Mathematical statistics with applications in R
著者
書誌事項
Mathematical statistics with applications in R
Academic Press , Elsevier, c2015
2nd ed
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注記
Includes bibliographical references
収録内容
- Descriptive statistics
- Basic concepts from probability theory
- Additional topics in probability
- Sampling distributions
- Statistical estimation
- Hypothesis testing
- Goodness-of-fit tests and applications
- Linear regression models
- Design of experiments
- Analysis of variance
- Bayesian estimation and inference
- Nonparametric tests
- Empirical methods
- Some issues in statistical applications: an overview
内容説明・目次
内容説明
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.
目次
1. Descriptive Statistics2. Basic Concepts from Probability Theory 3. Additional Topics in Probability4. Sampling Distributions5. Estimation6. Properties of Point Estimation, Hypothesis Testing7. Linear Regression Models8. Design of Experiments9. Analysis of variance10. Bayesian Estimation and Inference11. Nonparametric tests12. Empirical Methods13. Time-series Analysis14. Overview of Statistical Applications15. Appendices16. Selected Solutions to Exercises
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