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Transformation and Weighting in Regression

research. While the main focus of the book in on data transformation and weighting, it also draws upon ideas from diverse fields such as;

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Transformation and Weighting in Regression

research. While the main focus of the book in on data transformation and weighting, it also draws upon ideas from diverse fields such as;

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Regression Analysis by Example

of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the;

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Weighting Methods and their Effects on Multi Criteria Decision Making Model Outc

-making were used and these three databases were searched for different popular weighting methods for a period of 13 years (2000-2012). The book;

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Analysis of Incidence Rates

standardization, inverse-variance weighting, and Mantel-Haenszel methods Describes Poisson regression methods for adjusted rate ratios and rate;

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Analysis of Incidence Rates

, including standardization, inverse-variance weighting, and Mantel-Haenszel methods Describes Poisson regression methods for;

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Linear Parameter-Varying Control for Engineering Applications

controller to obtain the desired closed-loop response. The selection of appropriate weighting functions is difficult and sometimes appears arbitrary;

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Interpretability of Computational Intelligence Based Regression Models

, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule;

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Regression Modeling: Methods, Theory, and Computation with SAS

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques;

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Regression Modeling: Methods, Theory, and Computation with SAS

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques;

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Weighting . Zucker, Gabriel, CD

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Impact Evaluation

control trials (RCTs), matching and propensity score matching and weighting, instrumental variable estimation, difference-in-differences;

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Impact Evaluation

control trials (RCTs), matching and propensity score matching and weighting, instrumental variable estimation, difference-in-differences;

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Stop Weighting, Start Living

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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Spatial Statistics

statistics, and inverse distance weighting Geospatial statistics analysis using stepwise regression, ordinary least squares (OLS;

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Data Mining Algorithms

building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality;

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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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Multiattribute Evaluation

identifying and weighting the objectives of the stakeholders in a specific decision.;

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Applied Regression Modeling

model building, including predictor and response variable transformations, qualitative predictors, and regression pitfalls Three fully;

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Introduction to Linear Regression Analysis, 6th Edition

INTRODUCTION TO LINEAR REGRESSION ANALYSIS A comprehensive and current introduction to the fundamentals of regression analysis;

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Semiparametric Regression

ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular;

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Semiparametric Regression

ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular;

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Applied Regression and Modeling

regression analysis with applications, and (3) regression and modeling including the second order models, nonlinear regression, and interaction;

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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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