Statistical Inference in Stochastic Processes

Herausgeber: Prabhu, N U

• Broschiertes Buch

Produktbeschreibung

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

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 291
• Erscheinungstermin: 17. Oktober 2019
• Englisch
• Abmessung: 226mm x 152mm x 15mm
• Gewicht: 499g
• ISBN-13: 9780367403072
• ISBN-10: 0367403072
• Artikelnr.: 58059932

Autorenporträt

N. U. Prabhu, I. V. Basawa

Inhaltsangabe

Preface
1 Statistical Models and Methods in Image Analysis: A Survey /Alan F. Karr
2 Edge
Preserving Smoothing and the Assessment of Point Process Models for GATE Rainfall Fields /Colin R. Goodall and Michael J. Phelan
3 Likelihood Methods for Diffusions with Jumps /Michael SVJrensen
4 Efficient Estimating Equations for Nonparametric Filtered Models /P. E. Greenwood and W. Wefelmeyer
5 Nonparametric Estimation of Trends in Linear Stochastic Systems /Jan W. McKeague and Tiziano Tofani
6 Weak Convergence of Two
Sided Stochastic Integrals, with an Application to Models for Left Truncated Survival Data /Michael Davidsen and Martin Jacobsen
7 Asymptotic Theory of Weighted Maximum Likelihood Estimation for Growth Models /B. L. S. Prakasa Rao
8 Markov Chain Models for Type
Token Relationships /D. J. Daley, J. M. Gani, and D. A. Ratkowsky
9 A State
Space Approach to Transfer
Function Modeling /P. J. Brockwell, R. A. Davis, and H. Salehi
10 Shrinkage Estimation for a Dynamic Input
Output Linear Model /Young
Won Kim, David M. Nickerson, and I. V. Basawa
11 Maximum Probability Estimation for an Autoregressive Process /Lionel Weiss
Index.