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Clustering Longitudinal Profiles Using Non-parametric and Semi-parametric Mixed Effects Models
Meysam Tasallizadeh Khemes, Zahra Rezaei Ghahroodi
Abstract:   (2439 Views)

Longitudinal studies are a branch of Statistics associated with data which are collected over time. One area of scientific studies where longitudinal data can be collected is medicine and genetics studies. Since the clustering of time course gene expression cellular tissue of different people are useful and the acquisition of knowledge from massive data sets may seem complicated and in some cases impossible, the identification of ways to extract information from these types of data is essential. There are several methods for clustering time course gene expression data. But, these methods have limitations such as the lack of consideration of correlation over time and suffering of high computational.

In this paper, by introducing the non-parametric and semi parametric mixed effects model, this correlation over time is considered and by using penalized splines, computation burden dramatically reduced. At the end, using a simulation study to evaluate the performance of the presented method, this method is compared with previous methods and by using BIC criteria, the most appropriate model among the presented models is selected. Also the proposed approach is illustrated in a real time course gene expression data set. 

Keywords: Longitudinal Profiles, Spline Smoothing, Penalized Spline, Linear Mixed Effects Model, Model- Based Clustering, Gene Expression
Full-Text [PDF 7526 kb]   (619 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2015/04/3 | Accepted: 2016/04/10 | Published: 2016/12/20
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Tasallizadeh Khemes M, Rezaei Ghahroodi Z. Clustering Longitudinal Profiles Using Non-parametric and Semi-parametric Mixed Effects Models. J. of Stat. Sci.. 2017; 11 (1)
URL: http://jss.irstat.ir/article-1-366-en.html
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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