Many important statistics can be written as functions of sample means of vector variables. A fundamental contribution to the Edgeworth expansion for functions of sample means was made by Bhattacharya and Ghosh. In their work the crucial Cramer c-condition is assumed on the joint distribution of all the components of the vector variable. However, in many practical situations, only one or a few of the components satisfy (conditionally) this condition while the rest do not (such a case is referred to as satisfying the partial Cramer c-condition). The purpose of this paper is to establish Edgeworth expansions for functions of sample means when only the partial Cramer c-condition is satisfied
Let {Yn}n≥ 1 be a sequence of i.i.d. m-dimensional random vectors, and let f1,....., fk be rea...
This thesis is focused around Edgeworths expansion for aproximation of distribution for parameter es...
Under some regularity conditions on kernel, an asymptotic expansion with remainder term $ o(N^{-1}) ...
Many important statistics can be written as functions of sample means of vector variables. A fundame...
We derive the Edgeworth expansion to order n-1 of the cumulative distribution function of the studen...
International audienceWe introduce a new, weak Cramer condition on the characteristic function of a ...
We derive the Edgeworth expansion to order n-1 of the cumulative distribution function of the studen...
AbstractThe validity of formal Edgeworth expansions for statistics which are functions of sample ave...
Bloznelis M, Götze F. Edgeworth approximations for distributions of symmetric statistics. Probabilit...
The validity of formal Edgeworth expansions for statistics which are functions of sample averages wa...
Bloznelis M, Götze F. An edgeworth expansion for finite-population U-statistics. BERNOULLI. 2000;6(4...
We consider asymptotically normal statistics which are symmetric functions of N i.i.d. random variab...
Bentkus V, Götze F, van Zwet WR. An Edgeworth expansion for symmetric statistics. ANNALS OF STATISTI...
This thesis is focused around Edgeworth's expansion for approximation of distribution for parameter ...
Cramér's condition, Edgeworth expansion, linear process, spectral mean estimates, Whittle estimates,
Let {Yn}n≥ 1 be a sequence of i.i.d. m-dimensional random vectors, and let f1,....., fk be rea...
This thesis is focused around Edgeworths expansion for aproximation of distribution for parameter es...
Under some regularity conditions on kernel, an asymptotic expansion with remainder term $ o(N^{-1}) ...
Many important statistics can be written as functions of sample means of vector variables. A fundame...
We derive the Edgeworth expansion to order n-1 of the cumulative distribution function of the studen...
International audienceWe introduce a new, weak Cramer condition on the characteristic function of a ...
We derive the Edgeworth expansion to order n-1 of the cumulative distribution function of the studen...
AbstractThe validity of formal Edgeworth expansions for statistics which are functions of sample ave...
Bloznelis M, Götze F. Edgeworth approximations for distributions of symmetric statistics. Probabilit...
The validity of formal Edgeworth expansions for statistics which are functions of sample averages wa...
Bloznelis M, Götze F. An edgeworth expansion for finite-population U-statistics. BERNOULLI. 2000;6(4...
We consider asymptotically normal statistics which are symmetric functions of N i.i.d. random variab...
Bentkus V, Götze F, van Zwet WR. An Edgeworth expansion for symmetric statistics. ANNALS OF STATISTI...
This thesis is focused around Edgeworth's expansion for approximation of distribution for parameter ...
Cramér's condition, Edgeworth expansion, linear process, spectral mean estimates, Whittle estimates,
Let {Yn}n≥ 1 be a sequence of i.i.d. m-dimensional random vectors, and let f1,....., fk be rea...
This thesis is focused around Edgeworths expansion for aproximation of distribution for parameter es...
Under some regularity conditions on kernel, an asymptotic expansion with remainder term $ o(N^{-1}) ...