Generalized partially linear varying coefficient models with multiple smoothing variables

Rejoinder

“…together with rate-optimal estimators of the nonparametric component functions f j . The latter paper, by Yang & Lee (2014), extended this to the generalized setting…”

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

“…Yang & Park gave semiparametric efficient estimators of the regression coefficient vector β in the parametric part of the partially linear varying coefficient model together with rate‐optimal estimators of the nonparametric component functions f j . The latter paper, by Yang & Lee , extended this to the generalized setting with a link function g , so that the model accommodates discrete responses. The methods studied in these works may be tailored immediately to a model such as (6.5) where some different smoothing variables share a common linear effect variable.…”

Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data

“…When all coefficient functions share the same smoothing variable, model (1) becomes the varying-coefficient partially linear model which has been widely studied in the literature as well, for instance, the work of Zhang et al [5], Li et al [6], Fan and Huang [7], and Huang and Zhang [8] among others. When the coefficient functions have different smoothing variables, Ip et al [9] used a generalized likelihood ratio test to test coefficient functions in functional-coefficient regression models; Zhang and Li [10] discussed the functionalcoefficient regression models with different smoothing variables in different coefficient functions and defined the integrated estimates of the coefficient functions by marginal integration; Zhang and Li [11] introduced averaged estimation for coefficient functions in functional-coefficient regression models with different smoothing variables; Zhang and Li [12] proposed a procedure for estimating the coefficient functions in the functional-coefficient regression models with different smoothing variables in different coefficient functions; Yang and Lee [13] discussed the semiparametric efficient estimation for generalized functionalcoefficient regression models with multiple smoothing variables.…”