A challenge when interpreting replications is determining whether the results of a replication "successfully" replicate the original study. Looking for consistency between two studies is challenging because individual studies are susceptible to many sources of error that can cause study results to deviate from each other and the population effect in unpredictable directions and magnitudes. In t...

Cross-validation based point estimates of prediction accuracy are frequently reported in microarray class prediction problems. However these point estimates can be highly variable, particularly for small sample numbers, and it would be useful to provide confidence intervals of prediction accuracy. We performed an extensive study of existing confidence interval methods and compared their perform...

In this paper we compare several ADPCM schemes with nonlinear prediction based on neural nets with the classical ADPCM schemes based on several linear prediction schemes. Main studied variations of the ADPCM scheme with adaptive quantization (2 to 5 bits) are: -forward vs backward -sample adaptive vs block adaptive

A closed-loop power control (CLPC) scheme with a multistep (indicating multiple prediction steps) linear autoregressive predictor is presented. The proposed CLPC relies on low-rate sample vector based autoregressive prediction. Compared to currently available predictive CLCP schemes, it demonstrates particularly robust performance in the presence of large loop delays and channel estimation errors.

This paper presents a prediction error variance reduction procedure based on fractional digital differentiation with negative order. This reduction is achieved by increasing correlation in the signals. Applications to ECG signals show that savings of more than one bit per residual signal sample can be attained. Keywords—Linear prediction, fractional differentiation, ECG signal.