Latent class analysis identification of syndromes in alzheimer's disease: a bayesian approach
Abstract
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.
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Data
| Language: | English |
|---|---|
| Year of publishing: | 2006 |
| Typology: | 1.01 - Original Scientific Article |
| Organization: | UL FDV - Faculty of Social Sciences |
| Publisher: | Fakulteta za družbene vede |
| UDC: | 303 |
| COBISS: |
25331805
|
| ISSN: | 1854-0023 |
| Views: | 324 |
| Downloads: | 55 |
| Average score: | 0 (0 votes) |
| Metadata: |
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Other data
| Secondary language: | Unknown |
|---|---|
| URN: | URN:NBN:SI |
| Type (COBISS): | Not categorized |
| Pages: | str. 147-162 |
| Volume: | ǂVol. ǂ3 |
| Issue: | ǂno. ǂ1 |
| Chronology: | 2006 |
| Keywords (UDC): | social sciences;družbene vede;methods of the social sciences;metode družbenih ved; |
| ID: | 40640 |
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