Cluster sampling is a widely used sampling design for survey data selection. The collected data provide information on both the individual units and the clusters. The parameter of interest is an unknown population total at the unit level. Due to the cluster sampling design, the model-assisted...

In this note, we recollect some formulas and facts about linear mixed models in relation to Cohen’s effect size measure f2. It is shown how an estimate of the variance-covariance matrix for the estimated fixed effects parameter vector may serve to compute f2in the presence of random effects....

We discuss a bivariate beta distribution that can model arbitrary beta-distributed marginals with a positive correlation. The distribution is constructed from six independent gamma-distributed random variates. While previous work used an approximate and sometimes inaccurate method to compute the...

Markov chain Monte Carlo (MCMC) methods are sampling methods that have become a commonly used tool in statistics, for example to perform Monte Carlo integration. As a consequence of the increase in computational power, many variations of MCMC methods exist for generating samples from arbitrary,...

We discuss robust estimation of INARCH models for count time series, where each observation conditionally on its past follows a negative binomial distribution with a constant scale parameter, and the conditional mean depends linearly on previous observations. We develop several robust...

These remarks supplement the paper of Andreoli and Zoli from a practical view-point, that is, of a data analyst who wishes to compare distributions of socio-economic endowments regarding their inequality.