Least Absolute Deviation Estimate for Functional Coefficient Partially Linear Regression Models | Zendy

Yanqin Feng | Zendy; Guoxin Zuo | Zendy; Li Liu | Zendy

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Least Absolute Deviation Estimate for Functional Coefficient Partially Linear Regression Models

Author(s) -

Yanqin Feng,

Guoxin Zuo,

Li Liu

Publication year - 2012

Publication title -

journal of probability and statistics

Language(s) - English

Resource type - Journals

eISSN - 1687-9538

pISSN - 1687-952X

DOI - 10.1155/2012/131085

Subject(s) - mathematics , linear regression , proper linear model , linear model , estimator , least absolute deviations , linear predictor function , generalization , nonparametric statistics , coefficient matrix , nonparametric regression , regression analysis , statistics , polynomial regression , mathematical analysis , eigenvalues and eigenvectors , physics , quantum mechanics

The functional coefficient partially linear regression model is a useful generalization of the nonparametricmodel, partial linear model, and varying coefficient model. In this paper, the local linear technique and the method are employed to estimate all the functions in the functional coefficient partially linear regression model. The asymptotic properties of the proposed estimators are studied. Simulation studies are conducted to show the validity of the estimate procedure

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