachandran
language: en
Publisher: Academic Press
Release Date: 2020-05-14
Mathematical Statistics with Applications in R, Third 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 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. Step-by-step procedure to solve real problems make the topics very accessible. - Presents step-by-step procedures to solve real problems, making each topic more accessible - Provides updated application exercises in each chapter, blending theory and modern methods with the use of R - Includes new chapters on Categorical Data Analysis and Extreme Value Theory with Applications - Wide array coverage of ANOVA, Nonparametric, Bayesian and empirical methods
Mathematical Statistics with Resampling and R
Author: Laura M. Chihara
language: en
Publisher: John Wiley & Sons
Release Date: 2022-08-09
Mathematical Statistics with Resampling and R This thoroughly updated third edition combines the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. The third edition of Mathematical Statistics with Resampling and R combines modern resampling techniques and mathematical statistics. This book is classroom-tested to ensure an accessible presentation, and uses the powerful and flexible computer language R for data analysis. This book introduces permutation tests and bootstrap methods to motivate classical inference methods, as well as to be utilized as useful tools in their own right when classical methods are inaccurate or unavailable. The book strikes a balance between simulation, computing, theory, data, and applications. Throughout the book, new and updated case studies representing a diverse range of subjects, such as flight delays, birth weights of babies, U.S. demographics, views on sociological issues, and problems at Google and Instacart, illustrate the relevance of mathematical statistics to real-world applications. Changes and additions to the third edition include: New and updated case studies that incorporate contemporary subjects like COVID-19 Several new sections, including introductory material on causal models and regression methods for causal modeling in practice Modern terminology distinguishing statistical discernibility and practical importance New exercises and examples, data sets, and R code, using dplyr and ggplot2 A complete instructor’s solutions manual A new github site that contains code, data sets, additional topics, and instructor resources Mathematical Statistics with Resampling and R is an ideal textbook for undergraduate and graduate students in mathematical statistics courses, as well as practitioners and researchers looking to expand their toolkit of resampling and classical techniques.