Cookies helfen uns bei der Bereitstellung unserer Dienste. Durch die Nutzung unserer Dienste erklären Sie sich damit einverstanden, dass wir Cookies setzen.

Cuvillier Verlag

30 Jahre Kompetenz im wissenschaftlichen Publizieren
Internationaler Fachverlag für Wissenschaft und Wirtschaft

Cuvillier Verlag

De En Es
Estimating and Correcting the Effects of Model Selection Uncertainty

Printausgabe
EUR 32,00 EUR 30,40

E-Book
EUR 0,00

Estimating and Correcting the Effects of Model Selection Uncertainty

Georges Nguefack Tsague (Autor)

Vorschau

Inhaltsverzeichnis, Datei (75 KB)
Leseprobe, Datei (110 KB)

ISBN-13 (Printausgabe) 3865378447
ISBN-13 (Printausgabe) 9783865378446
ISBN-13 (E-Book) 9783736918443
Sprache Englisch
Seitenanzahl 182
Umschlagkaschierung glänzend
Auflage 1
Band 0
Erscheinungsort Göttingen
Promotionsort Göttingen
Erscheinungsdatum 24.04.2006
Allgemeine Einordnung Dissertation
Fachbereiche Land- und Agrarwissenschaften
Schlagwörter model selection, model uncertainty, model selection probability, post-model-selection estimation, inference, Bayesian model averaging, frequentist model averaging, Akaike weights, bootstrap
Beschreibung

Most applied statistical analyses are carried out under model uncertainty, meaning that the model which generated the observations is unknown, and so the data are first used to select one of a set of plausible models by means of some selection criterion. Generally the data are then used to make inferences about some quantity of interest, ignoring model selection uncertainty, i.e. the fact that the selection step was carried out using the same data, and despite the known fact that this leads to invalid inferences. This thesis investigates several issues relating to this problem from both the Bayesian and the frequentist points of view, and offers new suggestions for dealing with it.
We examine Bayesian model averaging (BMA) and point out that its frequentist performance is not always well-defined because, in some cases, it is unclear whether BMA methodology is truly Bayesian. We illustrate the point with a “fully Bayesian model averaging" that is applicable when the quantity of interest is parametric.