We consider kernel estimators of the instantaneous frequency of a slowly evolving sinusoid in white noise. The expected estimation error consists of two terms. The systematic bias error grows as the kernel halfwidth increases while the random error decreases. For a nonmodulated signal, g(t), the kernel halfwidth which minimizes the expected error scales as h ∼ [ σ2 N |∂2 t g |2 ]1/5 , where σ2 ...

In this paper, we develop approximations for the symbol error rate in a wireless code-division multiple-access channel. We assume that each user employs spectrally efficient M-ary quadrature amplitude modulation and undergoes independent Rayleigh fading. We study the performance of linear minimum mean-squared error receivers in situations where: i) the channels of all users are known perfectly;...

The aim of many microarray experiments is to build discriminatory diagnosis and prognosis models. Given the huge number of features and the small number of examples, model validity which refers to the precision of error estimation is a critical issue. Previous studies have addressed this issue via the deviation distribution (estimated error minus true error), in particular, the deterioration of...

This paper presents a simple and direct approach to understanding the threshold effect associated with maximum likelihood estimation of 'he frequency of a single complex tone. Motivation for the approach, stemming from known results in the field of phase locked loops, is given. It is shown both theoretically and experimentally that the onset of threshold can be directly characterized by a singl...

In the wrapper approach for feature selection, a popular criterion used is the leave-one-out estimate of the classification error. While being relatively unbiased, the leave-one-out error estimate is nonetheless known to exhibit a large variance, which can be detrimental especially for small samples. We propose reducing its variance (i.e. smoothing) at two levels. At the first level, we smooth ...

This paper deals with the estimation of linear dynamic models of the ARMA type for the conditional mean for time series with conditionally heteroskedastic innovation process widely used in modelling financial time series. Estimation is performed using subspace methods which are known to have computational advantages as compared to prediction error methods based on criterion minimization. These ...

Algorithms for retrieval of geophysical parameters from radiances measured by instruments onboard satellites play a large role in helping scientists monitor the state of the planet. Current retrieval algorithms based on neural networks are superior in accuracy and speed compared to physics-based algorithms like iterated minimum variance (IMV). However, they do not have any form of error estimat...