Exact rates are derived for the uniform convergence of the density of intermediate order statistics towards the normal or lognormal density under certain smoothness conditions. Our methods also give the exact rate of convergence in the uniform metric and in the total variation metric.Statistics & ProbabilitySCI(E)3ARTICLE11-231
The present paper first shows that, without any dependent structure assumptions for a sequence of ra...
AbstractLet (Xn)nϵN be a sequence of real, independent, not necessarily identically distributed rand...
Abstract Let { X n , n ≥ 1 } $\{X_{n},n\geq1\}$ be an independent and identically distributed random...
The authors first derive the normal expansion of the joint density function of two order statistics ...
Intermediate order statistics, extreme order statistics, von Mises-type conditions, asymptotic norma...
We discuss the convergence of the moments of intermediate order statistics under power normalization...
The density estimator ＄f_{￥tau_{n}}(t)=T_{￥overline{n}^{1}}￥sum_{j=1}^{￥tau_{n}}h_{j}^{-1}K((t-X_{j}...
The property of the continuation of the convergence of the distribution function of intermediate ord...
In the present paper we prove a general theorem which gives the rates of convergence in distribution...
In the present paper we prove a general theorem which gives the rates of convergence in distribution...
AbstractLet Mn denote the partial maximum of an independent and identically distributed lognormal ra...
Let (Xn)n[epsilon] be a sequence of real, independent, not necessarily identically distributed rando...
We deal with quantile processes based on intermediate order statistics. Using an approximation of th...
Let be a sequence of independent and identically distributed -distributed random variables. Defi...
Abstract We establish the rate of convergence of distributions of sums of independent iden-tically d...
The present paper first shows that, without any dependent structure assumptions for a sequence of ra...
AbstractLet (Xn)nϵN be a sequence of real, independent, not necessarily identically distributed rand...
Abstract Let { X n , n ≥ 1 } $\{X_{n},n\geq1\}$ be an independent and identically distributed random...
The authors first derive the normal expansion of the joint density function of two order statistics ...
Intermediate order statistics, extreme order statistics, von Mises-type conditions, asymptotic norma...
We discuss the convergence of the moments of intermediate order statistics under power normalization...
The density estimator ＄f_{￥tau_{n}}(t)=T_{￥overline{n}^{1}}￥sum_{j=1}^{￥tau_{n}}h_{j}^{-1}K((t-X_{j}...
The property of the continuation of the convergence of the distribution function of intermediate ord...
In the present paper we prove a general theorem which gives the rates of convergence in distribution...
In the present paper we prove a general theorem which gives the rates of convergence in distribution...
AbstractLet Mn denote the partial maximum of an independent and identically distributed lognormal ra...
Let (Xn)n[epsilon] be a sequence of real, independent, not necessarily identically distributed rando...
We deal with quantile processes based on intermediate order statistics. Using an approximation of th...
Let be a sequence of independent and identically distributed -distributed random variables. Defi...
Abstract We establish the rate of convergence of distributions of sums of independent iden-tically d...
The present paper first shows that, without any dependent structure assumptions for a sequence of ra...
AbstractLet (Xn)nϵN be a sequence of real, independent, not necessarily identically distributed rand...
Abstract Let { X n , n ≥ 1 } $\{X_{n},n\geq1\}$ be an independent and identically distributed random...
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