Computing Finite Mixture Estimators in the Tails
Marilena Furno · Journal of Classification · 2023
Abstract The finite mixtures approach identifies homogeneous groups within the sample. The data are aggregated into classes sharing similar patterns without any prior knowledge or assumption on the clustering. These clusters are characterized by group-specific regression coefficients to account for between groups heterogeneity. Two different approaches have been independently defined in the literature to compute this estimator not only at the conditional mean but also in the tails. One approach allows the grouping to change according to the selected location. The other defines the clusters once and for all at the conditional mean, and then moves the estimation to the tails, focusing on cluster specific estimates and allowing between groups comparison. Here we compare the behavior of both approaches, and in addition we consider a closely related estimator based on expectiles, together with few others more robust, quantile-based estimators. A case study on students’ performance concludes the analysis.