Statistical inference for microarray experiments usually involves the estimation of error variance for each gene. Because the sample size available for each gene is often low, the usual unbiased estimator of the error variance can be unreliable. Shrinkage methods, including empirical Bayes approaches that borrow information across genes to produce more stable estimates, have been developed in r...

fgN and fARIMA (0,d,0) are two of the models frequently employed in Internet traffic. However, there is not a set of data traces available. In this work, a set of 187 fGN data traces each one of 64KPoints were generated with three tools: SPlus, SelQoS; and a fGN-MatlabScript; and a cumulative analysis with 1144 files was done. From this study, it is concluded that the best algorithms respect to...

This paper presents a method for fusing measurement samples from multiple sensors into a dependable robust estimation of a variable in the control environment. Each sensor measurement is represented by a measurement value and a confidence marker that corresponds to the respective variance of the measurement. We propose the Confidence-weighted Averaging (CWA) algorithm for fusing measurements wi...

We introduce a recursive algorithm for performing compressed sensing on streaming data. The approach consists of a) recursive encoding, where we sample the input stream via overlapping windowing and make use of the previous measurement in obtaining the next one, and b) recursive decoding, where the signal estimate from the previous window is utilized in order to achieve faster convergence in an...

This paper accurately quantifies the way in which noise induced estimation errors are dependent on model structure, underlying system frequency response, measurement noise and input excitation. This exposes several new principles. In particular, it is shown here that when employing Output–Error model structures in a prediction-error framework, then the ensuing estimate variability in the freque...