Mathematical Statistics with Applications in R
by: Kandethody M. Ramachandran and Chris P. Tsokos
Paperback: 704 pages
Publisher Finelybook 出版社: Academic Press; 3 edition (29 July 2020)
Language 语言: English
ISBN-10: 0128178159
ISBN-13: 9780128178157
Book Description
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.
Preface
Flow chart
1.Descriptive statistics
2.Basic concepts from probability theory
3.Additional topics in probability
4.Sampling distributions
5.Statistical estimation
6.Hypothesis testing
7.Linear regression models
8.Design of experiments
9.Analysis of variance
10.Bayesian estimation and inference
11.Categorical data analysis and goodness-of-fit tests and
applications
12-Nonparametric Statistics
13.Empirical methods
14.Some issues in statistical applications: an overview
Appendix l.Set theory
Appendix l.Review of Markov chains
Appendix ll.Common probability distributions
Appendix IⅣ.What is R?
Appendix V.Probability tables
References
Index
Mathematical Statistics with Applications in R,3rd Edition
相关推荐
- Python GUI with PyQt: Learn to build modern and stunning GUIs in Python with PyQt5 and Qt Designer
- Machine Learning with Python for Everyone
- Object Design Style Guide: Powerful techniques for creating flexible, readable, and maintainable object-oriented code in any OO language, from Python to PHP
- Android Malware Detection using Machine Learning: Data-Driven Fingerprinting and Threat Intelligence
finelybook
