A new adaptive switching algorithm is presented where two adaptive filters are switched correspondingly for lower and higher noise ratio of the image. An adaptive center weighted vector median filter is used for the lower noise ratio whereas for higher noise ratio the noisy pixels are detected based on the comparison of the difference between the mean of the vector pixels in the window and the ...

We discuss the prediction of the sample variance of marks of a marked spatial point process on a continuous space by the threshold method. The threshold method is a statistical prediction using only the number of points with marks exceeding a given threshold value. Mase (1996) considered the method in the framework of spatial point processes on a discrete space and Sakaguchi and Mase (2003) ext...

Substantial additive genetic variance (V(A)) often exists for male signalling traits in spite of the directional selection that female choice imposes. One solution to this problem, a conundrum generally termed the 'lek paradox', is that genotype × environment interaction (GEI) occurs and generates a 'crossover' of reaction norms in which no one genotype performs in a superior manner in all envi...

It is tempting to assume that confounding bias is eliminated by choosing controls that are identical to the cases on the matched confounder(s). We used causal diagrams to explain why such matching not only fails to remove confounding bias, but also adds colliding bias, and why both types of bias are removed by conditioning on the matched confounder(s). As in some publications, we trace the logi...

The residuals of a least squares regression model are defined as the observations minus the modeled values. For least squares regression to produce valid CIs and P values, the residuals must be independent, be normally distributed, and have a constant variance. If these assumptions are not satisfied, estimates can be biased and power can be reduced. However, there are ways to assess these assum...

The problem of the sample variance computation for epistemic inter-val-valued data is, in general, NP-hard. Therefore, known efficient algorithms for computing variance require strong restrictions on admissible intervals like the no-subset property or heavy limitations on the number of possible intersections between intervals. A new asymptotic algorithm for computing the upper bound of the samp...