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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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Basic Data Analysis for Time Series with R

of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these;

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Computer Intensive Statistical Methods

This book focuses on computer intensive statistical methods, such as validation, model selection, and bootstrap, that help overcome;

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Computer Intensive Statistical Methods

This book focuses on computer intensive statistical methods, such as validation, model selection, and bootstrap, that help overcome;

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Interrupted Time Series Analysis

Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. It;

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Regression Models For Time Series Analysis

is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis;

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Dealing with Endogeneity in Regression Models with Dynamic Coefficients

throughout the book. While the approach has been extensively applied to the sample selection models and disequilibrium models in the micro;

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Cambridge Series in Statistical and Probabilistic Mathematics

models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book;

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Regression Models as a Tool in Medical Research

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the;

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Regression Models As A Tool In Medical Research

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the;

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Applied Regression Modeling

, multilevel models, Bayesian modeling, and time series forecasting. Illustrations, graphs, and computer software output appear throughout the book to;

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A Course on Statistics for Finance

author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance. The;

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A Course on Statistics for Finance

author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance;

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Modeling in Mathematics

representing noisy sounds with short-time sinusoids, robust Bayesian regularized estimation based on regression model and robust quadratic regression;

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Econometric Model Specification

modeling and inference. This book consists of two parts. The first part discusses consistent tests of functional form of regression and conditional;

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Regression Models

likelihood estimation and the interpretation of parameters. The author next discusses the basic sample selection model and the truncated regression;

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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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Wavelet Neural Networks

chaotic time series prediction An extensive introduction to neural networks that begins with regression models and builds to more complex;

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Linear Models and TimeSeries Analysis

A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA;

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Reproducible Econometrics Using R

in reproducible econometric research. Jeffrey S. Racine provides a step-by-step approach, and covers five sets of topics, i) linear time series models;

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Introduction to Time Series Modeling

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates;

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Confidence Intervals in Generalized Regression Models

regression model, generalized linear model (GLIM), logistic regression model, Poisson regression model, multinomial regression model, and Cox;

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Confidence Intervals in Generalized Regression Models

regression model, generalized linear model (GLIM), logistic regression model, Poisson regression model, multinomial regression model, and Cox;

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Hidden Markov Models for Time Series

discusses how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and;

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Gaussian Process Regression Analysis for Functional Data

discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new;

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Introduction To Time Series Analysis And Forecasting

forecasting topics, including: Regression-based methods, heuristic smoothing methods, and general time series models Basic statistical tools used;

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First Hitting Time Regression Models

first hitting time regression models (or threshold regression) presented here represent observed events as the outcome of an underlying;

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