George Casella Libros

This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.

Focusing on statistical analysis and modeling, this resource is tailored for graduate students and researchers in agriculture and related fields. It covers essential concepts and methods for analyzing DNA-based markers and phenotypic data, incorporating various statistical models and analytical procedures to enhance understanding and application in agricultural research.

Focusing on the evolution of statistical design, the book highlights its foundational role in the development of statistics, particularly in agricultural contexts. It emphasizes the importance of understanding the sources of variance and ensuring proper replication in experiments. The principles established in the past are presented as crucial for contemporary practice, underscoring their enduring relevance in the field of statistics.

Markov chain Monte Carlo methods were developed to provide the experimenter with realistic models. Written by two leading researchers, this up-to-date reference covers an important area of statistic which has many applications to engineering, aeronautics, biology, networks, and astronomy.

This second, much enlarged edition by Lehmann and Casella of Lehmann's classic text on point estimation maintains the outlook and general style of the first edition. All of the topics are updated, while an entirely new chapter on Bayesian and hierarchical Bayesian approaches is provided, and there is much new material on simultaneous estimation. Each chapter concludes with a Notes section which contains suggestions for further study. This is a companion volume to the second edition of Lehmann's "Testing Statistical Hypotheses."

Classical statistical theory is mainly the creation of two men: Ronald A. Fisher and Jerzy Neyman. This book explores the relationship between them, their interactions with other influential statisticians and the statistical history they helped create.

Focusing on statistical simulation, this book offers a programmer's perspective on essential tools and techniques. It details the R implementation of various simulation methods, enhancing comprehension through practical output examples. This approach facilitates better understanding and comparison of different statistical simulations.

This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.

A major new interpretation of the Zheng family of merchants and militarists, who dominated the seventeenth-century China Seas.