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Computing Finite Mixture Estimators in the Tails

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  • Marilena Furno

    (Università degli Studi di Napoli Federico II)

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.

Suggested Citation

  • Marilena Furno, 2023. "Computing Finite Mixture Estimators in the Tails," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 267-297, July.
    • Handle: RePEc:spr:jclass:v:40:y:2023:i:2:d:10.1007_s00357-023-09433-3
    DOI: 10.1007/s00357-023-09433-3
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    References listed on IDEAS

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    1. Bartolucci, F. & Scaccia, L., 2005. "The use of mixtures for dealing with non-normal regression errors," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 821-834, April.
    2. Bai, Xiuqin & Yao, Weixin & Boyer, John E., 2012. "Robust fitting of mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2347-2359.
    3. Giovanni Compiani & Yuichi Kitamura, 2016. "Using mixtures in econometric models: a brief review and some new results," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 95-127, October.
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