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Additive inverse regression models with convolution-type operators
Inverse regression Additive models Convolution-type operators Mathematical subject codes: primary 62G08 secondary 62G15 62G20
2013/4/27
In a recent paper Birke and Bissantz (2008) considered the problem of nonparametric estimation in inverse regression models with convolution-type operators. For multivariate predictors nonparametric m...
Representation theorem for multiply self-decomposable probability measures on generalized convolution algebra
Representation theorem multiply self-decomposable probability measures
2009/9/24
Representation theorem for multiply self-decomposable probability measures on generalized convolution algebra。
A Levy Khintchine type representation of convolution semigroups on commutative hypergroups
A Levy Khintchine type representation convolution semigroups commutative hypergroups
2009/9/22
We first study L6vy measures, Poisson and Gaussian
convolution semigroups on commutative hypergroups. Then we present
a Lbvy-Khintchine type representation of a convolution semigroup
(pJ,,, with sy...
Decomposition of convolution semigroups on groups and the 0-1 law
Decomposition of convolution semigroups groups and the 0-1 law
2009/9/22
Let (X (t))a,o be a stochastically continuous symmetric
Levy process with values in a complete separable group G. We denote
by h),,,, the semigroup of one-dimensional distributions of X(t). Suppose
...
A KINGMAN CONVOLUTION APPROACH TO BESSEL PROCESSES
Kingman convolution radial characteristic function independent increment-type processes
2009/9/18
In this paper we study Bessel processes in terms of the Kingman
convolution method. In particular, we propose a higher dimensional
model of the Kingman convolution algebras.We show that every Bessel...
Adaptive estimation of linear functionals in the convolution model and applications
adaptive density estimation ARCH models deconvolution linear functionals model selection penalized contrast
2010/3/18
We consider the model Zi = Xi + εi, for i.i.d. Xi’s and εi’s and independent sequences (Xi)i2Nand (εi)i2N. The density f" of ε1 is assumed to be known, whereas the one of X1, denoted by
g, is unknown...
Adaptive procedures in convolution models with known or partially known noise distribution
Adaptive procedures convolution models noise distribution
2010/4/26
In a convolution model, we observe random variables whose distribution is the
convolution of some unknown density f and some known or partially known noise
density g. In this paper, we focus on stat...