F. J. Samaniego has served on the faculty of the University of California, Davis, for four decades, teaching upper division courses on probability and mathematical statistics numerous times. In 2002, he received the UCD Academic Senate Distinguished Teaching Award. He was the 2004 recipient of the Davis Prize for Undergraduate Teaching and Scholarly Achievement.

"Stochastic Modeling and Mathematical Statistics is a new and welcome addition to the corpus of undergraduate statistical textbooks in the market. The singular thing that struck me when I initially perused the book was its lucid and endearing conversational tone, which pervades the entire text. It radiated warmth. … In my course at the University of Michigan, I rely primarily on my own lecture notes and have used Rice as supplementary material. Having gone through this text, I am strongly inclined to add this to the supplementary list as well. I have little doubt that this book will be very successful as a course textbook in the years to come."—International Statistical Review, 82, 2014

• The Calculus of Probability. Discrete Probability Models. Continuous Probability Models. Multivariate Models. Limit Theorems and Related Topics. Statistical Estimation: Fixed Sample Size Theory. Statistical Estimation: Asymptotic Theory. Interval Estimation. The Bayesian Approach to Estimation. Hypothesis Testing. Estimation and Testing for Linear Models. Nonparametric Statistical Methods. Tables. Bibliography. Index.

F. J. Samaniego has served on the faculty of the University of California, Davis, for four decades, teaching upper division courses on probability and mathematical statistics numerous times. In 2002, he received the UCD Academic Senate Distinguished Teaching Award. He was the 2004 recipient of the Davis Prize for Undergraduate Teaching and Scholarly Achievement.

"Stochastic Modeling and Mathematical Statistics is a new and welcome addition to the corpus of undergraduate statistical textbooks in the market. The singular thing that struck me when I initially perused the book was its lucid and endearing conversational tone, which pervades the entire text. It radiated warmth. … In my course at the University of Michigan, I rely primarily on my own lecture notes and have used Rice as supplementary material. Having gone through this text, I am strongly inclined to add this to the supplementary list as well. I have little doubt that this book will be very successful as a course textbook in the years to come."—International Statistical Review, 82, 2014

• The Calculus of Probability. Discrete Probability Models. Continuous Probability Models. Multivariate Models. Limit Theorems and Related Topics. Statistical Estimation: Fixed Sample Size Theory. Statistical Estimation: Asymptotic Theory. Interval Estimation. The Bayesian Approach to Estimation. Hypothesis Testing. Estimation and Testing for Linear Models. Nonparametric Statistical Methods. Tables. Bibliography. Index.

F. J. Samaniego has served on the faculty of the University of California, Davis, for four decades, teaching upper division courses on probability and mathematical statistics numerous times. In 2002, he received the UCD Academic Senate Distinguished Teaching Award. He was the 2004 recipient of the Davis Prize for Undergraduate Teaching and Scholarly Achievement.

"Stochastic Modeling and Mathematical Statistics is a new and welcome addition to the corpus of undergraduate statistical textbooks in the market. The singular thing that struck me when I initially perused the book was its lucid and endearing conversational tone, which pervades the entire text. It radiated warmth. … In my course at the University of Michigan, I rely primarily on my own lecture notes and have used Rice as supplementary material. Having gone through this text, I am strongly inclined to add this to the supplementary list as well. I have little doubt that this book will be very successful as a course textbook in the years to come."—International Statistical Review, 82, 2014

• The Calculus of Probability. Discrete Probability Models. Continuous Probability Models. Multivariate Models. Limit Theorems and Related Topics. Statistical Estimation: Fixed Sample Size Theory. Statistical Estimation: Asymptotic Theory. Interval Estimation. The Bayesian Approach to Estimation. Hypothesis Testing. Estimation and Testing for Linear Models. Nonparametric Statistical Methods. Tables. Bibliography. Index.