The traditional method of computing singular value decomposition (SVD) o...
Robust inference based on the minimization of statistical divergences ha...
In real life, we frequently come across data sets that involve some
inde...
Robust estimation under multivariate normal (MVN) mixture model is alway...
A basic algorithmic task in automated video surveillance is to separate
...
Penalized logistic regression is extremely useful for binary classificat...
The density power divergence (DPD) and related measures have produced ma...
Minimum divergence procedures based on the density power divergence and ...
Minimization of suitable statistical distances (between the data and mod...
Density-based minimum divergence procedures represent popular techniques...
Many real-life data sets can be analyzed using Linear Mixed Models (LMMs...
The semi-parametric Cox proportional hazards regression model has been w...
As in other estimation scenarios, likelihood based estimation in the nor...
In this paper a new family of minimum divergence estimators based on the...
The ordinary Bayes estimator based on the posterior density suffers from...
Preserving the robustness of the procedure has, at the present time, bec...
Health data are often not symmetric to be adequately modeled through the...
This paper presents new families of Rao-type test statistics based on th...
Entropy and cross-entropy are two very fundamental concepts in informati...
In this paper a new family of minimum divergence estimators based on the...
Cox proportional hazard regression model is a popular tool to analyze th...
Robust tests of general composite hypothesis under non-identically
distr...
The log-normal distribution is one of the most common distributions used...
We consider the problem of robust inference under the important generali...
In this work, a novel solution to the speaker identification problem is
...