Stochastic Analysis for Gaussian Random Processes and Fields

With Applications

von

Mandrekar, Vidyadhar S.Gawarecki, Leszek

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Verkaufsrang16667inMathematik

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Beschreibung

This monograph presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, the authors study Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs). They explain how covariances are related to RKHSs and examine the Bayes' formula, the filtering and analytic problem related to fractional Brownian motion, and equivalence and singularity of Gaussian random fields. The book also describes applications in finance and spatial statistics and presents results on Dirichlet forms and associated Markov processes.

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Details

ISBN/GTIN978-1-4987-0781-7

ProduktartBuch

EinbandGebunden

VerlagTaylor and FrancisChapman and Hall/CRC

Erscheinungsdatum23.06.2015

Auflage1. A.

ReiheChapman & Hall/CRC Monographs on Statistics and Applied Probability

Seiten202 Seiten

SpracheEnglisch

MasseBreite 156 mm, Höhe 234 mm

Gewicht950 g

IllustrationenFarb., s/w. Abb.

Artikel-Nr.22225973

KatalogBuchzentrum

Datenquelle-Nr.18146191

WarengruppeMathematik

Weitere Details

Reihe

Covariance Modelling

Multivariate Geostatistical Models

Statistical Inference

Analysis of Survival Data

Density Estimation for Statistics and Data Analysis

Transformation and Weighting in Regression

Analysis of Infectious Disease Data

Analysis of Binary Data

Analysis of Repeated Measures

Inspection Errors for Attributes in Quality Control

Queues

Nonparametric Regression and Generalized Linear Models

Differential Geometry and Statistics

Markov Models & Optimization

Networks and Chaos - Statistical and Probabilistic Aspects

Practical Risk Theory for Actuaries

Number-Theoretic Methods in Statistics

Predictive Inference

Inference and Asymptotics

Kernel Smoothing

An Introduction to the Bootstrap

Statistics for Long-Memory Processes

Biplots

Cyclic and Computer Generated Designs

Nonlinear Models for Repeated Measurement Data

Distribution-Free Statistical Methods, Second Edition

Local Polynomial Modelling and Its Applications

Time Series Models

Multivariate Dependencies

Analysis of Incomplete Multivariate Data

Variance Components

Retrial Queues

Statistical Evidence

Stochastic Geometry

Classification

Statistical Aspects of BSE and vCJD

Set-Indexed Martingales

Algebraic Statistics

The Theory of the Design of Experiments

Analysis of Time Series Structure

Empirical Likelihood

Accelerated Life Models

Statistics in the 21st Century

Components of Variance

Subset Selection in Regression

Statistical Inference and Simulation for Spatial Point Processes

Diagnostic Checks in Time Series

Stereology for Statisticians

Gaussian Markov Random Fields

Measurement Error in Nonlinear Models

Nonlinear Time Series

Missing Data in Longitudinal Studies

Antedependence Models for Longitudinal Data

Circular and Linear Regression

Expansions and Asymptotics for Statistics

Simultaneous Inference in Regression

Robust Nonparametric Statistical Methods

Statistical Inference

Extreme Value Methods with Applications to Finance

Smoothing Splines

Maximum Likelihood Estimation for Sample Surveys

Constrained Principal Component Analysis and Related Techniques

Statistical Analysis of Spatial and Spatio-Temporal Point Patterns

Analysis of Variance for Functional Data

Sequential Analysis

Quasi-Least Squares Regression

Robust Cluster Analysis and Variable Selection

Hierarchical Modeling and Analysis for Spatial Data

Design and Analysis of Cross-Over Trials

Data Analysis and Approximate Models

Inferential Models

Models for Dependent Time Series

Pareto Distributions