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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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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 Statistical Communication Theory

purpose of the book is to introduce Non-Gaussian statistical communication theory and demonstrate how the theory improves probabilistic model. The;

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The Inverse Gaussian Distribution: Theory

This monograph is a compilation of research on the inverse Gaussian distribution. It emphasizes the presentation of the statistical;

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The Inverse Gaussian Distribution: Theory

This monograph is a compilation of research on the inverse Gaussian distribution. It emphasizes the presentation of the statistical;

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Gaussian Process Regression Analysis for Functional Data

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis;

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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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Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the;

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Theory and Statistical Applications of Stochastic Processes

This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes. It;

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Introduction to General and Generalized Linear Models

Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models;

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Introduction to a Renormalisation Group Method

begins with critical behaviour and its basic discussion in statistical mechanics models, and subsequently explores perturbative and non;

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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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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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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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Statistical Portfolio Estimation

processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a;

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Statistical Portfolio Estimation

processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a;

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Asymptotic Statistics

different mathematical preferences of the reader. One reader may focus on the statistical theory, and thus on the chapters about Gaussian shift;

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VaR Methodology for Non-Gaussian Finance

non-Gaussian extensions of VaR and applies several basic statistical theories to extend classical results of VaR techniques such as the NP;

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Mathematical Foundations of Infinite-Dimensional Statistical Models

In nonparametric and high-dimensional statistical models, the classical Gauss-Fisher-Le Cam theory of the optimality of maximum likelihood;

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Bootstrap Techniques for Signal Processing

foundations of the bootstrap, its properties, its strengths and its limitations. The authors focus on bootstrap signal detection in Gaussian and non;

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Bootstrap Techniques for Signal Processing

foundations of the bootstrap, its properties, its strengths and its limitations. The authors focus on bootstrap signal detection in Gaussian and non;

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Statistical Inference for Financial Engineering

analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear;

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On the Intrinsic Geometry of Instanton Vacua

. Physically, for both the stationary and non-stationary statistical configurations with and without the statistical fluctuations of the gauge and;

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Probability

limiting theorems, Bayesian computational methods, as well as a modern view on sampling and reconstruction of Gaussian and non-Gaussian random;

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Quantum Continuous Variables

addresses the theory of Gaussian states, operations, and dynamics in great depth and breadth, through a novel approach that embraces both the;

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Quantum Continuous Variables: A Primer of Theoretical Methods

addresses the theory of Gaussian states, operations, and dynamics in great depth and breadth, through a novel approach that embraces both the;

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Linear and Generalized Linear Mixed Models and Their Applications

approach to inference about non-Gaussian linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics;

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