Adaptive estimation in circular functional linear models
نویسندگان
چکیده
منابع مشابه
Adaptive estimation in circular functional linear models
We consider the problem of estimating the slope parameter in circular functional linear regression, where scalar responses Y1, . . . , Yn are modeled in dependence of 1periodic, second order stationary random functions X1, . . . , Xn. We consider an orthogonal series estimator of the slope function β, by replacing the first m theoretical coefficients of its development in the trigonometric basi...
متن کاملPenalized contrast estimation in functional linear models with circular data
Our aim is to estimate the unknown slope function in the functional linear model when the response Y is real and the random function X is a second order stationary and periodic process. We obtain our estimator by minimizing a standard (and very simple) mean-square contrast on linear finite dimensional spaces spanned by trigonometric bases. Our approach provides a penalization procedure which al...
متن کاملOn adaptive estimation in partial linear models
The problem of estimation of the nite dimensional parameter in a partial linear model is considered. We derive upper and lower bounds for the second minimax order risk and show that the second order minimax estimator is a penalized maximum likelihood estimator. It is well known that the performance of the estimator is depending on the choice of a smoothing parameter. We propose a practically fe...
متن کاملMaximum Likelihood Estimation of Parameters in Generalized Functional Linear Model
Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to esti...
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: Mathematical Methods of Statistics
سال: 2010
ISSN: 1066-5307,1934-8045
DOI: 10.3103/s1066530710010035