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Copula Based Markov Models for Time Series

This book provides statistical methodologies for time series data, focusing on copula-based Markov chain models for serially correlated;

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Hidden Markov and Other Models for Discrete-Valued Time Series

developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time;

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Analysis of Survival Data with Dependent Censoring

This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily;

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Margining Systems For An Energy Exchange

few years exchange trading volumes in commodity trading and in particular for energy related products have grown massively. At the same time;

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Hidden Markov Models for Time Series

>Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types;

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Non-Linear Time Series

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book;

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Hidden Markov Models for Time Series

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models;

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Hidden Markov Models for Time Series

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models;

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Markov Chains

in marketing management. The authors present an approach based on Markov decision processes for the calculation of CLV using real data. Chapter 6;

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Dependence Modeling with Copulas

dependence and tail properties to assist in copula model selection. The author shows how numerical methods and algorithms for inference and;

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Dependence Modeling with Copulas

dependence and tail properties to assist in copula model selection. The author shows how numerical methods and algorithms for inference and;

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Contributions to Static and Time-Varying Copula-Based Modeling of Multivariate Association

theory of static and time-varying stochastic models for multivariate association based on the concept of copulas. These functions enable a;

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Mixture and Hidden Markov Models with R

This book discusses mixture and hidden Markov models for modeling behavioral data. Mixture and hidden Markov models are statistical models;

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Time Series

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and;

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Markov Models for Pattern Recognition

. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the;

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Time Series

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling;

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Statistical Methods and Modeling of Seismogenesis

multi-state system approach, earthquake simulators, post-seismic activity models, time series Markov models with regression, scaling properties;

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Recent Advances in Estimating Nonlinear Models

number of series exhibit nonlinear dynamics as opposed to the alternative--linear dynamics. Incorporating these concepts involves deriving and;

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Introduction to Bayesian Estimation and Copula Models of Dependence

copula modeling, allowing readers to gain insight for their own applied research and studies Separate reference lists within each chapter and end;

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Stochastic Models for Time Series

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools;

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Option Pricing and Estimation of Financial Models with R

Presents inference and simulation of stochastic process in the field of model calibration for financial times series modelled by continuous;

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American-Type Options

The book gives a systematical presentation of stochastic approximation methods for discrete time Markov price processes. Advanced methods;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov;

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State-Space Models

theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series;

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State-Space Models

theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series;

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Bayesian Econometric Methods

have also added many new exercises related to Gibbs sampling and Markov Chain Monte Carlo (MCMC) methods. The text includes regression-based;

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Bayesian Econometric Methods

have also added many new exercises related to Gibbs sampling and Markov Chain Monte Carlo (MCMC) methods. The text includes regression-based;

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