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Non-Nested Regression Models

This book addresses two interrelated problems in economics modelling: non-nested hypothesis testing in econometrics, and regression models;

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Multilevel Analysis

for all models for hierarchical or nested data. This book presents two types of multilevel models: the multilevel regression model; and a;

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Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction

, nested logit and latent class models), survival analysis models, non-parametric techniques (particularly neural networks and recursive;

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Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction

, nested logit and latent class models), survival analysis models, non-parametric techniques (particularly neural networks and recursive;

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Mixed Effects Models & Extensions Ecolog

measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive;

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Ordered Regression Models

Estimate and Interpret Results from Ordered Regression Models Ordered Regression Models: Parallel, Partial, and Non-Parallel;

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Ordered Regression Models

Estimate and Interpret Results from Ordered Regression Models Ordered Regression Models: Parallel, Partial, and Non-Parallel;

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Regression Analysis

The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for;

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Applied Statistical Science III

Use Confidence Regions in Multivariate Calibration Problem; The Likelihood Ratio Test of Non-Nested Linear Regression Models; Exact Power;

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Flexible Bayesian Regression Modelling

; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be;

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Regression Models

introduced and other regression models for non-continuous dependent variables, such as the ordered probit. Concluding with an exploration of some;

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SAS for Mixed Models, Second Edition

, multilocation, and repeated measures models hierarchical models with nested random effects analysis of covariance models spatial correlation models;

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Discrete Choice Modelling and Air Travel Demand

, nested and non-nested likelihood ratio tests, etc. The core chapters of the book are written at a level appropriate for airline practitioners;

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Statistical Models of Characteristics of Metal Vapor Lasers

multiple linear and quasilinear regression and other parametric and non-parametric models for modelling laser efficiency and output power. The best;

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Gaussian Process Regression Analysis for Functional Data

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis;

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Statistical Models For Causal Analysis

This volume provides an overview of statistical models commonly used in the causal analyses of non-experimental data in the social and;

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Simultaneous Inference in Regression

Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;

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Simultaneous Inference in Regression

Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;

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Confidence Intervals in Generalized Regression Models

A Cohesive Approach to Regression Models Confidence Intervals in Generalized Regression Models introduces a unified representation-the;

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Confidence Intervals in Generalized Regression Models

A Cohesive Approach to Regression Models Confidence Intervals in Generalized Regression Models introduces a unified representation-the;

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Regression Models For Time Series Analysis

A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time;

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Applied Regression Modeling

undergraduate statistics course in which regression analysis is a main focus, Applied Regression Modeling also belongs on the bookshelves of non;

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Spatial Regression Analysis Using Eigenvector Spatial Filtering

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation;

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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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Statistical Inference For Models With Multivariate T-Distrib

regression models with non-normal errors with practical real-world examples * Uniquely addresses regression models in Student's t-distributed errors;

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The Coordinate-Free Approach to Linear Models

regression models with non-random predictors in a finite-dimensional setting. This approach is more insightful, more elegant, more direct, and;

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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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