Variance function additive partial linear models
نویسندگان
چکیده
منابع مشابه
Variance function partially linear single-index models
We consider heteroscedastic regression models where the mean function is a partially linear single-index model and the variance function depends on a generalized partially linear single-index model.We do not insist that the variance function depends only on the mean function, as happens in the classical generalized partially linear single-index model.We develop efficient and practical estimatio...
متن کاملEstimation 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 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 کاملDifferenced-Based Double Shrinking in Partial Linear Models
Partial linear model is very flexible when the relation between the covariates and responses, either parametric and nonparametric. However, estimation of the regression coefficients is challenging since one must also estimate the nonparametric component simultaneously. As a remedy, the differencing approach, to eliminate the nonparametric component and estimate the regression coefficients, can ...
متن کاملSparse Partially Linear Additive Models
The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing them to have either a linear or nonlinear effect on the response. However, the assignment of features to the linear and nonlinear groups is typically assumed known. Thus, to make a GPLAM a viable approach in situatio...
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: Electronic Journal of Statistics
سال: 2015
ISSN: 1935-7524
DOI: 10.1214/15-ejs1080