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Local high-order polynomial fitting is employed for the estimation of the multivariate regression function m (x 1 , . . . ,xd) = E [ψ (Yd) | X 1 = x 1 , . . . ,Xd = xd], and of its partial derivatives, for stationary random processes {Yi , Xi}. The function ψ may be selected to yield estimates of the conditional mean, conditional moments and conditional(More)
The focus of this paper is on the performance of orthogonal frequency division multiplexing (OFDM) signals in mobile radio applications, such as 802.11a and digital video broadcasting (DVB) systems, e.g., DVB-CS2. The paper considers the evaluation of the error probability of an OFDM system transmitting over channels characterized by frequency selectivity(More)
This paper considers the estimation of the Fourier transform of continuous-time deterministic signals from a finite number N of discrete-time nonuniform observations. We first extend the recent results (mean and variance) of Tarczynski and Allay by providing both distributional properties as well as rates of almost sure convergence for the simple random(More)
The kernel-type estimation of the joint probability density functions of stationary random processes from noisy observations is considered. Precise asymptotic expressions and bounds on the mean-square estimation error are established, along with rates of mean-square convergence, for processes satisfying a variety of mixing conditions. The dependence of the(More)
Abs&sef-A new concept of alias-free sampling of continuous-t ime processes X=(X(t), -co<t<co} is introduced. TIE new concept is shown to be distinct from the traditional concept [lH3]. Various criteria for a given sampling scheme {a} to be alias-free in the new sense are developed. The relationship of the new defiition to the quest ion of estimating the(More)
We consider the problem of one-step-ahead prediction of a real-valued, stationary, strongly mixing random process fXig1i= 1. The best mean-square predictor of X0 is its conditional mean given the entire infinite past fXig 1 i= 1. Given a sequence of observations X1 X2 XN , we propose estimators for the conditional mean based on sequences of parametric(More)