IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v41y2003i3-4p429-440.html
   My bibliography  Save this article

Mixture model clustering for mixed data with missing information

Author

Listed:
  • Hunt, Lynette
  • Jorgensen, Murray

Abstract

No abstract is available for this item.

Suggested Citation

  • Hunt, Lynette & Jorgensen, Murray, 2003. "Mixture model clustering for mixed data with missing information," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 429-440, January.
    • Handle: RePEc:eee:csdana:v:41:y:2003:i:3-4:p:429-440
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(02)00190-1
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. 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.
    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. Di Zio, Marco & Guarnera, Ugo & Luzi, Orietta, 2007. "Imputation through finite Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5305-5316, July.
    2. Adelchi Azzalini & Giovanna Menardi, 2016. "Density-based clustering with non-continuous data," Computational Statistics, Springer, vol. 31(2), pages 771-798, June.
    3. Chauveau, Didier & Hoang, Vy Thuy Lynh, 2016. "Nonparametric mixture models with conditionally independent multivariate component densities," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 1-16.
    4. Woodward, Wayne A. & Sain, Stephan R., 2003. "Testing for outliers from a mixture distribution when some data are missing," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 193-210, October.
    5. Jing Xiao & Qiongqiong Xu & Chuanli Wu & Yuexia Gao & Tianqi Hua & Chenwu Xu, 2016. "Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.
    6. 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.
    7. Bohning, Dankmar & Seidel, Wilfried, 2003. "Editorial: recent developments in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 349-357, January.
    8. Marco Di Zio & Ugo Guarnera, 2009. "Semiparametric predictive mean matching," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 93(2), pages 175-186, June.
    9. Christophe Genolini & Bruno Falissard, 2010. "KmL: k-means for longitudinal data," Computational Statistics, Springer, vol. 25(2), pages 317-328, June.
    10. Reddy, Chandan K. & Rajaratnam, Bala, 2010. "Learning mixture models via component-wise parameter smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 732-749, March.
    11. Wasito, Ito & Mirkin, Boris, 2006. "Nearest neighbours in least-squares data imputation algorithms with different missing patterns," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 926-949, February.

    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. Julian Aichholzer & Sylvia Kritzinger & Carolina Plescia, 2021. "National identity profiles and support for the European Union," European Union Politics, , vol. 22(2), pages 293-315, June.
    2. Adrian Bruhin & Ernst Fehr & Daniel Schunk, 2019. "The many Faces of Human Sociality: Uncovering the Distribution and Stability of Social Preferences," Journal of the European Economic Association, European Economic Association, vol. 17(4), pages 1025-1069.
    3. Jacky C. K. Ng & Joanne Y. H. Chong & Hilary K. Y. Ng, 2023. "The way I see the world, the way I envy others: a person-centered investigation of worldviews and the malicious and benign forms of envy among adolescents and adults," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    4. Gillian C. Williams & Karen A. Patte & Mark A. Ferro & Scott T. Leatherdale, 2021. "Associations between Longitudinal Patterns of Substance Use and Anxiety and Depression Symptoms among a Sample of Canadian Secondary School Students," IJERPH, MDPI, vol. 18(19), pages 1-14, October.
    5. Mélissa Lemoine & Gerhard Gmel & Simon Foster & Simon Marmet & Joseph Studer, 2020. "Multiple trajectories of alcohol use and the development of alcohol use disorder: Do Swiss men mature-out of problematic alcohol use during emerging adulthood?," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    6. Sarstedt, Marko & Salcher, André, 2007. "Modellselektion in Finite Mixture PLS-Modellen," Discussion Papers in Business Administration 1394, University of Munich, Munich School of Management.
    7. Lebret, Rémi & Iovleff, Serge & Langrognet, Florent & Biernacki, Christophe & Celeux, Gilles & Govaert, Gérard, 2015. "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i06).
    8. Ellen Bouchery & Monica Farid, "undated". "Variation in Staff Salary Costs Associated with Characteristics of Substance Use Disorder Treatment Facilities," Mathematica Policy Research Reports 65b1484724354c0ca8270d1c6, Mathematica Policy Research.
    9. Wang, Kun & Marbut, Alexander R. & Suntai, Zainab & Zheng, Dianhan & Chen, Xiayu, 2022. "Patterns in older adults' perceived chronic stressor types and cognitive functioning trajectories: Are perceived chronic stressors always bad?," Social Science & Medicine, Elsevier, vol. 311(C).
    10. Tolu O Oyesanya & Roger L Brown & Lyn S Turkstra, 2017. "Caring for Patients with traumatic brain injury: a survey of nurses' perceptions," Journal of Clinical Nursing, John Wiley & Sons, vol. 26(11-12), pages 1562-1574, June.
    11. Anne Mäkikangas & Wilmar Schaufeli & Esko Leskinen & Ulla Kinnunen & Katriina Hyvönen & Taru Feldt, 2016. "Long-Term Development of Employee Well-Being: A Latent Transition Approach," Journal of Happiness Studies, Springer, vol. 17(6), pages 2325-2345, December.
    12. Yedith B. Guillén-Fernández, 2024. "Socioeconomic Factors Determining Multidimensional Child Poverty Groups in Central America: A Measurement Proposal from the Wellbeing Approach Using a Comprehensive Set of Children’s Rights," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 17(5), pages 2175-2217, October.
    13. Thomas Bassetti & Raul Caruso & Darwin Cortes, 2015. "Behavioral differences in violence: The case of intra-group differences of Paramilitaries and Guerrillas in Colombia," DISCE - Quaderni del Dipartimento di Politica Economica ispe0073, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    14. Joanna F. Dipnall & Belinda J. Gabbe & Warwick J. Teague & Ben Beck, 2020. "Identifying Homogeneous Patterns of Injury in Paediatric Trauma Patients to Improve Risk-Adjusted Models of Mortality and Functional Outcomes," IJERPH, MDPI, vol. 17(3), pages 1-20, January.
    15. Jeffrey H Dorfman & Christian Gregory & Zhongyuan Liu & Ran Huo, 2019. "Re-Examining the SNAP Benefit Cycle Allowing for Heterogeneity," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 41(3), pages 404-433.
    16. Andrew Clark & Fabien Postel-Vinay, 2009. "Job security and job protection," Oxford Economic Papers, Oxford University Press, vol. 61(2), pages 207-239, April.
    17. McCarthy, Molly & Ogilvie, James M. & Allard, Troy, 2022. "Exploring trajectories of offender harm: An alternative approach to understanding offending pathways over the life-course," Journal of Criminal Justice, Elsevier, vol. 82(C).
    18. Paul Nailly & Etienne Côme & Latifa Oukhellou & Allou Samé & Jacques Ferriere & Yasmine Merad-Boudia, 2024. "Multivariate count time series segmentation with “sums and shares” and Poisson lognormal mixture models: a comparative study using pedestrian flows within a multimodal transport hub," 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. 18(2), pages 455-491, June.
    19. Christian Schellhase & Göran Kauermann, 2012. "Density estimation and comparison with a penalized mixture approach," Computational Statistics, Springer, vol. 27(4), pages 757-777, December.
    20. 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.

    More about this item

    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:eee:csdana:v:41:y:2003:i:3-4:p:429-440. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.