Hierarchical Bayesian Models for Multiple Count Data
The aim of this paper is to develop a model for analyzing multiple response models for count data and that may take into account complex correlation structures. The model is specified hierarchically in several layers and can be used for sparse data as it is shown in the second part of the paper. It...
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| Format: | Article |
| Language: | English |
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Austrian Statistical Society
2016-04-01
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| Series: | Austrian Journal of Statistics |
| Online Access: | http://www.ajs.or.at/index.php/ajs/article/view/484 |
| _version_ | 1818925062526337024 |
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| author | Radu Tunaru |
| author_facet | Radu Tunaru |
| author_sort | Radu Tunaru |
| collection | DOAJ |
| description | The aim of this paper is to develop a model for analyzing multiple response models for count data and that may take into account complex correlation structures. The model is specified hierarchically in several layers and can be used for sparse data as it is shown in the second part of the paper. It is a discrete multivariate response approach regarding the left side of models equations. Markov Chain Monte Carlo techniques are needed for extracting inferential results. The possible correlation between different counts is more
general than the one used in repeated measurements or longitudinal studies framework. |
| first_indexed | 2024-12-20T02:35:15Z |
| format | Article |
| id | doaj.art-8981dd65527d42a29763e3d1a2c21fa0 |
| institution | Directory Open Access Journal |
| issn | 1026-597X |
| language | English |
| last_indexed | 2024-12-20T02:35:15Z |
| publishDate | 2016-04-01 |
| publisher | Austrian Statistical Society |
| record_format | Article |
| series | Austrian Journal of Statistics |
| spelling | doaj.art-8981dd65527d42a29763e3d1a2c21fa02022-12-21T19:56:27ZengAustrian Statistical SocietyAustrian Journal of Statistics1026-597X2016-04-01312&310.17713/ajs.v31i2&3.484Hierarchical Bayesian Models for Multiple Count DataRadu Tunaru0Economics Department, London Metropolitan UniversityThe aim of this paper is to develop a model for analyzing multiple response models for count data and that may take into account complex correlation structures. The model is specified hierarchically in several layers and can be used for sparse data as it is shown in the second part of the paper. It is a discrete multivariate response approach regarding the left side of models equations. Markov Chain Monte Carlo techniques are needed for extracting inferential results. The possible correlation between different counts is more general than the one used in repeated measurements or longitudinal studies framework.http://www.ajs.or.at/index.php/ajs/article/view/484 |
| spellingShingle | Radu Tunaru Hierarchical Bayesian Models for Multiple Count Data Austrian Journal of Statistics |
| title | Hierarchical Bayesian Models for Multiple Count Data |
| title_full | Hierarchical Bayesian Models for Multiple Count Data |
| title_fullStr | Hierarchical Bayesian Models for Multiple Count Data |
| title_full_unstemmed | Hierarchical Bayesian Models for Multiple Count Data |
| title_short | Hierarchical Bayesian Models for Multiple Count Data |
| title_sort | hierarchical bayesian models for multiple count data |
| url | http://www.ajs.or.at/index.php/ajs/article/view/484 |
| work_keys_str_mv | AT radutunaru hierarchicalbayesianmodelsformultiplecountdata |