Efficient estimation for partially linear varying coefficient models when coefficient functions have different smoothing variables

Yang, Seong J.; Park, Byeong U.

doi:10.1016/j.jmva.2014.01.004

Cited by 8 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, we employed a profiling method to estimate the model (6.5) in our data example. A detailed account of the method for related models is contained in Yang & Park and Yang & Lee . Yang & Park gave semiparametric efficient estimators of the regression coefficient vector β in the parametric part of the partially linear varying coefficient model

E (Y | U, X, Z) = U^{⊤} β + X_{1} f_{1} (Z_{1}) + \dots + X_{d} f_{d} (Z_{d}),

together with rate‐optimal estimators of the nonparametric component functions f j .…”

Section: Statistical Inference and Partially Linear Varying Coefficiementioning

confidence: 99%

“…A detailed account of the method for related models is contained in Yang & Park and Yang & Lee . Yang & Park gave semiparametric efficient estimators of the regression coefficient vector β in the parametric part of the partially linear varying coefficient model

E (Y | U, X, Z) = U^{⊤} β + X_{1} f_{1} (Z_{1}) + \dots + X_{d} f_{d} (Z_{d}),

together with rate‐optimal estimators of the nonparametric component functions f j . The latter paper, by Yang & Lee , extended this to the generalized setting

g (E (Y | U, X, Z)) = U^{⊤} β + X_{1} f_{1} (Z_{1}) + \dots + X_{d} f_{d} (Z_{d}),

with a link function g , so that the model accommodates discrete responses.…”

Section: Statistical Inference and Partially Linear Varying Coefficiementioning

confidence: 99%

See 1 more Smart Citation

Rejoinder

Park

Mammen

Lee

et al. 2014

Int Statistical Rev

Self Cite

View full text Add to dashboard Cite

E (Y | U, X, Z) = U^{⊤} β + X_{1} f_{1} (Z_{1}) + \dots + X_{d} f_{d} (Z_{d}),

together with rate‐optimal estimators of the nonparametric component functions f j .…”

Section: Statistical Inference and Partially Linear Varying Coefficiementioning

confidence: 99%

E (Y | U, X, Z) = U^{⊤} β + X_{1} f_{1} (Z_{1}) + \dots + X_{d} f_{d} (Z_{d}),

together with rate‐optimal estimators of the nonparametric component functions f j . The latter paper, by Yang & Lee , extended this to the generalized setting

g (E (Y | U, X, Z)) = U^{⊤} β + X_{1} f_{1} (Z_{1}) + \dots + X_{d} f_{d} (Z_{d}),

with a link function g , so that the model accommodates discrete responses.…”

Section: Statistical Inference and Partially Linear Varying Coefficiementioning

confidence: 99%

Rejoinder

Park

Mammen

Lee

et al. 2014

Int Statistical Rev

Self Cite

View full text Add to dashboard Cite

“…They also proposed adaptive penalization methods for variable selection in model (1.1) and demonstrated that the methods possess the oracle property. More references and techniques of the semivarying coefficient model can be found in Zhou and Liang (2009), Fan et al (2012) and Yang and Park (2014) among others.…”

Section: Introductionmentioning

confidence: 99%

Empirical likelihood for semivarying coefficient model with measurement error in the nonparametric part

Fan

Huang

2015

AStA Adv Stat Anal

View full text Add to dashboard Cite

A semivarying coefficient model with measurement error in the nonparametric part was proposed by Feng and Xue (Ann Inst Stat Math 66:121-140, 2014), but its inferences have not been systematically studied. This paper applies empirical likelihood method to construct confidence regions/intervals for the regression parameter and coefficient function. When some auxiliary information about the parametric part is available, the empirical log-likelihood ratio statistic for the regression parameter is introduced based on the corrected local linear estimator of the coefficient function. Furthermore, corrected empirical log-likelihood ratio statistic for coefficient function is also investigated with the use of auxiliary information. The limiting distributions of the resulting statistics both for the regression parameter and coefficient function are shown to have standard Chi-squared distribution. Simulation experiments and a real data set are presented to evaluate the finite sample performance of our proposed method.

show abstract

Effective identification and estimation for the semiparametric measurement error model

Hao

2017

Journal of the Korean Statistical Society

View full text Add to dashboard Cite

Efficient estimation for partially linear varying coefficient models when coefficient functions have different smoothing variables

Cited by 8 publications

References 19 publications

Rejoinder

Rejoinder

Empirical likelihood for semivarying coefficient model with measurement error in the nonparametric part

Effective identification and estimation for the semiparametric measurement error model

Contact Info

Product

Resources

About