Mathematical Statistics with Applications in R 3rd Edition
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
About the Author
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.
Chris P. Tsokos is Distinguished University Professor of Mathematics and Statistics at the University of South Florida. Dr. Tsokos’ research has extended into a variety of areas, including stochastic systems, statistical models, reliability analysis, ecological systems, operations research, time series, Bayesian analysis, and mathematical and statistical modelling of global warming, both parametric and nonparametric survival analysis, among others. He is the author of more than 400 research publications in these areas, including Random Integral Equations with Applications to Life Sciences and Engineering, Probability Distribution: An Introduction to Probability Theory with Applications, Mainstreams of Finite Mathematics with Applications, Probability with the Essential Analysis, Applied Probability Bayesian Statistical Methods with Applications to Reliability, and Mathematical Statistics with Applications, among others.
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