Kandethody M. Ramachandran, Chris P. Tsokos

Mathematical Statistics with Applications in R (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 704
• Erscheinungstermin: 14. Mai 2020
• Englisch
• ISBN-13: 9780128178164
• Artikelnr.: 61681504

Autorenporträt

Kandethody M Ramachandran is a Professor of Mathematics and Statistics at the University of South Florida (USF). His research interests are concentrated in the areas of applied probability and statistics. His research publications span a variety of areas such as control of heavy traffic queues, stochastic delay systems, machine learning methods applied to game theory, finance, cyber security, and other areas, software reliability problems, applications of statistical methods to microarray data analysis, and streaming data analysis. He is also, co-author of three books. He is the founding director of the Interdisciplinary Data Sciences Consortium (IDSC). He is extensively involved in activities to improve statistics and mathematics education. He is a recipient of the Teaching Incentive Program award at the University of South Florida. He is also the PI of 2 million dollar grant from NSF, and a co_PI of 1.4 million grant from HHMI to improve STEM education at USF.

Inhaltsangabe

1. Descriptive Statistics2. Basic Concepts from Probability Theory3.
Additional Topics in Probability4. Sampling Distributions5. Statistical
Estimation6. Hypothesis Testing7. Linear Regression models8. Design of
Experiments9. Analysis of Variance 10. Bayesian Estimation and Inference11.
Categorical Data Analysis and Goodness of Fit Tests and Applications12.
Nonparametric Tests13. Empirical Methods14. Some applications and Some
Issues in Statistical Applications: An Overview