On Fourier coefficient estimators consistent in the mean-square sense

Waldemar Popiński¹

¹

Applicationes Mathematicae, Tome 22 (1993) no. 2, pp. 275-284.

Résumé

The properties of two recursive estimators of the Fourier coefficients of a regression function $f \in L^2[a,b]$ with respect to a complete orthonormal system of bounded functions (e_k) , k=1,2,..., are considered in the case of the observation model $y_i = f(x_i) + η_i$, i=1,...,n , where $η_i$ are independent random variables with zero mean and finite variance, $x_i \in [a,b] \subset {\sym R}^1$, i=1,...,n, form a random sample from a distribution with density ϱ =1/(b-a) (uniform distribution) and are independent of the errors $η_i$, i=1,...,n . Unbiasedness and mean-square consistency of the examined estimators are proved and their mean-square errors are compared.

Détail

DOI : 10.4064/am-22-2-275-284

Keywords: unbiasedness, consistent estimator, Fourier coefficients, mean-square error