A bandwidth selector for local polynomial fitting is proposed following the bootstrap idea, which is just a double smoothing bandwidth selector with a bootstrap variance estimator, defined as the mean squared residuals of a pilot estimate. No simulated resampling is required in this context,...

A bandwidth selector for local polynomial fitting is proposed following the bootstrap idea, which is just a double smoothing bandwidth selector with a bootstrap variance estimator, defined as the mean squared residuals of a pilot estimate. No simulated resampling is required in this context,...

In this paper a modified double smoothing bandwidth selector, MDS, based on a new criterion, which combines the plug-in and the double smoothing ideas, is proposed. A self-complete iterative double smoothing rule (_IDS ) is introduced as a pilot method. The asymptotic properties of both_IDS...

A bandwidth selector for local polynomial fitting is proposed following the bootstrap idea, which is just a double smoothing bandwidth selector with a bootstrap variance estimator, defined as the mean squared residuals of a pilot estimate. No simulated resampling is required in this context,...

The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which...

Identifying patterns in bivariate data on a scatterplot remains a ba- sic statistical problem, with special flavor when both variables are on the same footing. Ideas of double, diagonal, and polar smoothing inspired by Cleveland and McGill’s 1984 paper in the Journal of the American...

In this note we present a direct and simple approach to obtain bounds on the asymptotic minimax risk for the estimation of restrained binominal and multinominal proportions. Quadratic, normalized quadratic and entropy loss are considered and it is demonstrated that in all cases linear estimators...

For the two-parameter exponential family, a linear Bayes method is proposed to simultaneously estimate the parameter vector consisting of location and scale parameters. The superiority of the proposed linear Bayes estimator (LBE) over the classical UMVUE is established in terms of the mean...

We consider estimation of the mean vector, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\theta $$</EquationSource> </InlineEquation>, of a spherically symmetric distribution with known scale parameter under quadratic loss and when a residual vector is available. We show minimaxity of generalized Bayes estimators corresponding to superharmonic priors with a non...</equationsource></inlineequation>

We propose point forecast accuracy measures based directly on distance of the forecast-error c.d.f. from the unit step function at 0 (\stochastic error distance," or SED). We provide a precise characterization of the relationship between SED and standard predictive loss functions, showing that...