 SPLINE MODELS WHICH USE IN LONGITUDINAL DATA ANALYSISSeda BAÄžDATLI Kalkan ()
Eurasian Eononometrics, Statistics and Emprical Economics Journal, 2017, vol. 6, issue 6, 18-35 Abstract: Longitudinal data is defined as data obtained by a repeated measurement of variables pertaining to the same units over time. The analysis of longitudinal data cannot be achieved through classical regression models because of the independence and multicollinearity assumptions. For this reason, specific regression models have been developed for such data. Classical parametric models are based on the rationale that the relation between the dependent variable and the independent variable(s) is linear or the relation is expressed through known parametric functions. In such a case, it is not possible to reveal the actual structure of the relation, which will prevent the researcher from achieving reliable and rational outcomes particularly in longitudinal datasets. Non-parametric regression model is utilized in cases where the relation between the dependent variable and the independent variable(s) is more complicated in longitudinal data. In this study spline models in nonparametric regression models which use in longitudinal data are investigated theoretically. Date: 2017 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eas:econst:v:6:y:2017:i:6:p:18-35 DOI: 10.17740/eas.stat.2017-V6-02 Access Statistics for this article More articles in Eurasian Eononometrics, Statistics and Emprical Economics Journal from Eurasian Academy Of Sciences |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.