منابع مشابه
A new class of generalized Bayes minimax ridge regression estimators
Let y = Aβ + ε, where y is an N × 1 vector of observations, β is a p× 1 vector of unknown regression coefficients, A is an N × p design matrix and ε is a spherically symmetric error term with unknown scale parameter σ. We consider estimation of β under general quadratic loss functions, and, in particular, extend the work of Strawderman [J. Amer. Statist. Assoc. 73 (1978) 623–627] and Casella [A...
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This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the La- grangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting t...
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For a Poisson point process X , Itô’s famous chaos expansion implies that every square integrable regression function r with covariate X can be decomposed as a sum of multiple stochastic integrals called chaos. In this paper, we consider the case where r can be decomposed as a sum of δ chaos. In the spirit of Cadre and Truquet (2015), we introduce a semiparametric estimate of r based on i.i.d. ...
متن کاملRidge Regression Estimation for Survey Samples
A procedure for constructing a vector of regression weights is considered. Under the regression superpopulation model, the ridge regression estimator that has minimum model mean squared error is derived. Through a simulation study, the ridge regression weights, regression weights, quadratic programming weights and raking ratio weights are compared. The ridge regression procedure with weights bo...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1980
ISSN: 0090-5364
DOI: 10.1214/aos/1176345141