Though ubiquitous, Likert scaling's traditional mode of analysis is often unable to uncover all
of the valid information in a data set. Here, the authors discuss a solution to this problem …

The Expectation–Maximization (EM) algorithm is a popular tool in a wide variety of statistical
settings, in particular in the maximum likelihood estimation of parameters when clustering …

Likert scaling is one of the oldest and most widely used methods in behavioral science
research, and is one of the key methodologies for attitude measurement. Arguably, there are …

We present an alternating least squares (ALS) type numerical optimization scheme to
estimate conditionally independent mixture models in, without parametrizing the …

In this article, based on progressively Type-II censored samples from a heterogeneous
population that can be represented by a finite mixture of two-component Rayleigh lifetime …

The importance of global environmental questions has significantly advanced the impact of
climate change phenology. Whilst spatial applications continue to be a core application of …

The distributions of empirical data are often complex. Such complexity cannot be sufficiently
addressed by the individual theoretical statistical distribution function. Furthermore, the …

Incomplete data is a common problem of survey research. Recent work on multiple
imputation techniques has increased analysts' awareness of the biasing effects of missing …

Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific
studies. There are numerous articles that show how to fit Poisson and other models which …

We propose a kernel method to identify finite mixtures of nonparametric product
distributions. It is based on a Hilbert space embedding of the joint distribution. The rank of …