Add to ORKGMany asymptotic results for kernel-based estimators were established under some smoothness assumption on density. For cases where smoothness assumptions that are used to derive unbiasedness or asymptotic rate may not hold we propose a combined estimator that could lead to the best available rate without knowledge of density smoothness. A Monte Carlo example confirms good performance of the combined estimator.
Nonparametric estimation of the mode of a density or regression function via kernel methods is consi...
Nonparametric estimation of the mode of a density or regression function via kernel methods is consi...
Nonparametric estimation of the mode of a density or regression function via kernel methods is consi...
Many asymptotic results for kernel-based estimators were established under some smoothness assumptio...
Many asymptotic results for kernel-based estimators were estab-lished under some smoothness assumpti...
For local and average kernel based estimators, smoothness conditions ensure that the kernel order de...
For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias...
In this paper, we summarize results on convergence rates of various kernel based non- and semiparame...
Semiparametric and nonparametric estimators are becoming indispensable tools in applied econometric...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
Results on nonparametric kernel estimators of density differ according to the assumed degree of dens...
AbstractIn this paper, we propose a combined regression estimator by using a parametric estimator an...
Nonparametric estimation of the mode of a density or regression function via kernel methods is consi...
Nonparametric estimation of the mode of a density or regression function via kernel methods is consi...
Nonparametric estimation of the mode of a density or regression function via kernel methods is consi...
Many asymptotic results for kernel-based estimators were established under some smoothness assumptio...
Many asymptotic results for kernel-based estimators were estab-lished under some smoothness assumpti...
For local and average kernel based estimators, smoothness conditions ensure that the kernel order de...
For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias...
In this paper, we summarize results on convergence rates of various kernel based non- and semiparame...
Semiparametric and nonparametric estimators are becoming indispensable tools in applied econometric...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a cl...
Results on nonparametric kernel estimators of density differ according to the assumed degree of dens...
AbstractIn this paper, we propose a combined regression estimator by using a parametric estimator an...
Nonparametric estimation of the mode of a density or regression function via kernel methods is consi...
Nonparametric estimation of the mode of a density or regression function via kernel methods is consi...
Nonparametric estimation of the mode of a density or regression function via kernel methods is consi...