Statistical Inference in Stochastic Processes

✍ Scribed by Prabhu, N U(Editor)

Publisher: Routledge
Year: 1990
Tongue: English
Leaves: 289
Series: Contemporary mathematics
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Covering both theory and applications, this collection surveys the role of probabilistic models and statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters.

✦ Table of Contents

Cover......Page 1
Half Title......Page 2
Series Page......Page 3
Title Page......Page 4
Copyright Page......Page 5
Preface......Page 6
Contributors......Page 8
Table of Contents......Page 10
1 Statistical Models and Methods in Image Analysis: A Survey......Page 14
2 Edge-Preserving Smoothing and the Assessment of Point Process Models for GATE Rainfall Fields......Page 48
3 Likelihood Methods for Diffusions with Jumps......Page 80
4 Efficient Estimating Equations for Nonparametric Filtered Models......Page 120
5 Nonparametric Estimation of Trends in Linear Stochastic Systems......Page 156
6 Weak Convergence of Two-Sided Stochastic Integrals, with an Application to Models for Left Truncated Survival Data......Page 180
7 Asymptotic Theory of Weighted Maximum Likelihood Estimation for Growth Models......Page 196
8 Markov Chain Models for Type-Token Relationships......Page 222
9 A State-Space Approach to Transfer-Function Modeling......Page 246
10 Shrinkage Estimation for a Dynamic Input-Output Linear Model......Page 262
11 Maximum Probability Estimation for an Autoregressive Process......Page 280
Index......Page 284

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