Title
Generalized likelihood ratio test for varying-coefficient models with different smoothing variables
Abstract
Varying-coefficient models are popular multivariate nonparametric fitting techniques. When all coefficient functions in a varying-coefficient model share the same smoothing variable, inference tools available include the F-test, the sieve empirical likelihood ratio test and the generalized likelihood ratio (GLR) test. However, when the coefficient functions have different smoothing variables, these tools cannot be used directly to make inferences on the model because of the differences in the process of estimating the functions. In this paper, the GLR test is extended to models of the latter case by the efficient estimators of these coefficient functions. Under the null hypothesis the new proposed GLR test follows the @g^2-distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters, known as Wilks phenomenon. Further, we have derived its asymptotic power which is shown to achieve the optimal rate of convergence for nonparametric hypothesis testing. A simulation study is conducted to evaluate the test procedure empirically.
Year
DOI
Venue
2007
10.1016/j.csda.2006.07.027
Computational Statistics & Data Analysis
Keywords
Field
DocType
sieve empirical likelihood ratio,test procedure empirically,new proposed glr test,nonparametric hypothesis testing,multivariate nonparametric fitting technique,generalized likelihood ratio,varying-coefficient model,different smoothing variable,coefficient function,generalized likelihood ratio test,glr test,degree of freedom,efficient estimator,hypothesis test,nuisance parameter
Econometrics,Score test,Nuisance parameter,Likelihood-ratio test,F-test,Empirical likelihood,Smoothing,Statistics,Mathematics,Statistical hypothesis testing,Ratio test
Journal
Volume
Issue
ISSN
51
9
Computational Statistics and Data Analysis
Citations 
PageRank 
References 
5
4.80
0
Authors
3
Name
Order
Citations
PageRank
Wai-Cheung Ip12711.72
Heung Wong28022.74
Riquan Zhang35221.55