Regularization Methods for Item Response and Paired Comparison Models

Name: Regularization Methods for Item Response and Paired Comparison Models
Brand: Cuvillier
SKU: 9783736991651
Price: 4017.0 INR
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by Gunther Schauberger

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ISBN13: 9783736991651
Binding: Paperback
Subject: N/A
Publisher: Cuvillier
Publisher Imprint: Cuvillier
Publication Date: December 11, 2015
Pages: 218
Original Price: GBP 36.0
Language: English
Edition: N/A
Item Weight: 264 grams
BISAC Subject(s): General

About The Book

A main aspect in psychometric modeling is the measurement of latent traits. This dissertation focuses on two popular methods to analyze latent traits, namely item response methods and paired comparisons. In conventional models for item response (e.g. the Rasch model) or paired comparison data (e.g. the Bradley-Terry model) no covariate information is used. This thesis is concerned with the inclusion of different types of covariates into item response and paired comparison models. The increased flexibility of the proposed models also leads to a higher complexity of the models. Regularization methods prove to be an effective instrument to deal with the increased number of parameters and to differentiate between necessary and unnecessary parameters. The proposed methods are illustrated in various simulations and real data applications. Ein Hauptaspekt der psychometrischen Modellierung liegt in der Messung latenter Eigenschaften. Diese Arbeit besch�ftigt sich haupts�chlich mit zwei weit verbreiteten Methoden um latente Eigenschaften zu analysieren, n�mlich Item Response Daten und Paarvergleiche. In gebr�uchlichen Modellen f�r Item Response Daten (z.B. dem Rasch Modell) oder Paarvergleichsdaten (z.B. dem Bradley-Terry Modell) wird keine Information aus Kovariablen ber�cksichtigt. Diese Arbeit behandelt die Einbeziehung verschiedener Arten von Kovariablen in Item Response Modellen und Paarvergleichsmodellen. Die gr� ere Flexibilit�t der vorgeschlagenen Methoden f�hrt aber auch zu einer gr� eren Komplexit�t der Modelle. Regularisierungsmethoden erweisen sich als probates Instrument um mit der gr� eren Anzahl an Parametern umzugehen und um zwischen notwendigen und unn�tigen Parametern zu unterscheiden. Die vorgeschlagenen Methoden werden anhand von verschiedenen Simulationen und Anwendungen auf echte Daten veranschaulicht.

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Regularization Methods for Item Response and Paired Comparison Models

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