W. Härdle, Z. Hlavka, S. Klinke

XploRe® - Application Guide (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 525
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9783642572920
• Artikelnr.: 53101758

Autorenporträt

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.

Inhaltsangabe

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.