Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven one, which satisfies a constraint on the number of modes of the estimated density. When using a random bandwidth, it is of particular interest to show that it goes toward 0 in probability when the sample size goes to infinity. Such a property is important to prove satisfying asymptotic results about the corresponding kernel density estimator. It is shown here that this property is not true for the uniform kernel
ABSTRACT. – In the usual right-censored data situation, let fn, n ∈ N, denote the convolution of the...
In this thesis, we are concerned with nonparametric estimation of the density given a random sample....
AbstractMultivariate kernel density estimators are known to systematically deviate from the true val...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
Abstract. Among available bandwidths for kernel density estimators, the critical bandwidth is a data...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
International audienceA data-driven bandwidth choice for a kernel density estimator called critical ...
International audienceA data-driven bandwidth choice for a kernel density estimator called critical ...
International audienceA data-driven bandwidth choice for a kernel density estimator called critical ...
International audienceA data-driven bandwidth choice for a kernel density estimator called critical ...
This paper establishes asymptotic lower bounds which provide limits, in various contexts, as to how ...
This paper establishes asymptotic lower bounds which provide limits, in various contexts, as to how ...
ABSTRACT. – In the usual right-censored data situation, let fn, n ∈ N, denote the convolution of the...
In this thesis, we are concerned with nonparametric estimation of the density given a random sample....
AbstractMultivariate kernel density estimators are known to systematically deviate from the true val...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
Abstract. Among available bandwidths for kernel density estimators, the critical bandwidth is a data...
Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven on...
International audienceA data-driven bandwidth choice for a kernel density estimator called critical ...
International audienceA data-driven bandwidth choice for a kernel density estimator called critical ...
International audienceA data-driven bandwidth choice for a kernel density estimator called critical ...
International audienceA data-driven bandwidth choice for a kernel density estimator called critical ...
This paper establishes asymptotic lower bounds which provide limits, in various contexts, as to how ...
This paper establishes asymptotic lower bounds which provide limits, in various contexts, as to how ...
ABSTRACT. – In the usual right-censored data situation, let fn, n ∈ N, denote the convolution of the...
In this thesis, we are concerned with nonparametric estimation of the density given a random sample....
AbstractMultivariate kernel density estimators are known to systematically deviate from the true val...