Del i serien Probability: Pure and Applied

Statistical Inference in Stochastic Processes

Häftad, Engelska, 2019

1 249 kr

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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

Produktinformation

Utgivningsdatum2019-10-17
Mått152 x 229 x 16 mm
Vikt453 g
FormatHäftad
SpråkEngelska
SerieProbability: Pure and Applied
Antal sidor291
FörlagTaylor & Francis Ltd
ISBN9780367403072

Tillhör följande kategorier

Matematisk statistik inom Naturvetenskap och teknik

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.