The use of electrocardiograms (ECGs) to diagnose and analyse atrial fibrillation (AF) has received much attention recently. When studying AF, it is important to isolate the atrial activity (AA) component of the ECG plot. We present a new autoregressive (AR) model for semiblind source extraction of the AA signal. Previous researchers showed that one could extract a signal with the smallest norma...

Estimation of the mean of the lognormal distribution has received much attention in the literature beginning with Finney (1941). The problem is of significant practical importance because of the ubiquitous use of log-transformation. In this paper we consider estimation of a parametric function associated with the lognormal distribution of which the mean, median and moments are special cases. We...

A decision theoretic foundation of the maximum likelihood estimation method is provided. In regular parametric models, the maximum likelihood estimator θ̂ is shown to be finite sample efficient for the parameter θ, with respect to the mean squared error of the scores and within a large class C of estimates; C includes for some of these models the unbiased, as well as the equivariant estimators o...

| Modeling a noisy time series requires the dual estimation of both the model parameters and the underlying clean time series. Most approaches estimate the model parameters by minimizing the mean squared prediction error, but estimate the time series by minimizing another cost function. We justify the use of the same maximum-likelihood cost for both parameter and time series estimation, and pre...

Empirical tests of forecast optimality have traditionally been conducted under the assumption of mean squared error loss or some other known loss function. This paper establishes new testable properties that hold when the forecaster’s loss function is unknown but testable restrictions can be imposed on the data generating process, trading off conditions on the data generating process against co...

Motor learning can be defined as changing performance so as to optimize some function of the task, such as accuracy. The measure of accuracy that is optimized is called a loss function and specifies how the CNS rates the relative success or cost of a particular movement outcome. Models of pointing in sensorimotor control and learning usually assume a quadratic loss function in which the mean sq...