Mathematical Statistics with Applications in R - Hardcover

K. M. Ramachandran; Chris P. Tsokos

Hardcover

ISBN 10: 0124171133 ISBN 13: 9780124171138

Publisher: Academic Press Inc, 2014

Synopsis

Mathematical Statistics with Applications, Second Edition, gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner. The book introduces 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. Goodness of fit methods are included to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Engineering students, especially, will find these methods to be very important in their studies. * Step-by-step procedure to solve real problems, making the topic more accessible* Exercises blend theory and modern applications* Practical, real-world chapter projects * Provides an optional section in each chapter on using Minitab, SPSS and SAS commands* Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods* Instructor's Manual; Solutions to Selected Problems, data sets, and image bank for students

About this title

About the Authors

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. Dr. Tsokos is the inventor or co-inventor recipient of several U.S. patents in cybersecurity and health sciences.

From the Back Cover

Mathematical Statistics with Applications, Second Edition gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner.The book introduces 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. Goodness of fit methods are included to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Engineering students, especially, will find these methods to be very important in their studiesKey Features• Includes case studies to show how statistics can be applied to real world society problems such as cancer research, hurricanes, and global warming• Provides an optional section in each chapter on using R, Minitab, SPSS and SAS commands• Practical, real-world chapter projects

Edition details

• Publisher: Academic Press Inc
• Publication date: 2014
• Language: English
• ISBN 10: 0124171133
• ISBN 13: 9780124171138
• Binding: Hardcover
• Edition number: 2
• Number of pages: 740