IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v9y2015i4p423-445.html
   My bibliography  Save this article

Robust model-based clustering via mixtures of skew-t distributions with missing information

Author

Listed:
  • Wan-Lun Wang
  • Tsung-I Lin

Abstract

Multivariate mixture modeling approach using the skew-t distribution has emerged as a powerful and flexible tool for robust model-based clustering. The occurrence of missing data is a ubiquitous problem in almost every scientific field. In this paper, we offer a computationally flexible EM-type procedure for learning multivariate skew-t mixture models to deal with missing data under missing at random mechanisms. Further, we present an information-based approach to approximating the asymptotic covariance matrix of the maximum likelihood estimators using the outer product of the scores. To assist the development and ease the implementation of our algorithm, two auxiliary permutation matrices are utilized for fast determination of the observed and missing parts of each observation. The practical usefulness of the proposed methodology is illustrated through simulations with varying proportions of artificial missing values and a real data example with genuine missing values. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Wan-Lun Wang & Tsung-I Lin, 2015. "Robust model-based clustering via mixtures of skew-t distributions with missing information," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 423-445, December.
    • Handle: RePEc:spr:advdac:v:9:y:2015:i:4:p:423-445
    DOI: 10.1007/s11634-015-0221-y
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11634-015-0221-y
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11634-015-0221-y?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 search for a different version of it.

    References listed on IDEAS

    as
    1. M. C. Jones & M. J. Faddy, 2003. "A skew extension of the t‐distribution, with applications," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 159-174, February.
    2. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464.
    3. ., 1999. "The assessment of capital adequacy," Chapters, in: Handbook of Banking Regulation and Supervision in the United Kingdom, chapter 17, Edward Elgar Publishing.
    4. -, 2003. "Capital flows to Latin America: first quarter 2003," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28822, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    5. Sharon Lee & Geoffrey McLachlan, 2013. "Model-based clustering and classification with non-normal mixture distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 427-454, November.
    6. A. Azzalini & A. Capitanio, 1999. "Statistical applications of the multivariate skew normal distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 579-602.
    7. Boldea, Otilia & Magnus, Jan R., 2009. "Maximum Likelihood Estimation of the Multivariate Normal Mixture Model," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1539-1549.
    8. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    9. L. García-Escudero & A. Gordaliza & A. Mayo-Iscar, 2014. "A constrained robust proposal for mixture modeling avoiding spurious solutions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 27-43, March.
    10. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    11. -, 2003. "Capital flows to Latin America: second quarter 2002," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28812, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    12. Lounasheimo, Antton, 1999. "The Impact of Human Capital on Economic Growth," Discussion Papers 673, The Research Institute of the Finnish Economy.
    13. Álvarez Alvarado, Marcos Tulio, 2003. "¿Existe una alternativa al capitalismo?," Observatorio de la Economía Latinoamericana, Servicios Académicos Intercontinentales SL. Hasta 31/12/2022, issue 16, November.
    14. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
    15. -, 2003. "Capital flows to Latin America: first quarter 2002," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28811, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    16. -, 2003. "Capital flows to Latin America: fourth quarter 2002," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28814, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    17. Vrbik, Irene & McNicholas, Paul D., 2014. "Parsimonious skew mixture models for model-based clustering and classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 196-210.
    18. -, 2003. "Capital flows to Latin America: third quarter 2003," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28824, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    19. -, 2003. "Capital flows to Latin America: third quarter 2002," Oficina de la CEPAL en Washington (Estudios e Investigaciones) 28813, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    20. 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.
    21. Tsung-I Lin & Hsiu Ho & Pao Shen, 2009. "Computationally efficient learning of multivariate t mixture models with missing information," Computational Statistics, Springer, vol. 24(3), pages 375-392, August.
    22. Lin, Tsung-I, 2014. "Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 183-195.
    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. Hung Tong & Cristina Tortora, 2022. "Model-based clustering and outlier detection with missing data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 5-30, March.
    2. Wei, Yuhong & Tang, Yang & McNicholas, Paul D., 2019. "Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 18-41.
    3. Sahin, Özge & Czado, Claudia, 2022. "Vine copula mixture models and clustering for non-Gaussian data," Econometrics and Statistics, Elsevier, vol. 22(C), pages 136-158.
    4. Wan-Lun Wang & Tsung-I Lin, 2020. "Automated learning of mixtures of factor analysis models with missing information," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 1098-1124, December.
    5. Lachos, Víctor H. & Moreno, Edgar J. López & Chen, Kun & Cabral, Celso Rômulo Barbosa, 2017. "Finite mixture modeling of censored data using the multivariate Student-t distribution," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 151-167.
    6. Reyhaneh Rikhtehgaran & Iraj Kazemi, 2016. "The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 541-562, December.
    7. Francisco H. C. Alencar & Larissa A Matos & Víctor H. Lachos, 2022. "Finite Mixture of Censored Linear Mixed Models for Irregularly Observed Longitudinal Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 463-486, November.

