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Stable Non Gaussian Self Similar Processes with Stationary Increments

This book provides a self-contained presentation on the structure of a large class of stable processes, known as self-similar mixed moving;

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Stochastic Analysis & Applications

Increments of a Class of Gaussian Processes; Random Walk Tests & Pseudorandom Number Generators; Increments Theory on Gaussian Processes; The Law;

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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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Non-Gaussian Autoregressive-Type Time Series

-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian;

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Estimates of Stochastic Processes with Stationary Increments and Cointegrated Sequences

processing. This book gives a deep understanding of spectral theory and estimation techniques for stochastic processes with stationary increments. It;

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Gaussian Random Processes

The book deals mainly with three problems involving Gaussian stationary processes. The first problem consists of clarifying the conditions;

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Theory and Statistical Applications of Stochastic Processes

combines classic topics such as construction of stochastic processes, associated filtrations, processes with independent increments, Gaussian;

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Dynamics of Statistical Experiments

the representation of a stationary Gaussian statistical experiment with the Markov property, as a stochastic difference equation solution;

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Discrete Stochastic Processes and Optimal Filtering

Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from;

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Discrete Stochastic Processes and Optimal Filtering

Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from;

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Student's t-Distribution and Related Stochastic Processes

Student's distributions, represented as variance and mean-variance mixtures of multivariate Gaussian distributions with the reciprocal gamma mixing;

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Non-Gaussian Random Vibration Fatigue Analysis and Accelerated Test

include statistical analysis method of non-Gaussian random vibration, modeling and simulation of non-Gaussiannon-stationary random vibration;

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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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Selected Aspects of Fractional Brownian Motion

long-memory property which is in sharp contrast with martingales and Markov processes. FBm has become a popular choice for applications where;

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Selected Aspects of Fractional Brownian Motion

long-memory property which is in sharp contrast with martingales and Markov processes. FBm has become a popular choice for applications where;

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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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Multifractional Stochastic Fields

Fractional Brownian Motion (FBM) is a very classical continuous self-similar Gaussian field with stationary increments. In 1940, some works;

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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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Random Processes By Example

continuous Gaussian processes or to processes with jumps in the limiting regime. Finally, in this volume, readers will find discussions on the;

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Gaussian and Non-Gaussian Linear Time Series and Random Fields

with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted;

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Advanced Signal Processing and Digital Noise Reduction

measures. The concepts of stationary, non-stationary and ergodic processes are introduced in this chapter, and some important classes of random;

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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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Introduction to Time Series Modeling with Applications in R

and non-Gaussian modeling. ... [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space;

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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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