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Most statistical applications involve computational work with data stored on a computer. The mechanics of interaction with the data is a function of the sta tistical computing environment. This application guide is intended for slightly experienced statisticians in computer-aided data analysis who desire to learn advanced applications in various fields of statistics. The prerequisities for XploRe-the statistic computing environment-are an introductory course in statistics or mathematics. This book is designed as an e-book which means that the text contained in here is also available as an integrated document in HTML and PDF format. The reader of this application guide should therefore be familiar with the basics of Acrobat Reader and of HTML browsers in order to profit from direct computing possibilities within this document. The quantlets presented here may be used together with the academic edi tion of XploRe (http://www.i-xplore.de) or via the XploRe Quantlet Client (XQC) on http://www.xplore-stat.de. The book comes together with a CD Rom that contains the XploRe Quantlet Server (XQS) and the full Auto Pilot Support System (APSS). With this e-book bundle one may directly try the application without being dependent on a specific software version. The quantlets described in the book can be accessed via the links included All executable quantlets are denoted by the symbol . Some in the text.
Inhaltsverzeichnis
I Regression Models.- 1 Quantile Regression.- 2 Least Trimmed Squares.- 3 Errors-in-Variables Models.- 4 Simultaneuos-Equations Models.- 5 Hazard Regression.- 6 Generalized Partial Linear Models.- 7 Generalized Additive Models.- II Data Exploration.- 8 Growth Regression and Counterfactual Income Dynamics.- 9 Cluster Analysis.- 10 Classification and Regression Trees.- 11 DPLS: Partial Least Squares Program.- 12 Uncovered Interest Parity.- 13 Correspondence Analysis.- III Dynamic Statistical Systems.- 14 Long-Memory Analysis.- 15 ExploRing Persistence in Financial Time Series.- 16 Flexible Time Series Analysis.- 17 Multiple Time Series Analysis.- 18 Robust Kalman Filtering.
Über den Autor / die Autorin
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.
