Asymptotic Statistics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 3)
Product Description
Asymptotic Statistics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 3)
- Used Book in Good Condition
Here is a practical and mathematically rigorous introduction to the field of asymptotic statistics. In addition to most of the standard topics of an asymptotics course--likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures--the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, one of the book's unifying themes that mainly entails the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation.
Technical Specifications
You might also like
High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics)
Testing Statistical Hypotheses (Springer Texts in Statistics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics)
Probability: Theory and Examples (Cambridge Series in Statistical and Probabilistic Mathematics)
A Course in Large Sample Theory (Chapman & Hall/CRC Texts in Statistical Science)
High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics)
Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs)