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The EM Algorithm and Related Statistical Models

Exploring the application and formulation of the EM algorithm, The EM Algorithm and Related Statistical Models offers a valuable method for;

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Methods of Statistical Model Estimation

maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R;

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Methods of Statistical Model Estimation

maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R;

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The EM Algorithm and Extensions

successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical;

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Finite Mixture Models

statistical contexts. Major issues discussed in this book include identifiability problems, actual fitting of finite mixtures through use of the EM;

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Incomplete Categorical Data Design

Response Techniques for Sensitive Questions in Surveys is the first book on non-randomized response designs and statistical analysis methods. The;

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Incomplete Categorical Data Design

Response Techniques for Sensitive Questions in Surveys is the first book on non-randomized response designs and statistical analysis methods. The;

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In All Likelihood

covers many modern topics such as generalized linear models and mixed models, non parametric smoothing, robustness, the EM algorithm and;

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Stochastic Models, Statistics and Their Applications

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed;

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Statistical Models and Methods for Reliability and Survival Analysis

. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models;

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Random Processes For Engineers

* Kalman and Wiener filtering * Hidden Markov models for statistical inference * The estimation maximization (EM) algorithm * An introduction to;

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Perturbations, Optimization, and Statistics

describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments;

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Statistical Mechanics & Random Walks

In this book, the authors gather and present topical research in the study of statistical mechanics and random walk principles and;

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Regression Models For Time Series Analysis

Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm Prediction and interpolation Stationary processes;

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Stochastic Modeling of Scientific Data

in complicated models and the forward backward algorithm for analyzing hidden Markov models is presented. The strength of this text lies in the use;

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Stochastic Modeling of Scientific Data

in complicated models and the forward backward algorithm for analyzing hidden Markov models is presented. The strength of this text lies in the use;

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Elements of Statistical Computing

over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter;

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Statistical Factor Analysis And Related Methods

Statistical Factor Analysis and Related Methods Theory and Applications In bridging the gap between the mathematical and statistical theory;

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Discrete Choice Methods with Simulation

. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models;

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Discrete Choice Methods with Simulation

. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models;

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Statistical Methods For Speech Recogniti

statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria;

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Cure Models

Covers statistical concepts, methods and applications related to all aspects of survival analysis with cure, integration of insights gained;

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Cure Models

Covers statistical concepts, methods and applications related to all aspects of survival analysis with cure, integration of insights gained;

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Latent Class Analysis of Survey Error

: * Measurement error modeling forcategorical data * The Hui-Walter model and othermethods for two indicators * The EM algorithm and its role;

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Probability Random Processes & Statistic

, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and Ito;

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Applied Regression Analysis and Generalized Linear Models

Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the;

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Matrix Tricks for Linear Statistical Models

statistical analysis). In matrix algebra, there are handy, sometimes even very simple tricks which simplify and clarify the treatment of a problem;

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