Estimation and variable selection for generalized additive partial linear models
نویسندگان
چکیده
منابع مشابه
Estimation and Variable Selection for Generalized Additive Partial Linear Models.
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus ...
متن کاملEstimation and Variable Selection for Generalized Additive Partial Linear Models By
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus ...
متن کاملEstimation and Variable Selection for Semiparametric Additive Partial Linear Models (SS-09-140).
Semiparametric additive partial linear models, containing both linear and nonlinear additive components, are more flexible compared to linear models, and they are more efficient compared to general nonparametric regression models because they reduce the problem known as "curse of dimensionality". In this paper, we propose a new estimation approach for these models, in which we use polynomial sp...
متن کاملBayesian variable selection in additive partial linear models
Many studies in recent time include a large number of predictor variables, but typically only a few of the predictors have significant roles. Variable selection techniques have been developed using both non-Bayesian and Bayesian approaches. Additive partial linear models (APLM) provide a flexible yet manageable extension of linear models, where some variables can have non-linear effects. We dev...
متن کاملVariable Selection in Generalized Functional Linear Models.
Modern research data, where a large number of functional predictors is collected on few subjects are becoming increasingly common. In this paper we propose a variable selection technique, when the predictors are functional and the response is scalar. Our approach is based on adopting a generalized functional linear model framework and using a penalized likelihood method that simultaneously cont...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2011
ISSN: 0090-5364
DOI: 10.1214/11-aos885