TY - JOUR
T1 - Sparsistent and constansistent estimation of the varying-coefficient model with a diverging number of predictors
AU - Zhao, Kaifeng
AU - Lian, Heng
PY - 2016/11/1
Y1 - 2016/11/1
N2 - The varying-coefficient model is a flexible class of approaches that extends simple linear relationships between covariates and responses. Two related problems concerning these models are selecting relevant variables and determining non-varying coefficients among those relevant ones. In this paper we study the sparsistency and constansistency of the regularized estimation approach when the number of predictors diverges with the sample size. Here, constansistency refers to the desired property that the non-zero, non-varying coefficients are identified with probability tending to one.
AB - The varying-coefficient model is a flexible class of approaches that extends simple linear relationships between covariates and responses. Two related problems concerning these models are selecting relevant variables and determining non-varying coefficients among those relevant ones. In this paper we study the sparsistency and constansistency of the regularized estimation approach when the number of predictors diverges with the sample size. Here, constansistency refers to the desired property that the non-zero, non-varying coefficients are identified with probability tending to one.
KW - B-spline basis
KW - BIC
KW - Constansistency
KW - Varying-coefficient models
UR - http://www.scopus.com/inward/record.url?scp=84981724884&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84981724884&origin=recordpage
U2 - 10.1080/03610926.2014.890224
DO - 10.1080/03610926.2014.890224
M3 - RGC 21 - Publication in refereed journal
SN - 0361-0926
VL - 45
SP - 6385
EP - 6399
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 21
ER -