Estimation of Covariance Matrix on Bi-Response Longitudinal Data Analysis with Penalized Spline Regression

The correlation assumption of the longitudinal data with bi-response occurs on the measurement between the subjects of observation and the response. It causes the auto-correlation of error, and this can be overcome by using a covariance matrix. In this article, we estimate the covariance matrix base...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 979; no. 1; pp. 12093 - 12099
Main Authors Islamiyati, A, Fatmawati, Chamidah, N
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.03.2018
Subjects
Covariance matrix
Data analysis
Errors
Physics
Regression models
Smoothness
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Summary:The correlation assumption of the longitudinal data with bi-response occurs on the measurement between the subjects of observation and the response. It causes the auto-correlation of error, and this can be overcome by using a covariance matrix. In this article, we estimate the covariance matrix based on the penalized spline regression model. Penalized spline involves knot points and smoothing parameters simultaneously in controlling the smoothness of the curve. Based on our simulation study, the estimated regression model of the weighted penalized spline with covariance matrix gives a smaller error value compared to the error of the model without covariance matrix.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/979/1/012093