Xplore: An Interactive Statistical Computing Environment

ISBN-13: 9781461286998 / Angielski / Miękka / 2011 / 387 str.

Wolfgang Hardle; Sigbert Klinke; Berwin A. Turlach

Opis

This book describes an interactive statistical computing environment called 1 XploRe. As the name suggests, support for exploratory statistical analysis is given by a variety of computational tools. XploRe is a matrix-oriented statistical language with a comprehensive set of basic statistical operations that provides highly interactive graphics, as well as a programming environ- ment for user-written macros; it offers hard-wired smoothing procedures for effective high-dimensional data analysis. Its highly dynamic graphic capa- bilities make it possible to construct student-level front ends for teaching basic elements of statistics. Hot keys make it an easy-to-use computing environment for statistical analysis. The primary objective of this book is to show how the XploRe system can be used as an effective computing environment for a large number of statistical tasks. The computing tasks we consider range from basic data matrix manipulations to interactive customizing of graphs and dynamic fit- ting of high-dimensional statistical models. The XploRe language is similar to other statistical languages and offers an interactive help system that can be extended to user-written algorithms. The language is intuitive and read- ers with access to other systems can, without major difficulty, reproduce the examples presented here and use them as a basis for further investigation.

Spis treści

I A Beginner’s Course.- 1 Un Amuse-Gueule.- 2 An XploRe Tutorial.- 2.1 Getting Started.- 2.2 Two-Dimensional Plots.- 2.3 Creating a Macro.- 2.4 The Interactive Help System.- 2.5 Three-Dimensional Plots.- 2.6 Reading and Writing Data.- 3 The Integrated Working Environment.- 3.1 Introduction.- 3.2 The Editor.- 3.3 How to Run and Debug a Program.- 3.4 The Context-Sensitive Help System.- 3.5 Graphic Tools in XploRe.- 3.6 Multiple Window Displays.- 3.7 Manipulating Windows.- 3.8 How to Print a Graphic.- 3.9 The Static 2-D Window.- 3.10 The Dynamic 3-D Window.- 3.11 The Boxplot Window.- 3.12 The Flury Faces Window.- 3.13 How to Use and Create Libraries.- II XploRe in Use.- 4 Graphical Aids for Statistical Data Analysis.- 4.1 Introduction.- 4.2 First Pictures.- 4.3 Stratifications.- 5 Density and Regression Smoothing.- 5.1 Introduction.- 5.2 Density Estimation.- 5.3 Nadaraya-Watson Nonparametric Regression.- 5.4 Local Polynomial Fitting.- 5.5 Estimation of Regression Derivatives.- 5.6 Variable Amount of Smoothing.- 6 Bandwidth Selection in Density Estimation.- 6.1 Introduction.- 6.2 Choosing the Smoothing Parameter.- 6.3 Density Estimation in Action.- 7 Interactive Graphics for Teaching Simple Statistics.- 7.1 Introduction.- 7.2 The General Structure of the Teachware System.- 7.3 Description of the Main Macros in the Module.- 7.4 Details on XploRe Language.- 7.5 Conclusions.- 8 XClust: Clustering in an Interactive Way.- 8.1 Introduction.- 8.2 Cluster Analysis and Classification.- 8.3 The K-Means Method in XploRe.- 8.4 The Adaptive K-Means Method.- 8.5 The Hierarchical Cluster Analysis.- 8.6 Classification and Regression Tree (CART).- 8.7 The Investigation of the Stability of Adaptive Weights.- 9 Exploratory Projection Pursuit.- 9.1 Introduction.- 9.2 Projection Pursuit Indices.- 9.3 The Index Functions in Practice.- 9.4 What Will Be Found on Real Data?.- 9.5 Computational Aspects.- 9.6 The PPEXPL Macro.- 10 Generalized Linear Models.- 10.1 Introduction.- 10.2 Some Theory.- 10.3 Implementation.- 10.4 Example for a Gamma Model.- 10.5 Negative Binomial Regression.- 10.6 GLM Extensions: Parametric Survival Models.- 11 Additive Modeling.- 11.1 Introduction.- 11.2 Generalized Additive Models.- 11.3 Sliced Inverse Regression.- 11.4 Average Derivative Estimation.- 12 Comparing Parametric and Semiparametric Binary Response Models.- 12.1 Introduction.- 12.2 The Data.- 12.3 Parametric and Semiparametric Binary Response Models.- 12.4 Estimation.- 12.5 Testing the Adequacy of the Logit Link.- 12.6 Summary and Conclusions.- 13 Approximative Methods for Regression Models with Errors in Covariates.- 13.1 Introduction.- 13.2 Regression Calibration.- 13.3 Simulation and Extrapolation (SIMEX).- 14 Nonlinear Time Series Analysis.- 14.1 Introduction.- 14.2 Parametric Approaches.- 14.3 Nonparainetric Approach.- 14.4 Nonlinearity Tests.- 14.5 Nonlinear Prediction.- 15 Un Digestif.- 15.1 A Whole Bunch of Pictures.- 15.2 Interactive Contouring.- A Questions to XploRe.- B Language Reference.- B.1 Operators.- B.2 Flow Control.- B.3 Commands.- References.- Author Index.

O autorze

Wolfgang Härdle is a professor of statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. the Centre for Applied Statistics and Economics. He teaches quantitative finance and semiparametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected ISI member and advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan.