A constant problem is to localize a number of acoustic sources, to separate their individual signals and to estimate their strengths in a propagation medium. An acoustic receiving array with signal processing algorithms is then used. The most widely used algorithm is the conventional beamforming algorithm but it has a very low resolution and high sidelobes that may cause a signal leakage proble...

The role and importance of the industrial sector in the economic development specify the necessity of having accurate and timely data for exact planning. As outliers data in establishment surveys are common due to the structure of the economy, the evaluation of survey data by identifying and investigating outliers prior to the release of data is necessary. In this paper the practical applicatio...

This article develops tests of covariance restrictions after estimating by three-stage least squares a dynamic random effects model from panel data. The asymptotic distribution of covariance matrix estimates under non-normality is obtained. It is shown how minimum chi-square tests for interesting covariance restrictions can be calculated from a generalised linear regression involving the sample...

The sparse inverse covariance estimation problem is commonly solved using an l1-regularizedGaussian maximum likelihood estimator known as “graphical lasso”, but its computational cost becomes prohibitive for large data sets. A recent line of results showed–under mild assumptions–that the graphical lasso estimator can be retrieved by soft-thresholding the sample covariance matrix and solving a m...

Robust regression tools are commonly used to develop regression-type ratio estimators with traditional measures of location whenever data contaminated outliers. Recently, the researchers extended this idea and developed through robust minimum covariance determinant (MCD) estimation. In study, quantile MCD-based is utilized a class mean proposed. The squared errors (MSEs) proposed also obtained....

In Cator and Lopuhaä [3] an asymptotic expansion for the MCD estimators is established in a very general framework. This expansion requires the existence and non-singularity of the derivative in a first-order Taylor expansion. In this paper, we prove the existence of this derivative for multivariate distributions that have a density and provide an explicit expression. Moreover, under suitable s...

For a zero mean, proper, complex random vector x, the Hermitian covariance Exx is a complete second-order characterization. However, if the vector x is improper, it is correlated with its complex conjugate, meaning Exx 6= 0. This improper or complementary covariance must be accounted for in a complete second-order characterization. The improper covariance has been exploited for widely linear (W...

Explicit expressions for the second order statistics of cepstral components representing clean and noisy signal waveforms are derived. The noise is assumed additive to the signal, and the spectral components of each process are assumed statistically independent complex Gaussian random variables. The key result developed here is an explicit expression for the cross-covariance between the log-per...

A classical problem in many radar and sonar applications is the adaptive detection/esti-mation of a given signal in the presence of zero mean Gaussian noise. Reed, Mallett, and Brennan (RMB) derived and analyzed an adaptive detection scheme where the noise adaptation and non-trivial nature of their analysis resulted from the use of a noise sample covariance matrix (SCM). The case now considered...