Sampling designs for estimation of a random process

Abstract

A random process X(t), t ∈ [0, 1], is sampled at a finite number of appropriately designed points. On the basis of these observations, we estimate the values of the process at the unsampled points and we measure the performance by an integrated mean square error. We consider the case where the process has a known, or partially or entirely unknown mean, i.e., when it can be modeled as X(t) = m(t)+ N(t), where m(t) is nonrandom and N(t) is random with zero mean and known covariance function. Specifically, we consider (1) the case where m(t) is known, (2) the semiparametric case where m(t) = β1f1(t)+ . + βqfq(t), the βi's are unknown coefficients and the fi's are known regression functions, and (3) the nonparametric case where m(t) is unknown. Here fi(t) and m(t) are of comparable smoothness with the purely random part N(t), and N(t) has no quadratic mean derivative