Displaying similar documents to “Nonparametric estimation of probability density functions based on orthogonal expansions.”

Change-point estimation from indirect observations. 2. Adaptation

A. Goldenshluger, A. Juditsky, A. Tsybakov, A. Zeevi (2008)

Annales de l'I.H.P. Probabilités et statistiques

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We focus on the problem of adaptive estimation of signal singularities from indirect and noisy observations. A typical example of such a singularity is a discontinuity (change-point) of the signal or of its derivative. We develop a change-point estimator which adapts to the unknown smoothness of a nuisance deterministic component and to an unknown jump amplitude. We show that the proposed estimator attains optimal adaptive rates of convergence. A simulation study demonstrates reasonable...

On the problem of the means of weighted normal populations.

Mikhail S. Nikulin, Vassiliy G. Voinov (1995)

Qüestiió

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An analytical problem, which arises in the statistical problem of comparing the means of two normal distributions, the variances of which -as well as their ratio- are unknown, is well known in the mathematical statistics as the Behrens-Fisher problem. One generalization of the Behrens-Fisher problem and different aspect concerning the estimation of the common mean of several independent normal distributions with different variances are considered and one solution is proposed. ...

Asymptotic normality of the integrated square error of a density estimator in the convolution model.

Cristina Butucea (2004)

SORT

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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...

A note on orthogonal series regression function estimators

Waldemar Popiński (1999)

Applicationes Mathematicae

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The problem of nonparametric estimation of the regression function f(x) = E(Y | X=x) using the orthonormal system of trigonometric functions or Legendre polynomials e k , k=0,1,2,..., is considered in the case where a sample of i.i.d. copies ( X i , Y i ) , i=1,...,n, of the random variable (X,Y) is available and the marginal distribution of X has density ϱ ∈ L 1 [a,b]. The constructed estimators are of the form f ^ n ( x ) = ∑ k = 0 N ( n ) c ^ k e k ( x ) , where the coefficients c ^ 0 , c ^ 1 , . . . , c ^ N are determined by minimizing the empirical risk n - 1 ∑ i = 1 n ( Y i - ∑ k = 0 N c k e k ( X i ) ) 2 . Sufficient conditions...