In estimating a quantitative assay's lower limit of detection (LOD), standard deviation (SD) is the most common measure used to quantify the dispersion of the data, yet this LOD calculation method assumes that the low concentration samples follow a Gaussian distribution, which is not always true in reality. Here, a few LOD estimating methods that are based on different dispersion measures were ...

This study compared the outcome of students' performance using the standard setting method with the equivalent outcome they would have obtained using the absolute grading method. It involved the comparison of fail, pass, honors and distinction grades in Digestive System, Endocrine System, Cardiovascular System and Health and the Environment courses in the MBBS Stage I examination. The performan...

The median absolute deviation (MAD) of each trait (principal component) was used to measure within-line variance, as it is less sensitive to non-normality than other measures of spread [67, 68]. However, plotting the raw MADs of each line against the line median tended to show a dependency of the MAD on the median (Figure S2). To correct for this dependency, the residuals of a local (lowess) re...

Median regression analysis has robustness properties which make it an attractive alternative to regression based on the mean. In this paper, the missing information principle is applied to a right-censored version of the median regression model, leading to a new estimator for the regression parameters. Our approach adapts Efron's derivation of self-consistency for the Kaplan{Meier estimator to ...

Absolute deviation is utilized as a measure of risk and a new function is provided for it. We consider the mean-absolute deviation (MAD) portfolio optimization problem in a frictional market with additional constraints representing the socalled short sales. An algorithm for solving the optimization problem is thus presented, which uses the special structure of the original problem to reduce to ...

It is necessary to find or search for a way by which the important variables are selected be included in model studied. especially when study data suffers from cut-off point that occurs as result of an abnormal interruption phenomenon studied, leads division experimental units into two groups, where this gap Or jump values observations response variable, so we propose paper new method process e...

Microarray technology results in high-dimensional and low-sample size data sets. Therefore, fitting sparse models is substantial because only a small number of influential genes can reliably be identified. A number of variable selection approaches have been proposed for high-dimensional time-to-event data based on Cox proportional hazards where censoring is present. The present study applied th...