IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i6d10.1007_s00180-021-01177-1.html

Robust order selection of mixtures of regression models with random effects

Author

Listed:
  • Luísa Novais

    (University of Minho)

  • Susana Faria

    (University of Minho)

Abstract

Finite mixtures of regression models with random effects are a very flexible statistical tool to model data, as these models allow to model the heterogeneity of the population and to account for multiple correlated observations from the same individual at the same time. The selection of the number of components for these models has been a long-standing challenging problem in statistics. However, the majority of the existent methods for the estimation of the number of components are not robust and, therefore, are quite sensitive to outliers. In this article we study a robust estimation of the number of components for mixtures of regression models with random effects, investigating the performance of trimmed information and classification criteria comparatively to the performance of the traditional information and classification criteria. The simulation study and a real-world application showcase the superiority of the trimmed information and classification criteria in the presence of contaminated data.

Suggested Citation

  • Luísa Novais & Susana Faria, 2025. "Robust order selection of mixtures of regression models with random effects," Computational Statistics, Springer, vol. 40(6), pages 3205-3228, July.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:6:d:10.1007_s00180-021-01177-1
    DOI: 10.1007/s00180-021-01177-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-021-01177-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-021-01177-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Meng Li & Sijia Xiang & Weixin Yao, 2016. "Robust estimation of the number of components for mixtures of linear regression models," Computational Statistics, Springer, vol. 31(4), pages 1539-1555, December.
    2. Francis K.C. Hui & David I. Warton & Scott D. Foster, 2015. "Order selection in finite mixture models: complete or observed likelihood information criteria?," Biometrika, Biometrika Trust, vol. 102(3), pages 724-730.
    3. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    4. Joseph E. Cavanaugh, 2004. "Criteria for Linear Model Selection Based on Kullback's Symmetric Divergence," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 46(2), pages 257-274, June.
    5. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    6. Cavanaugh, Joseph E., 1999. "A large-sample model selection criterion based on Kullback's symmetric divergence," Statistics & Probability Letters, Elsevier, vol. 42(4), pages 333-343, May.
    7. Chun Yu & Weixin Yao & Guangren Yang, 2020. "A Selective Overview and Comparison of Robust Mixture Regression Estimators," International Statistical Review, International Statistical Institute, vol. 88(1), pages 176-202, April.
    8. Derek S. Young & David R. Hunter, 2015. "Random effects regression mixtures for analyzing infant habituation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1421-1441, July.
    9. Hiroyuki Kasahara & Katsumi Shimotsu, 2015. "Testing the Number of Components in Normal Mixture Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1632-1645, December.
    10. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
    11. Koller, Manuel, 2016. "robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i06).
    12. Nicolas Depraetere & Martina Vandebroek, 2014. "Order selection in finite mixtures of linear regressions," Statistical Papers, Springer, vol. 55(3), pages 871-911, August.
    13. Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David Fernando Muñoz & Verónica Andrea González-López & Jürgen Symanzik, 2025. "Editorial on the special issue on the V Latin American conference on statistical computing," Computational Statistics, Springer, vol. 40(6), pages 2849-2856, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nicolas Depraetere & Martina Vandebroek, 2014. "Order selection in finite mixtures of linear regressions," Statistical Papers, Springer, vol. 55(3), pages 871-911, August.
    2. Meng Li & Sijia Xiang & Weixin Yao, 2016. "Robust estimation of the number of components for mixtures of linear regression models," Computational Statistics, Springer, vol. 31(4), pages 1539-1555, December.
