Latent class analysis identification of syndromes in alzheimer's disease: a bayesian approach

Walsh, Cathal D. (author)

Latent variable models have been used extensively in the social sciences. In this work a latent class analysis is used to identify syndromes within Alzheimer's disease. The fitting of the model is done in a Bayesian framework,and this is examined in detail here. In particular, the label switching problem is identified, and solutions presented. Graphical summaries of the posterior distribution are included.

• Language: English
• Work type: Not categorized
• Typology: 1.01 - Original Scientific Article
• Organization: FDV - Faculty of Social Sciences
• Year: 2006
• Number of pages: Str. 147-162
• Numbering: Vol. 3, no. 1
• PID: 20.500.12556/RUL-8446
• UDC: 303
• ISSN on article: 1854-0023
• COBISS.SI-ID: 25331805
• Publication date in RUL: 11.07.2014

Record is a part of a journal

• Title: Advances in methodology and statistics
• Shortened title: Metodol. zv.
• Publisher: Fakulteta za družbene vede
• ISSN: 1854-0023
• COBISS.SI-ID: 215795712