Variance analysis for kernel smoothing of a varying-coefficient model with longitudinal data

UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
Jinsong Chen (Creator)
The University of North Carolina Wilmington (UNCW )
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Abstract: We consider the estimation of the k + 1-dimensional nonparametric component (t) of the varying-coefficient model Y (t) = XT (t) (t) + "(t) based on longitudinal observation (Yij ,Xi(tij), tij), i = 1, ..., n, j = 1, ..., ni, where tij is the jth observed design time point t of the ith subjects at tij . The subjects are independently selected, but the repeated measurements within subject are possibly correlated.A Monte Carlo Simulation was established, kernel smoothing method was used to estimate (t) that minimizes a local least square criterion. The distribution for "(t) was analyzed. The degree of freedom was investigated.

Additional Information

A Thesis Submitted to the University of North Carolina at Wilmington in Partial Fulfillment Of the Requirements for the Degree of Master of Arts
Language: English
Date: 2009
Functional analysis, Kernel functions, Monte Carlo method, Nonparametric statistics
Kernel functions
Monte Carlo method
Nonparametric statistics
Functional analysis

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