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Model Selection & Model Averaging

have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize;

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Bayesian Model Selection and Statistical Modeling

Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model;

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Model Averaging

This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means;

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Bayesian Econometrics

considered include an endogenous selection ordered probit model, a censored treatment-response model, equilibrium job search models and various other;

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Bayesian Econometrics

considered include an endogenous selection ordered probit model, a censored treatment-response model, equilibrium job search models and various other;

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Regression And Time Series Model Selection

selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including;

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Selection Models for Nonignorable Missing Data

models for nonignorable missing data. The theory of selection models is described and illustrated by examples. Maximum Likelihood as well as;

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Comparative Study on Model Selection Criteria in Gee

Model (or variable) selection is an essential part of any statistical analysis. Since a GEE model does not specify a likelihood structure;

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Model Selection and Multimodel Inference

introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that;

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Model Selection and Multimodel Inference

introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that;

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Bayesian Model Comparison

issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian;

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Bayesian Model Selection and Statistical Modeling

different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors.;

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Support Vector Data Description

model in logicalsituations. We designed experimental setup for datacollection, developed unique class of model includingvariable selection, and;

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A Hybrid Feature Selection Model For Genome Wide Association Studies

dimensional data and to be able to find the nonlinear relations, data mining approaches are needed. In this work, a hybrid feature selection model;

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Concentration Fluctuations and Averaging Time in Vapor Clouds

statistics; and formulating an operation model for concentration fluctuations based on the current state of knowledge of dispersion processes.;

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Reproducible Econometrics Using R

approaches for dealing with model uncertainty, namely model selection and model averaging. Throughout the book there is an emphasis on the benefits;

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Model Discrimination for Nonlinear Regression Models

Borowiak (math, U. of Akron) discusses model discrimination based upon incorrect selection probability, presents diagnostic statistics and;

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Differential Equations with Maxima

-adequately model real-world processes whose present state significantly depends on the maximum value of the state on a past time interval. More and;

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Differential Equations with Maxima

-adequately model real-world processes whose present state significantly depends on the maximum value of the state on a past time interval. More and;

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Nucleon Optical Model,the

of a comprehensive introduction to the subject and a selection of papers by the author describing the optical model in detail. It contains full;

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Group Selection in Predator-Prey Communities. (MPB-9)

to be too slow ever to oppose individual selection successfully. In this book Michael Gilpin presents a model, based on predator-prey;

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Model Selection and Error Estimation in a Nutshell

practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by;

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A Dynamical Perspective on the 4 Model

This book presents a careful selection of the most important developments of the phi^4 model, offering a judicious summary of this model;

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Bayesian Analysis for Population Ecology

of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data;

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Bayesian Analysis for Population Ecology

of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data;

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Fence Methods, The

and complex model selection problems with practical considerations. The idea involves a procedure to isolate a subgroup of what are known as;

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Mixture Models and Applications

interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the;

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