This paper mainly deals with the problem of target detection in the presence of Compound-Gaussian (CG) clutter with the Inverse Gaussian (IG) texture and the unknown Power Spectral Density (PSD). The traditional CG distributions, in particular the K distribution and the complex multivariate t distribution, are widely used for modeling the real clutter data from the High-Resolution (HR) radars. ...

We construct a family of martingales with Gaussian marginal distributions. We give a weak construction as Markov, inhomogeneous in time processes, and compute their infinitesimal generators. We give the predictable quadratic variation and show that the paths are not continuous. The construction uses distributions Gσ having a logconvolution semigroup property. Further, we categorize these proces...

Using Beck and Cohen’s superstatistics, we introduce in a systematic way a family of generalised Wishart-Laguerre ensembles of random matrices with Dyson index β = 1,2, and 4. The entries of the data matrix are Gaussian random variables whose variances η fluctuate from one sample to another according to a certain probability density f(η) and a single deformation parameter γ. Three superstatisti...

in this paper, the effect of size on melting temperature of metallic nanoparticles (au, pb and bi) is theoretically simulated and explained. in this regard, the cause of difference in various experimental data is introduced, which is the difference between nanoparticles’ grain gaussian distribution. this volume-depended model with the help of the gaussian distribution can describe the relation ...

This paper presents two theoretical models to assess the variance of fatigue damage in stationary narrow-band and non-Gaussian stochastic processes. The extend solutions existing literature restricted Gaussian new here developed exploit a non-linear transformation that links domains based on skewness kurtosis coefficients, which are used quantify deviation from distribution. Monte Carlo numeric...

This paper aims to estimate the Value-at-Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of ...