Longitudinal Data Analysis Using Liu Regression

Ramalingam Shanmugam, Arashi M and Salarzadeh Jenatabadi H
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Ramalingam Shanmugam: Department of Statistics, Shahrood University of Technology, Shahrood, Iran
Arashi M: Department of Statistics, Shahrood University of Technology, Shahrood, Iran
Salarzadeh Jenatabadi H: Department of Science and Technology Studies, University of Malaya, Malaysia

Biostatistics and Biometrics Open Access Journal, 2018, vol. 7, issue 5, 102-105

Abstract: For understanding and characterizing discase progression over time, Eliot et al. [1] proposed a mixed ridge regression to account correlated outcomes and potentially high degree of correlated predictors for Biomarker data. However, the ridge estimator is non-linear in nature w.r.t. the ridge parameter and hence it is hard to estimate In this paper, we propose a linear unified approach to combat this difficultly. Numerical studies illustrate the usefulness of our approach compared to the mixed model.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:7:y:2018:i:5:p:102-105

DOI: 10.19080/BBOAJ.2018.07.555725

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