Theory of Statistics by Mark J Schervish
The aim of this graduate textbook is to provide a comprehensive advanced course in the theory of statistics covering those topics in estimation, testing, and large sample theory which a graduate student might typically need to learn as preparation for work on a Ph.D. An important strength of this book is that it provides a mathematically rigorous account of both classical and Bayesian inference in order to give readers a broad perspective. For example, the uniformly most powerful approach to testing is contrasted with available decision-theoretic approaches. Commencing with chapters on probability models and the theory of sufficient statistics, the author covers decision theory, hypothesis testing, estimation, equivariance, large sample theory, hierarchical models, and, finally, sequential analysis. Every chapter concludes with exercises which range in difficulty from the easy to the challenging. As a result, this textbook provides an excellent course in modern theoretical statistics.-
The Elements of Statistical Learning
-
Dragons of Winter Night
-
Functional Data Analysis
-
Time Series: Theory and Methods
-
Modeling Discrete Time-to-Event Data
-
Targeted Learning
-
Models for Discrete Longitudinal Data
-
Regression Modeling Strategies
-
Targeted Learning in Data Science
-
An Introduction to Sequential Monte Carlo
-
Sampling Algorithms
-
Modern Multidimensional Scaling
-
Correlation Theory of Stationary and Related Random Functions
-
Mathematical Statistics
-
Breakthroughs in Statistics
-
Annotated Readings in the History of Statistics
-
Statistical Models Based on Counting Processes
-
Hidden Markov Processes and Adaptive Filtering
-
Design of Observational Studies
-
Chaos: A Statistical Perspective
-
A Comparison of the Bayesian and Frequentist Approaches to Estimation
-
Smoothing Spline ANOVA Models
-
Analysis of Neural Data
-
Finite Mixture and Markov Switching Models
-
The Gini Methodology
-
Ten Projects in Applied Statistics
-
Growth Curve Models and Statistical Diagnostics
-
Statistical Methods in Software Engineering
-
Gaussian and Non-Gaussian Linear Time Series and Random Fields
-
Markov Bases in Algebraic Statistics
-
Data
-
A Course on Point Processes
-
Shrinkage Estimation
-
Bayesian and Frequentist Regression Methods
-
A Statistical Model
-
Prediction Theory for Finite Populations
-
Tools for Statistical Inference
-
Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
-
ARMA Model Identification
-
Statistical Design and Analysis for Intercropping Experiments
-
Approximate Distributions of Order Statistics
From the reviews:
"Another excellent book in theory of statistics is by Mark JSchervish. … Readers will enjoy reading this book to see how differently the theory can be presented … . This well written book contains nine chapters and four appendices. ... Each chapter has both easy and challenging problems. The book is suitable for graduate level statistical theory courses. Examples and illustrations are well explained. I liked the author’s presentation, and learned a lot from the book. I highly recommend this book to theoretical statisticians." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 74 (11), November, 2004)
| SKU | Unavailable |
| ISBN 13 | 9781461287087 |
| ISBN 10 | 1461287081 |
| Title | Theory of Statistics |
| Author | Mark J Schervish |
| Series | Springer Series In Statistics |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2011-11-21 |
| Number of pages | 716 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |