• Media type: Electronic Thesis; Doctoral Thesis; E-Book
  • Title: Hierarchical Binary Spatial Regression Models with Cluster Effects ; Hierarchische Binäre räumliche Regressionsmodelle mit Clustereffekten
  • Contributor: Prokopenko, Sergiy [Author]
  • imprint: Technical University of Munich; Technische Universität München, 2007-07-18
  • Language: English
  • Keywords: binäre Datensätze;Cluster;räumliche Effekte;Markov Chain Monte Carlo;Markov Zufallsfelder ; Mathematik ; binary data;spatial;cluster effects;conditional autoregression;markov random fields;markov chain monte carlo;pettitt's model;data augmentation
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  • Description: This work is motivated by a mobility study conducted in the city of Munich, Germany. The variable of interest is a binary response, which indicates whether public transport has been utilized or not. One of the central questions is to identify areas of low/high utilization of public transport after adjusting for explanatory factors such as trip, individual and household attributes. The goal of this thesis is to develop flexible statistical models for a binary response with covariate, spatial and cluster effects. One approach for modeling spatial effects are Markov Random Fields (MRF). A modification of a class of MRF models introduced by \citeN{pettitt:02} is developed in this work. This modification has the desirable property to contain the intrinsic MRF in the limit and still allows for fast and efficient spatial parameter updates in Markov Chain Monte Carlo (MCMC) algorithms. In addition to spatial effects, cluster effects are taken into consideration. Group and individual approaches for modeling these effects are suggested. The first one models heterogeneity between clusters, while the second one models heterogeneity within clusters. An unidentifiability problem occurring in the second case is solved. For hierarchical spatial binary regression model with individual cluster effects two MCMC algorithms for parameter estimation are developed. The first one is based on a direct evaluation of the likelihood. The second one is based on the representation of binary responses with Gaussian latent variables through a threshold mechanism, which is particularly useful for probit models. Extensive simulations are conducted to investigate the finite sample performance of the MCMC algorithms developed. They demonstrate satisfactory behaviour. Finally the proposed model classes are applied to the mobility study. ; Diese Arbeit wurde von einer Mobilitätsstudie in München motiviert. Dabei zeigt eine binäre Zielvariable an, ob ein Weg mit öffentlichem Verkehrsmittel (ÖV) oder Auto zurückgelegt wurde. Ein zentrales Ziel ist es, ...
  • Access State: Open Access