Bøger af Nitis Mukhopadhyay

This gracefully organized text presents the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. Beginning with the basic ideas and techniques of probability theory and progressing to more rigor

Introductory Statistical Inference develops the concepts and intricacies of statistical inference. With a review of probability concepts, this book discusses topics such as sufficiency, ancillarity, point estimation, minimum variance estimation, confidence intervals, multiple comparisons, and large-sample inference. It introduces techniques of two-stage sampling, fitting a straight line to data, tests of hypotheses, nonparametric methods, and the bootstrap method. It also features worked examples of statistical principles as well as exercises with hints. This text is suited for courses in probability and statistical inference at the upper-level undergraduate and graduate levels.

Through this book and its computer programs, readers will better understand the methods of sequential analysis and be able to use them in real-world settings. The book explains concepts, methodologies, implementations, and properties in layman¿s terms. It also provides essential computing tools so readers can run simulations and perform experiments with real or simulated data. The authors cover basic sampling techniques and inference procedures, address competing sampling methodologies in several statistical problems, and emphasize the relevance and importance of sequential methodologies in clinical trials, integrated pest management, experimental psychology, horticulture, and more.

Presents the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. This book covers various topics typically addressed in a two-semester course in probability and statistical inference.

Develops the concepts and intricacies of statistical inference. This book discusses topics such as sufficiency, ancillarity, point estimation, minimum variance estimation, confidence intervals, multiple comparisons, and large-sample inference. It introduces techniques of two-stage sampling, fitting a straight line to data, and tests of hypotheses.

Illustrates the efficiency of sequential methodologies when dealing with contemporary statistical challenges in many areas. This book explores fixed sample size, sequential probability ratio, and nonparametric tests. It also presents multistage estimation methods for fixed-width confidence interval as well as minimum and bounded risk problems.