    3. Marianna Virtanen & Jussi Vahtera & Jenny Head & Rosemary Dray-Spira & Annaleena Okuloff & Adam G Tabak & Marcel Goldberg & Jenni Ervasti & Markus Jokela & Archana Singh-Manoux & Jaana Pentti & Marie , 2015. "Work Disability among Employees with Diabetes: Latent Class Analysis of Risk Factors in Three Prospective Cohort Studies," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    4. Danks, Nicholas P. & Sharma, Pratyush N. & Sarstedt, Marko, 2020. "Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM)," Journal of Business Research, Elsevier, vol. 113(C), pages 13-24.
    5. Morgan, Grant B. & Hodge, Kari J. & Baggett, Aaron R., 2016. "Latent profile analysis with nonnormal mixtures: A Monte Carlo examination of model selection using fit indices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 146-161.
    6. Xiongya Li & Xiuqin Bai & Weixing Song, 2025. "Robust mixture of linear mixed modeling via multivariate Laplace distribution," Computational Statistics, Springer, vol. 40(8), pages 4209-4230, November.
    7. Aline Riboli Marasca & Maurício Scopel Hoffmann & Anelise Reis Gaya & Denise Ruschel Bandeira, 2021. "Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(3), pages 1121-1137, June.
    8. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    9. Omar N. Solinger & Woody van Olffen & Robert A. Roe & Joeri Hofmans, 2013. "On Becoming (Un)Committed: A Taxonomy and Test of Newcomer Onboarding Scenarios," Organization Science, INFORMS, vol. 24(6), pages 1640-1661, December.
    10. Mengya Xia & Caitlin M. Hudac, 2023. "Social Connection Constellations and Individual Well-Being Typologies: Using the Loglinear Modeling Approach with Latent Variables," Journal of Happiness Studies, Springer, vol. 24(6), pages 1991-2012, August.
    11. Laura Dal Corso & Alessandro De Carlo & Francesca Carluccio & Daiana Colledani & Alessandra Falco, 2020. "Employee burnout and positive dimensions of well-being: A latent workplace spirituality profile analysis," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-17, November.
    12. à lvaro García del Castillo-López & María Berenguer-Soler & David Pineda, 2025. "Relationship Love Styles’ Effects on Conflict, Emotional Intelligence, and Sexual Satisfaction: A Latent Profile Analysis," SAGE Open, , vol. 15(3), pages 21582440251, July.
    13. dos Santos, Fabio Luis Marques & Duboz, Amandine & Grosso, Monica & Raposo, María Alonso & Krause, Jette & Mourtzouchou, Andromachi & Balahur, Alexandra & Ciuffo, Biagio, 2022. "An acceptance divergence? Media, citizens and policy perspectives on autonomous cars in the European Union," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 224-238.
    14. Stephens, Christina M. & Crosby, Danielle A. & Mendez Smith, Julia, 2024. "Accessibility of the early care and education supply: Variation within the center-based provider sector," Children and Youth Services Review, Elsevier, vol. 164(C).
    15. repec:jss:jstsof:06:i02 is not listed on IDEAS
    16. Yang, Chih-Chien, 2006. "Evaluating latent class analysis models in qualitative phenotype identification," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1090-1104, February.
    17. Eun Seo Park & Young Il Cho, 2025. "Classifying Stalking Persons in South Korea into Sub-Groups Based on Urgent Emergency Measure Checklist," SAGE Open, , vol. 15(3), pages 21582440251, September.
    18. Michael T. Baglivio & Kevin T. Wolff, 2021. "Adverse Childhood Experiences Distinguish Violent Juvenile Sexual Offenders’ Victim Typologies," IJERPH, MDPI, vol. 18(21), pages 1-14, October.
    19. Md. Matiar Rahman & Mahbubul Muttakin & Animesh Pal & Abu Zar Shafiullah & Bidyut Baran Saha, 2019. "A Statistical Approach to Determine Optimal Models for IUPAC-Classified Adsorption Isotherms," Energies, MDPI, vol. 12(23), pages 1-34, November.
    20. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2008. "Determining the Number of Market Segments Using an Experimental Design," FEP Working Papers 263, Universidade do Porto, Faculdade de Economia do Porto.
    21. Marhuenda, Yolanda & Morales, Domingo & del Carmen Pardo, María, 2014. "Information criteria for Fay–Herriot model selection," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 268-280.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:40:y:2025:i:6:d:10.1007_s00180-021-01177-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.