Journal article
A Diagnostic for Assessing the Influence of Cases on the Prediction of Random Effects in a Mixed Model
Journal of data science, Vol.3(2), pp.137 -151
04/01/2005
DOI: 10.6339/JDS.2005.03(2).203
Abstract
A diagnostic defined in terms of the Kullback-Leibler directed divergence is developed for identifying cases which impact the prediction of the random effects in a mixed model. The diagnostic compares two conditional densities governing the prediction of the random effects: one based on parameter estimates computed using the full data set, the other based on parameter estimates computed using a case-deleted data set. We present the definition of the diagnostic and derive a formula for its evaluation. Its performance is investigated in an application where exam scores are modeled using a mixed model containing a fixed exam effect and a random subject effect.
Details
- Title: Subtitle
- A Diagnostic for Assessing the Influence of Cases on the Prediction of Random Effects in a Mixed Model
- Creators
- Joseph E. CavanaughJun-Feng Shang
- Resource Type
- Journal article
- Publication Details
- Journal of data science, Vol.3(2), pp.137 -151
- DOI
- 10.6339/JDS.2005.03(2).203
- ISSN
- 1683-8602
- eISSN
- 1683-8602
- Publisher
- 中華資料採礦協會
- Number of pages
- 15
- Language
- English
- Date published
- 04/01/2005
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984214706402771
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