    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. Mehdi Amiri & Ahad Jamalizadeh & Mina Towhidi, 2015. "Inference and further probabilistic properties of the $$ SUN_{n,2}$$ S U N n , 2 -distribution," Statistical Papers, Springer, vol. 56(4), pages 1071-1098, November.
    2. Wan-Lun Wang & Min Liu & Tsung-I Lin, 2017. "Robust skew-t factor analysis models for handling missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 649-672, November.
    3. McLachlan, Geoff & Lee, Sharon X, 2013. "EMMIXuskew: An R Package for Fitting Mixtures of Multivariate Skew t Distributions via the EM Algorithm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i12).
    4. Ahad Jamalizadeh & Tsung-I Lin, 2017. "A general class of scale-shape mixtures of skew-normal distributions: properties and estimation," Computational Statistics, Springer, vol. 32(2), pages 451-474, June.
    5. Komárek, Arnošt & Komárková, Lenka, 2014. "Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i12).
    6. Tao Lu, 2017. "Bayesian inference on longitudinal-survival data with multiple features," Computational Statistics, Springer, vol. 32(3), pages 845-866, September.
    7. Yangxin Huang & Tao Lu, 2017. "Bayesian inference on partially linear mixed-effects joint models for longitudinal data with multiple features," Computational Statistics, Springer, vol. 32(1), pages 179-196, March.
    8. John Marangos & Charles J. Whalen, 2011. "Evolution without fundamental change: the Washington Consensus on economic development," Chapters, in: Charles J. Whalen (ed.), Financial Instability and Economic Security after the Great Recession, chapter 8, pages 153-178, Edward Elgar Publishing.
    9. Tsung-I Lin & Pal Wu & Geoffrey McLachlan & Sharon Lee, 2015. "A robust factor analysis model using the restricted skew- $$t$$ t distribution," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 510-531, September.
    10. Alvaro Cuervo-Cazurra & Luis Alfonso Dau, 2009. "Structural Reform and Firm Exports," Management International Review, Springer, vol. 49(4), pages 479-507, September.
    11. Olcay Arslan, 2015. "Variance-mean mixture of the multivariate skew normal distribution," Statistical Papers, Springer, vol. 56(2), pages 353-378, May.
    12. Morris, Katherine & Punzo, Antonio & McNicholas, Paul D. & Browne, Ryan P., 2019. "Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 145-166.
    13. Wang, Wan-Lun, 2013. "Mixtures of common factor analyzers for high-dimensional data with missing information," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 120-133.
    14. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    15. Anna Gottard & Simona Pacillo, 2007. "On the impact of contaminations in graphical Gaussian models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 343-354, February.
    16. Azzalini, Adelchi & Browne, Ryan P. & Genton, Marc G. & McNicholas, Paul D., 2016. "On nomenclature for, and the relative merits of, two formulations of skew distributions," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 201-206.
    17. David Mayston, 2015. "Analysing the effectiveness of public service producers with endogenous resourcing," Journal of Productivity Analysis, Springer, vol. 44(1), pages 115-126, August.
    18. Diani, Cecilia & Galimberti, Giuliano & Soffritti, Gabriele, 2022. "Multivariate cluster-weighted models based on seemingly unrelated linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    19. Sylvia Frühwirth-Schnatter & Gertraud Malsiner-Walli, 2019. "From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 33-64, March.
    20. Wei, Yuhong & Tang, Yang & McNicholas, Paul D., 2019. "Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 18-41.

    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:advdac:v:9:y:2015:i:4:p:423-445. 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.