TIT
2002
13 years 11 months ago
2002
We consider a two-dimensional problem of positron emission tomography where the random mechanism of the generation of the tomographic data is modeled by Poisson processes. The goa...
SAC
2002
ACM
13 years 11 months ago
2002
ACM
Estimating multivariate location and scatter with both affine equivariance and positive breakdown has always been difficult. A well-known estimator which satisfies both properties...
NECO
2002
13 years 11 months ago
2002
In the presence of a heavy-tail noise distribution, regression becomes much more di cult. Traditional robust regression methods assume that the noise distribution is symmetric and...
NECO
2000
13 years 11 months ago
2000
The Bayesian committee machine (BCM) is a novel approach to combining estimators which were trained on different data sets. Although the BCM can be applied to the combination of a...
CCR
2002
13 years 11 months ago
2002
This paper presents a trace-driven simulation study of two classes of retransmission timeout (RTO) estimators in the context of realtime streaming over the Internet. We explore th...
TWC
2008
13 years 11 months ago
2008
This letter addresses the problem of joint estimation of carrier frequency offset (CFO) and channel for OFDM systems in the presence of timing ambiguity. Based on two signal models...
TSP
2008
13 years 11 months ago
2008
In this paper, we address the joint data-aided estimation of frequency offsets and channel coefficients in uplink MIMO-OFDMA systems. As the Maximum Likelihood (ML) estimator is i...
TIP
2008
13 years 11 months ago
2008
Abstract--Image denoising methods are often designed to minimize mean-squared error (MSE) within the subbands of a multiscale decomposition. However, most high-quality denoising re...
TASLP
2008
13 years 11 months ago
2008
We propose a new family of Bayesian estimators for speech enhancement where the cost function includes both a power law and a weighting factor. The parameters of the cost function,...
PR
2006
13 years 11 months ago
2006
A cross-validation error estimator is obtained by repeatedly leaving out some data points, deriving classifiers on the remaining points, computing errors for these classifiers on ...