XploRe (R) - Application Guide

; Hlavka Zdenek ; Sigbert Klinke ; Z. Hlavka

XploRe (R) - Application Guide

This book offers a detailed application guide to XploRe - an interactive statistical computing environment. As a guide it contains case studies of real data analysis situations. Les mer
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(Paperback) Fri frakt!
Leveringstid: Sendes innen 14 dager

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XploRe (R) - Application Guide

This book offers a detailed application guide to XploRe - an interactive statistical computing environment. As a guide it contains case studies of real data analysis situations. It helps the beginner in statistical data analysis to learn how XploRe works in real life applications. Many examples from practice are discussed and analysed in full length. Great emphasis is put on a graphic based understanding of the data interrelations. The case studies include: Survival modelling with Cox's proportional hazard regression, Vitamin C data analysis with Quantile Regression, and many others.

I Regression Models.- 1 Quantile Regression.- 1.1 Introduction.- 1.2 Quantile Regression.- 1.2.1 Definitions.- 1.2.2 Computation.- 1.3 Essential Properties.- 1.3.1 Equivariance.- 1.3.2 Invariance to Transformations.- 1.3.3 Robustness.- 1.4 Inference.- 1.4.1 Main Asymptotic Results.- 1.4.2 Wald Test.- 1.4.3 Rank Tests.- 1.5 Description of Quantlets.- 1.5.1 Quantlet rqfit.- 1.5.2 Quantlet rrstest.- 2 Least Trimmed Squares.- 2.1 Robust Regression.- 2.1.1 Introduction.- 2.1.2 High Breakdown point Estimators.- 2.2 Least Trimmed Squares.- 2.2.1 Definition.- 2.2.2 Computation.- 2.3 Supplementary Remarks.- 2.3.1 Choice of the Trimming Constant.- 2.3.2 LTS as a Diagnostic Tool.- 2.3.3 High Subsample Sensitivity.- 3 Errors-in-Variables Models.- 3.1 Linear EIV Models.- 3.1.1 A Single Explanatory Variable.- 3.1.2 Vector of Explanatory Variables.- 3.2 Nonlinear EIV Models.- 3.2.1 Regression Calibration.- 3.2.2 Simulation Extrapolation.- 3.3 Partially Linear EIV Models.- 3.3.1 The Variance of Error Known.- 3.3.2 The Variance of Error Unknown.- 3.3.3 XploRe Calculation and Practical Data.- 4 Simultaneuos-Equations Models.- 4.1 Introduction.- 4.2 Estimation.- 4.2.1 Identification.- 4.2.2 Some Notation.- 4.2.3 Two-Stage Least Squares.- 4.2.4 Three-Stage Least Squares.- 4.2.5 Computation.- 4.3 Application: Money-Demand.- 5 Hazard Regression.- 5.1 Data Structure.- 5.2 Kaplan-Meier Estimates.- 5.3 The Cox Proportional Hazards Model.- 5.3.1 Estimating the Regression Coefficients.- 5.3.2 Estimating the Hazard and Survival Functions.- 5.3.3 Hypothesis Testing.- 5.3.4 Example: Length of Stay in Nursing Homes.- 6 Generalized Partial Linear Models.- 6.1 Estimating GPLMs.- 6.1.1 Models.- 6.1.2 Semiparametric Likelihood.- 6.2 Data Preparation.- 6.2.1 General.- 6.2.2 Example.- 6.3 Computing GPLM Estimates.- 6.3.1 Estimation.- 6.3.2 Estimation in Expert Mode.- 6.4 Options.- 6.4.1 Setting Options.- 6.4.2 Grid and Starting Values.- 6.4.3 Weights and Offsets.- 6.4.4 Control Parameters.- 6.4.5 Model Parameters.- 6.4.6 Specification Test.- 6.4.7 Output Modification.- 6.5 Statistical Evaluation and Presentation.- 6.5.1 Statistical Characteristics.- 6.5.2 Output Display.- 6.5.3 Model selection.- 7 Generalized Additive Models.- 7.1 Brief Theory.- 7.1.1 Models.- 7.1.2 Marginal Integration.- 7.1.3 Backfitting.- 7.1.4 Orthogonal Series.- 7.2 Data Preparation.- 7.3 Noninteractive Quantlets for Estimation.- 7.3.1 Estimating an AM.- 7.3.2 Estimating an APLM.- 7.3.3 Estimating an AM and APLM.- 7.3.4 Estimating a GAM.- 7.3.5 Estimating a GAPLM.- 7.3.6 Estimating Bivariate Marginal Influence.- 7.3.7 Estimating an AM with Interaction Terms.- 7.3.8 Estimating an AM Using Marginal Integration.- 7.4 Interactive Quantlet GAMFIT.- 7.5 How to Append Optional Parameters.- 7.6 Noninteractive Quantlets for Testing.- 7.6.1 Component Analysis in APL Models.- 7.6.2 Testing for Interaction.- 7.6.3 Testing for Interaction.- 7.7 Odds and Ends.- 7.7.1 Special Properties of GAM Quantlib Quantlets.- 7.7.2 Estimation on Principal Component by PCAD.- 7.8 Application for Real Data.- II Data Exploration.- 8 Growth Regression and Counterfactual Income Dynamics.- 8.1 A Linear Convergence Equation.- 8.2 Counterfactual Income Dynamics.- 8.2.1 Sources of the Growth Differential With Respect to a Hypothetical Average Economy.- 8.2.2 Univariate Kernel Density Estimation and Bandwidth Selection.- 8.2.3 Mu

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