On orthogonal series estimation of bounded regression functions
نویسندگان
چکیده
منابع مشابه
Orthogonal series density estimation
Orthogonal series density estimation is a powerful nonparametric estimation methodology that allows one to analyze and present data at hand without any prior opinion about shape of an underlying density. The idea of construction of an adaptive orthogonal series density estimator is explained on the classical example of a direct sample from a univariate density. Data-driven estimators, which hav...
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In the paper [Y. Okuyama, {it On the absolute generalized N"{o}rlund summability of orthogonal series},Tamkang J. Math. Vol. 33, No. 2, (2002), 161-165] the author has found some sufficient conditions under which an orthogonal seriesis summable $|N,p,q|$ almost everywhere. These conditions are expressed in terms of coefficients of the series. It is the purpose ofthis paper to extend this result...
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Let (X; Y) be a pair of random variables with supp(X) 0; 1] and EY 2 < 1. Let m be the corresponding regression function. Estimation of m from i.i.d. data is considered. The L 2 error with integration with respect to the design measure (i.e., the distribution of X) is used as an error criterion. Estimates are constructed by estimating the coeecients of an orthonormal expansion of the regression...
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The standard kernel density estimator suffers from a boundary bias issue for probability density function of distributions on the positive real line. The Gamma kernel estimators and orthogonal series estimators are two alternatives which are free of boundary bias. In this paper, a simulation study is conducted to compare small-sample performance of the Gamma kernel estimators and the orthog...
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ژورنال
عنوان ژورنال: Applicationes Mathematicae
سال: 2001
ISSN: 1233-7234,1730-6280
DOI: 10.4064/am28-3-2