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 ...

In this work, space-time processing for co-channel interference (CCI) reduction and intersymbol interference (ISI) equalization with antenna arrays is studied. As compared to spatial-only processing, the space-time processing proposed takes the temporal correlation of the CCI into account by modeling the noise and CCI as a Gaussian vector valued autoregressive process. This is a good approximat...

We study parameter estimation for discretely observed stochastic differential equations driven by small Levy noises. There have been many applications of small noise asymptotics to mathematical finance and insurance. Using small noise models we can deal with both applications and statistical inference. We do not impose Lipschitz condition on the dispersion coefficient function and any moment co...

The range and midrange are widely used, particularly in statistical quality control as an estimator of the dispersion tendency. In fact, the range itself is a very simple measure of dispersion, gives a quick and easy to estimate indication about the spread of data. The extremal quotient is used in climatic study, industrial quality control, life testing, small-area variation analysis and the cl...

We present a new method to parameterize Type Ia Supernovae (SN Ia) multi-color light curves. The method was developed in order to analyze the large number of SN Ia multi-color light curves measured in current high-redshift projects. The technique is based on empirically modeling SN Ia luminosity variations as a function of phase, wavelength, a shape parameter, and a color parameter. The model i...

We propose a new procedure to perform Reduced Rank Regression (RRR) in nonGaussian contexts, based on Multivariate Dispersion Models. Reduced-Rank Multivariate Dispersion Models (RR-MDM) generalise RRR to a very large class of distributions, which include continuous distributions like the normal, Gamma, Inverse Gaussian, and discrete distributions like the Poisson and the binomial. A multivaria...

Suppose that X is a d-dimensional elliptically distributed random vector with dispersion matrix Σ . Many multivariate statistical methods like principal component analysis, canonical correlation analysis, linear discriminant analysis, and multivariate regression require the covariance or dispersion matrix of X only up to a scaling constant. Hence, the present work concentrates on the shape matr...

in recent years, the study of soil contamination problems surrounding the mines and smelting plants has attracted the serious attention of the environmental experts. in iran, due to the mining and mine development, the study of environmental problems seems to be obligatory. the main point of this research is the implications of the statistical and geostatistical studies to evaluate and prepare ...

High-resolution N-body simulations are used to investigate systematic trends in the mass profiles and total masses of clusters as derived from 3 simple estimators: (1) the weak gravitational lensing shear field under the assumption of an isothermal cluster potential, (2) the dynamical mass obtained from the measured velocity dispersion under the assumption of an isothermal cluster potential, an...

state estimation is the foundation of any control and decision making in power networks. the first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. this paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...