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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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Artificial Intelligence in Civil Engineering

Inference System, Support Vector Machine, Least Square Support Vector Machine, Relevance Vector Machine, Multivariate Adaptive Regression Spline;

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Handbook of Regression Methods

Handbook of Regression Methods concisely covers numerous traditional, contemporary, and nonstandard regression methods. The handbook;

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Economic Applications of Quantile Regression

Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing;

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Economic Applications of Quantile Regression

Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing;

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Linear Models in Statistics

, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical;

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Ecological Inference

ecological regression models and ecological correlation.;

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Stochastic Processes - Inference Theory

fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both;

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Cross-Level Inference

explains why older methods like ecological regression so often fail, and it examines the promising new techniques for cross-level inference;

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Regression: Models, Methods and Applications

regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal;

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The SAGE Handbook of Regression Analysis and Causal Inference

Covering both general and advanced aspects of multivariate methods, this handbook focuses on regression analysis of cross-sectional and;

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Nonlinear Statistical Models

models, multivariate regression models, and simultaneous equation models. Methods are illustrated with worked examples, complete with figures;

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Nonlinear Regression

of current theoretical literature, requiring only familiarity with linear regression methods. The three chapters on central computational questions;

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A Course in Econometrics

form their own critical opinions. A Course in Econometrics thoroughly covers the fundamentals-classical regression and simultaneous;

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Using R for Introductory Econometrics

series regression Pooled cross-sections and panel data Instrumental variables and two-stage least squares Simultaneous equation models Limited;

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Data Analysis Using Regression & Multi

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied;

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Applied Statistical Science II

Contents: Nonparametric Perspectives in Regression Analysis; Record Values of Univariate Distribution; A Transfer Theorem for Multitype;

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Introduction to Spatial Econometrics

Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not;

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Introduction to Spatial Econometrics

Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not;

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Constrained Statistical Inference

comprehend methodological resolutions. Chapter coverage includes:* Population means and isotonic regression* Inequality-constrained tests on normal;

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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data;

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Limit Theorems For Nonlinear Cointegrating Regression

This book provides the limit theorems that can be used in the development of nonlinear cointegrating regression. The topics include weak;

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Probability and Bayesian Modeling

, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies;

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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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