Advanced Search

The subject of this paper is to estimate adaptively the common probability density of $n$ independent, identically distributed random variables. The estimation is done at a fixed point $x_0\in ℝ$, over the density functions that belong to the Sobolev class $W_n(\beta ,L)$. We consider the adaptive problem setup, where the regularity parameter $\beta$ is unknown and varies in a given set $B_n$. A sharp adaptive estimator is obtained, and the explicit asymptotical constant, associated to its rate of convergence is found.

In this paper we consider a kernel estimator of a density in a convolution model and give a central limit theorem for its integrated square error (ISE). The kernel estimator is rather classical in minimax theory when the underlying density is recovered from noisy observations. The kernel is fixed and depends heavily on the distribution of the noise, supposed entirely known. The bandwidth is not fixed, the results hold for any sequence of bandwidths decreasing to 0. In particular the central limit...

The subject of this paper is to estimate adaptively the common probability density of independent, identically distributed random variables. The estimation is done at a fixed point $x_0\in ℝ$, over the density functions that belong to the Sobolev class . We consider the adaptive problem setup, where the regularity parameter is unknown and varies in a given set . A sharp adaptive estimator is obtained, and the explicit asymptotical constant, associated to its rate of convergence is found. ...

In the regression model with errors in variables, we observe i.i.d. copies of (, ) satisfying = ()+ and =+ involving independent and unobserved random variables , , plus a regression function , known up to a finite dimensional . The common densities of the ’s and of the ’s are unknown, whereas the distribution of is completely known. We aim at estimating the parameter by using the observations ( ...

In a convolution model, we observe random variables whose distribution is the convolution of some unknown density and some known noise density . We assume that is polynomially smooth. We provide goodness-of-fit testing procedures for the test : = , where the alternative is expressed with respect to ${𝕃}_2$-norm (i.e. has the form ${\psi }_n^{-2}{\parallel f-f_0\parallel }_2^2\ge 𝒞$). Our procedure is adaptive with respect to the unknown smoothness parameter of . Different testing rates ( ...