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Gaussian and Non-Gaussian Linear Time Series and Random Fields

questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gaussian models and deals;

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Non-Gaussian Autoregressive-Type Time Series

This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates;

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Non-Gaussian Random Vibration Fatigue Analysis and Accelerated Test

This book discusses the theory, method and application of non-Gaussian random vibration fatigue analysis and test. The main contents;

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Stochastic Analysis for Gaussian Random Processes and Fields

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic;

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Stochastic Analysis for Gaussian Random Processes and Fields

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic;

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Random Fields for Spatial Data Modeling

. Other chapters focus on non-Gaussian random fields and stochastic simulation methods. The book also presents results based on the author's;

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Stable Non-Gaussian Random Processes

This book presents similarity between Gaussian and non-Gaussian stable multivariate distributions and introduces the one-dimensional stable;

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Some Random Series of Functions

Hilbert spaces, random Taylor or Fourier series, Brownian motion and other Gaussian processes, plus certain types of random sets and measures. The;

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Multilevel and Longitudinal Modeling Using Stata, Volumes I and II, Third Edition

mixed because they allow fixed and random effects, and they are generalized because they are appropriate for continuous Gaussian responses as;

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Statistical Modeling Using Local Gaussian Approximation

most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation;

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Stochastic Models for Time Series

turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory;

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Introduction to Time Series Modeling with Applications in R

the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle;

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Bayesian Estimation and Tracking

-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The;

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Statistical Inference in Financial and Insurance Mathematics with R

, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments;

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Simulation of Stochastic Processes with Given Accuracy and Reliability

reliability in functional spaces. The authors explore analysis of the theory of Sub-Gaussian (including Gaussian one) and Square Gaussian random;

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Weakly Stationary Random Fields, Invariant Subspaces and Applications

The first book to examine weakly stationary random fields and their connections with invariant subspaces (an area associated with;

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Weakly Stationary Random Fields, Invariant Subspaces and Applications

The first book to examine weakly stationary random fields and their connections with invariant subspaces (an area associated with;

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Level Sets and Extrema of Random Processes and Fields

. Level Sets and Extrema of Random Processes and Fields is an excellent book for courses on probability theory, spatial statistics, Gaussian;

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Gaussian Markov Random Fields

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to;

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Gaussian Markov Random Fields

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to;

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Gaussian Hilbert Spaces

This book treats the very special and fundamental mathematical properties that hold for a family of Gaussian (or normal) random variables;

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Gaussian Hilbert Spaces

This book treats the very special and fundamental mathematical properties that hold for a family of Gaussian (or normal) random variables;

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Topological Complexity of Smooth Random Functions

, typically at a more intuitive and less technical level. They also cover some more recent material relating to random algebraic topology and;

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Stochastic Dynamics and Control

processes Random vibrations Structural reliability and fatigue, Non-Gaussian fatigue Monte Carlo methods Stochastic calculus;

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Stochastic Optimal Control of Structures

by engineering excitations in the nature of non-stationary and non-Gaussian processes. Further, it establishes the theory of and methods for;

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Stochastic Optimal Control of Structures

by engineering excitations in the nature of non-stationary and non-Gaussian processes. Further, it establishes the theory of and methods for;

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Statistical Mechanics & Random Walks

; subordinated Gaussian processes; random walk models in biophysical science; non-equilibrium dynamics and diffusion processes; global random walk;

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