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Limit theorems for kernel density estimators under dependent samples
Kernel density estimator consistency convergence rate mixing rate
2013/6/14
In this paper, we construct a moment inequality for mixing dependent random variables, it is of independent interest. As applications, the consistency of the kernel density estimation is investigated....
Probit transformation for kernel density estimation on the unit interval
transformation kernel density estimator boundary bias local likelihood density estimation local log-polynomial density estimation
2013/4/27
Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well known boundary bias issues a conventional kernel density estimator would ne...
A comparative study of new cross-validated bandwidth selectors for kernel density estimation
kernel density estimation data-adaptive bandwidth selection indirect cross-validation do-validation.
2012/11/22
Recent contributions to kernel smoothing show that the performance of cross-validated bandwidth selectors improve significantly from indirectness. Indirect crossvalidation first estimates the classica...
We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density est...
Minimax properties of beta kernel density estimators
Beta Kernel Density Minimax estimation
2010/3/9
In this paper, we are interested in the study of beta kernel estimators from
an asymptotic minimax point of view. It is well known that beta kernel estimators
are—on the contrary of classical kernel...
Note on asymptotic normality of kernel density estimator for linear process under short-range dependence
asymptotic normality kernel density estimator linear process short-range dependence
2009/9/21
Wc mnf;i&r the paablem of density estimation for
m a one-sided linear prosees X, = zt _ , a, Z, , with i.id square iategra-
Me kovatims - We prove that under weak contritions on
(ai)&, which imply ...