Mean squared error properties of kernel estimates of regression quantiles
- Jones, M. C.
- Hall, Peter
DOIAbstract
Mean squared error properties of kernel estimates of regression quantiles, for both fixed and random design cases, are derived and discussed
Mean squared error properties of kernel estimates of regression quantiles, for both fixed and random design cases, are derived and discussed
Convolution type kernel estimators such as the Priestley-Chao estimator have been discussed by sever...
Title: Regression Quantiles Author: Peter Rusnák Department: Department of Probabilty and Mathematic...
Quantile regression extends the statistical analysis of the response models beyond conditional means...
We consider the problem of estimating quantile regression coefficients in errorsin -variables models...
We consider the problem of estimating quantile regression coecients in errors invariables models Wh...
AbstractLet (X, Y), (X1, Y1), …, (Xn, Yn) be i.d.d. Rr × R-valued random vectors with E|Y| < ∞, and ...
This article considers estimation of regression function ff in the fixed design model Y(xi)=f(xi)...
We establish the asymptotic normality of the regression estimator in a fixed-design setting when the...
Local kernel estimates and B-spline estimates are considered in the nonparametric regression and the...
We propose two estimators of quantile density function in linear regression model. The estimators, e...
International audienceIn this paper, we investigate kernel regression estimation when the data are c...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
Allowing for misspecification in the linear conditional quantile function, this paper provides a new...
In this paper, we establish asymptotic normality of Powell's kernel estimator for the asymptoti...
We explore the aims of, and relationships between, various kernel-type regression estimators. To do ...
Convolution type kernel estimators such as the Priestley-Chao estimator have been discussed by sever...
Title: Regression Quantiles Author: Peter Rusnák Department: Department of Probabilty and Mathematic...
Quantile regression extends the statistical analysis of the response models beyond conditional means...
We consider the problem of estimating quantile regression coefficients in errorsin -variables models...
We consider the problem of estimating quantile regression coecients in errors invariables models Wh...
AbstractLet (X, Y), (X1, Y1), …, (Xn, Yn) be i.d.d. Rr × R-valued random vectors with E|Y| < ∞, and ...
This article considers estimation of regression function ff in the fixed design model Y(xi)=f(xi)...
We establish the asymptotic normality of the regression estimator in a fixed-design setting when the...
Local kernel estimates and B-spline estimates are considered in the nonparametric regression and the...
We propose two estimators of quantile density function in linear regression model. The estimators, e...
International audienceIn this paper, we investigate kernel regression estimation when the data are c...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
Allowing for misspecification in the linear conditional quantile function, this paper provides a new...
In this paper, we establish asymptotic normality of Powell's kernel estimator for the asymptoti...
We explore the aims of, and relationships between, various kernel-type regression estimators. To do ...
Convolution type kernel estimators such as the Priestley-Chao estimator have been discussed by sever...
Title: Regression Quantiles Author: Peter Rusnák Department: Department of Probabilty and Mathematic...
Quantile regression extends the statistical analysis of the response models beyond conditional means...
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