Functional ANOVA Models for Generalized Regression
نویسندگان
چکیده
منابع مشابه
Functional ANOVA Models for Generalized Regression
Functional ANOVA models are considered in the context of generalized regression, which includes logistic regression, probit regression and Poisson regression as special cases. The multivariate predictor function is modeled as a speci ed sum of a constant term, main e ects and interaction terms. Maximum likelihood estimates are used, where the maximizations are taken over suitably chosen approxi...
متن کاملGeneralized Ridge Regression Estimator in Semiparametric Regression Models
In the context of ridge regression, the estimation of ridge (shrinkage) parameter plays an important role in analyzing data. Many efforts have been put to develop skills and methods of computing shrinkage estimators for different full-parametric ridge regression approaches, using eigenvalues. However, the estimation of shrinkage parameter is neglected for semiparametric regression models. The m...
متن کاملApplication of functional ANOVA models for hazard regression to the Hisayama data.
A methodology for modeling covariate effects on the time-to-event data is developed. The covariates are allowed to be time dependent and their effects are modeled using polynomial splines in order to account for possibly non-linear effects. The methodology is applied to examine the effects on the incidence brain infarction based on a cohort study in Hisayama, Japan. The results indicate that at...
متن کاملGeneralized Multilevel Functional Regression.
We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models (GLMMs). Thus, GMFLMs can be analyzed using the mixed effects inferential machinery and can be generalized within a well researched statistical framew...
متن کاملProjection Estimation in Multiple Regression with Application to Functional Anova Models
A general theory on rates of convergence in multiple regression is developed, where the regression function is modeled as a member of an arbitrary linear function space (called a model space), which may be niteor in nite-dimensional. A least squares estimate restricted to some approximating space, which is in fact a projection, is employed. The error in estimation is decomposed into three parts...
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: Journal of Multivariate Analysis
سال: 1998
ISSN: 0047-259X
DOI: 10.1006/jmva.1998.1753