Abstract
We develop a classified mixed logistic model prediction (CMLMP) method for clustered binary data by extending a method proposed by Jiang et al. (2018) for continuous outcome data. By identifying a class, or cluster, that the new observations belong to, we are able to improve the prediction accuracy of a probabilistic mixed effect associated with a future observation over the traditional method of logistic regression and mixed model prediction without matching the class. Furthermore, we develop a new strategy for identifying the class for the new observations by utilizing covariates information, which improves accuracy of the class identification. In addition, we develop a method of obtaining second-order unbiased estimators of the mean squared prediction errors (MSPEs) for CMLMP, which are used to provide measures of uncertainty. We prove consistency of CMLMP, and demonstrate finite-sample performance of CMLMP via simulation studies. Our results show that the proposed CMLMP method outperforms the traditional methods in terms of predictive performance. An application to medical data is discussed.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 63-74 |
| Number of pages | 12 |
| Journal | Journal of Multivariate Analysis |
| Volume | 168 |
| DOIs | |
| State | Published - Nov 1 2018 |
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Keywords
- Clustered binary data
- CMLMP
- CMMP
- Matching
- Mixed logistic model
- Mixed model prediction
- MSPE
ASJC Scopus subject areas
- Statistics and Probability
- Numerical Analysis
- Statistics, Probability and Uncertainty
Cite this
Classified mixed logistic model prediction. / Sun, Hanmei; Nguyen, Thuan; Luan, Yihui; Jiang, Jiming.
In: Journal of Multivariate Analysis, Vol. 168, 01.11.2018, p. 63-74.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Classified mixed logistic model prediction
AU - Sun, Hanmei
AU - Nguyen, Thuan
AU - Luan, Yihui
AU - Jiang, Jiming
PY - 2018/11/1
Y1 - 2018/11/1
N2 - We develop a classified mixed logistic model prediction (CMLMP) method for clustered binary data by extending a method proposed by Jiang et al. (2018) for continuous outcome data. By identifying a class, or cluster, that the new observations belong to, we are able to improve the prediction accuracy of a probabilistic mixed effect associated with a future observation over the traditional method of logistic regression and mixed model prediction without matching the class. Furthermore, we develop a new strategy for identifying the class for the new observations by utilizing covariates information, which improves accuracy of the class identification. In addition, we develop a method of obtaining second-order unbiased estimators of the mean squared prediction errors (MSPEs) for CMLMP, which are used to provide measures of uncertainty. We prove consistency of CMLMP, and demonstrate finite-sample performance of CMLMP via simulation studies. Our results show that the proposed CMLMP method outperforms the traditional methods in terms of predictive performance. An application to medical data is discussed.
AB - We develop a classified mixed logistic model prediction (CMLMP) method for clustered binary data by extending a method proposed by Jiang et al. (2018) for continuous outcome data. By identifying a class, or cluster, that the new observations belong to, we are able to improve the prediction accuracy of a probabilistic mixed effect associated with a future observation over the traditional method of logistic regression and mixed model prediction without matching the class. Furthermore, we develop a new strategy for identifying the class for the new observations by utilizing covariates information, which improves accuracy of the class identification. In addition, we develop a method of obtaining second-order unbiased estimators of the mean squared prediction errors (MSPEs) for CMLMP, which are used to provide measures of uncertainty. We prove consistency of CMLMP, and demonstrate finite-sample performance of CMLMP via simulation studies. Our results show that the proposed CMLMP method outperforms the traditional methods in terms of predictive performance. An application to medical data is discussed.
KW - Clustered binary data
KW - CMLMP
KW - CMMP
KW - Matching
KW - Mixed logistic model
KW - Mixed model prediction
KW - MSPE
UR - http://www.scopus.com/inward/record.url?scp=85050077527&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050077527&partnerID=8YFLogxK
U2 - 10.1016/j.jmva.2018.06.004
DO - 10.1016/j.jmva.2018.06.004
M3 - Article
AN - SCOPUS:85050077527
VL - 168
SP - 63
EP - 74
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
SN - 0047-259X
ER -