Mathematical Statistics with Applications in R

Häftad, Engelska, 2020

Av Kandethody M. Ramachandran, Chris P. Tsokos, Kandethody M. (Professor of Mathematics and Statistics at the University of South Florida (USF)) Ramachandran, Chris P. (Distinguished University Professor of Mathematics and Statistics at the University of South Florida) Tsokos, Kandethody M Ramachandran, Chris P Tsokos

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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

Produktinformation

Utgivningsdatum2020-07-21
Mått216 x 276 x 30 mm
Vikt1 430 g
FormatHäftad
SpråkEngelska
Antal sidor704
Upplaga3
FörlagElsevier Science
ISBN9780128178157

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Kandethody M. Ramachandran is Professor of Mathematics and Statistics at the University of South Florida. His research interests are concentrated in the areas of applied probability, statistics, machine learning, and generative AI. 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, health sciences, and other emerging areas. 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 a two million dollar grant from NSF, and a co_PI of a 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. Dr. Tsokos is the recipient of many distinguished awards and honors, including Fellow of the American Statistical Association, USF Distinguished Scholar Award, Sigma Xi Outstanding Research Award, USF Outstanding Undergraduate Teaching Award, USF Professional Excellence Award, Pi Mu Epsilon, election to the International Statistical Institute, Sigma Pi Sigma, USF Teaching Incentive Program, and several humanitarian and philanthropic recognitions and awards. He is also serves as Honorary Editor, Chief-Editor, Editor or Associate Editor for more than twelve academic research journals.

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