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Linear Regression
David J. Olive (Gebundene Ausgabe, Englisch)
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Beschreibung
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after… variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response transformations for multiple linear regression or experimental design models. This text is for graduates and undergraduates with a strong mathematical background. The prerequisites for this text are linear algebra and a calculus based course in statistics. Dieses Produkt haben wir gerade leider nicht auf Lager.
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Technische Daten
Erscheinungsdatum
25.04.2017
Sprache
Englisch
EAN
9783319552507
Herausgeber
Springer International Publishing
Sonderedition
Nein
Autor
David J. Olive
Seitenanzahl
494
Auflage
1
Einbandart
Gebundene Ausgabe
Autorenporträt
David Olive is a Professor at Southern Illinois University, Carbondale, IL, USA. His research interests include the development of computationally practical robust multivariate location and dispersion estimators, robust multiple linear regression estimators, and resistant dimension reduction estimators.
Schlagwörter
Regression, Prediction Interval, Bootstrap Confidence Region, Experimental Design, Variable Selection, Multivariate Regression, Response Transformation, Generalized Linear Model
Thema-Inhalt
PBT - Wahrscheinlichkeitsrechnung und Statistik
UFM - Mathematische und statistische Software
Höhe
235 mm
Breite
15.5 cm
Transparenz & Sicherheit
Hersteller: Springer, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH
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