The g-and-h distributional family is generated from a relatively simple transformation of the standard normal and can approximate a broad spectrum of distributions. Consequently, it is easy to use in simulation studies and has been applied in multiple areas, including risk management, stock...

In this work, we develop the modeling and estimation approach for the analysis of cross-sectional clustered data with multimodal conditional distributions, where the main interest is in analysis of subpopulations. It is proposed to model such data in a hierarchical model with conditional...

The family of weighted likelihood estimators largely overlaps with minimum divergence estimators. They are robust to data contaminations compared to MLE. We define the class of generalized weighted likelihood estimators (GWLE), provide its influence function and discuss the efficiency...

We present an orthonormal bases approach for detecting general differences among continuous distributions. An unknown density function is represented by a finite vector of its estimated Fourier coefficients with respect to a suitable orthonormal basis. For a wide class of orthonormal bases, we...