Statistical Inference in Stochastic Processes: 1st Edition (Paperback) book cover

Statistical Inference in Stochastic Processes

1st Edition

Edited by N.U. Prabhu

CRC Press

288 pages

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Covering both theory and applications, this collection of eleven contributed papers surveys the role of probabilistic models and statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters that determine the drift and the jump mechanism of a di

Table of Contents

1. Statistical Models and Methods in Image Analysis: A Survey. 2. Edge-Preserving Smoothing and the Assessment of Point Process Models for GATE Rainfall Fields. 3. Likelihood Methods for Diffusions with Jumps. 4. Efficient Estimating Equations for Nonparametric Filtered Models. 5. Nonparametric Estimation of Trends in Linear Stochastic Systems. 6. Weak Convergence of Two-Sided Stochastic Integrals, with an Application to Models for Left Truncated Survival Data. 7. Asymptotic Theory of Weighted Maximum Likelihood Estimation for Growth Models. 8. Markov Chain Models for Type-Token Relationships. 9. A State-Space Approach to Transfer-Function Modelling. 10. Shrinkage Estimation for a Dynamic Input-Output Linear Model. 11. Maximum Probability Estimation for an Autoregressive Process

About the Editor

N. U. Prabhu, I. V. Basawa

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General