Asymptotic theory of statistical inference

Author(s)

• Prakasa Rao, B. L. S.

Bibliographic Information

Asymptotic theory of statistical inference

B.L.S. Prakasa Rao

（Wiley series in probability and mathematical statistics, . Probability and mathematical statistics）

Wiley, c1987

Note

Includes bibliographies and index

Description and Table of Contents

Description

An up-to-date and concise description of recent results in probability theory and stochastic processes useful in the study of asymptotic theory of statistical inference. The book brings together new material on the interplay between recent advances in probability theory and their applications to the asymptotic theory of statistical inference. Asymptotic theory of maximum likelihood and Bayes estimation, asymptotic properties of least squares estimators in non-linear regression, and estimators of parameters for stable laws are discussed from the point of view of stochastic processes. This leads to better results than the Taylor expansions approach used in the classical theory of maximum likelihood estimation.

Table of Contents

• Probability and Stochastic Processes
• Limit Theorems for Some Statistics
• Asymptotic Theory of Estimation
• Linear Parametric Inference
• Martingale Approach to inference
• Inference in Non-Linear Regression
• Von-Mises Functionals
• Empirical Characteristic Function and Its Applications
• Index.

by "Nielsen BookData"

Details

• NCID: BA0043902X
• ISBN:
  • 0471843350
• LCCN: 86015735
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: New York
• Pages/Volumes: xiv, 438 p.
• Size: 24 cm
• Classification:
  • LCC : QA276
  • DC19 : 519.5/4
• Subject Headings:
  • LCSH : Mathematical statistics -- Asymptotic theory
• Parent Bibliography ID:
  • BA00019013