IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v66y2025i1d10.1007_s00362-024-01658-7.html
   My bibliography  Save this article

Nonparametric Bayesian inferences on the skewed data using a Dirichlet process mixture model

Author

Listed:
  • Amin Ghalamfarsa Mostofi

    (Shiraz University)

  • Mahmood Kharrati-Kopaei

    (Shiraz University)

Abstract

This paper presents a new mixture model that can be regarded as a modified version of the Dirichlet process normal mixture models. In this model, the component distribution depends on a parameter whose value affects directly the skewness of the population distribution. Unlike the usual normal mixture model, one can impose prior information on the skewness parameter and make inferences. A nonparametric Bayesian approach is proposed to make inferences about the parameters of the model, including mean, variance, mode, and skewness parameters. An example is given to illustrate the use of the proposed mixture model in testing symmetry and fitting a distribution to data. We also compare our proposed method with two existing methods in terms of mean squared error and mean integrated squared error of the predictive density estimation.

Suggested Citation

  • Amin Ghalamfarsa Mostofi & Mahmood Kharrati-Kopaei, 2025. "Nonparametric Bayesian inferences on the skewed data using a Dirichlet process mixture model," Statistical Papers, Springer, vol. 66(1), pages 1-24, January.
  • Handle: RePEc:spr:stpapr:v:66:y:2025:i:1:d:10.1007_s00362-024-01658-7
    DOI: 10.1007/s00362-024-01658-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-024-01658-7
    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/s00362-024-01658-7?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. A. Ghalamfarsa Mostofi & M. Kharrati-Kopaei, 2012. "Bayesian nonparametric inference for unimodal skew-symmetric distributions," Statistical Papers, Springer, vol. 53(4), pages 821-832, November.
    2. Mingming Chen & Jianghong Ma & Yee Leung & Hector Gomez, 2022. "The Slash Power Normal Distribution with Application to Pollution Data," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, February.
    3. Caroline C. Vieira & Rosangela H. Loschi & Denise Duarte, 2015. "Nonparametric Mixtures Based on Skew-normal Distributions: An Application to Density Estimation," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(8), pages 1552-1570, April.
    4. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    5. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
    6. Nadarajah, Saralees & Kotz, Samuel, 2003. "Skewed distributions generated by the normal kernel," Statistics & Probability Letters, Elsevier, vol. 65(3), pages 269-277, November.
    7. Tao Chen & Julian Morris & Elaine Martin, 2006. "Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(5), pages 699-715, November.
    Full references (including those not matched with items on IDEAS)

    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. Kerenaftali Klein & Stefanie Hennig & Sanjoy Ketan Paul, 2016. "A Bayesian Modelling Approach with Balancing Informative Prior for Analysing Imbalanced Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-12, April.
    2. John K. Kruschke, 2021. "Bayesian Analysis Reporting Guidelines," Nature Human Behaviour, Nature, vol. 5(10), pages 1282-1291, October.
    3. Chen, Kefei & O'Leary, Rebecca A. & Evans, Fiona H., 2019. "A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool," Agricultural Systems, Elsevier, vol. 173(C), pages 140-150.
    4. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    5. Hancock, Joana & Vieira, Sara & Lima, Hipólito & Schmitt, Vanessa & Pereira, Jaconias & Rebelo, Rui & Girondot, Marc, 2019. "Overcoming field monitoring restraints in estimating marine turtle internesting period by modelling individual nesting behaviour using capture-mark-recapture data," Ecological Modelling, Elsevier, vol. 402(C), pages 76-84.
    6. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    7. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2001. "Comparing dynamic equilibrium economies to data," FRB Atlanta Working Paper 2001-23, Federal Reserve Bank of Atlanta.
    8. Lada, Emily K. & Wilson, James R., 2006. "A wavelet-based spectral procedure for steady-state simulation analysis," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1769-1801, November.
    9. Enver Yücesan, 1993. "Randomization tests for initialization bias in simulation output," Naval Research Logistics (NRL), John Wiley & Sons, vol. 40(5), pages 643-663, August.
    10. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    11. Atahan Afsar; José Elías Gallegos; Richard Jaimes; Edgar Silgado Gómez & José Elías Gallegos & Richard Jaimes & Edgar Silgado Gómez, 2020. "Reconciling Empirics and Theory: The Behavioral Hybrid New Keynesian Model," Vniversitas Económica, Universidad Javeriana - Bogotá, vol. 0(0), pages 1-41, December.
    12. Bai, Yizhou & Xue, Cheng, 2021. "An empirical study on the regulated Chinese agricultural commodity futures market based on skew Ornstein-Uhlenbeck model," Research in International Business and Finance, Elsevier, vol. 57(C).
    13. Burda Martin & Bélisle Louis, 2019. "Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo," Dependence Modeling, De Gruyter, vol. 7(1), pages 133-149, January.
    14. Yu, Jun, 2012. "A semiparametric stochastic volatility model," Journal of Econometrics, Elsevier, vol. 167(2), pages 473-482.
    15. Goldman Elena & Tsurumi Hiroki, 2005. "Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-38, June.
    16. Andr'es Ram'irez-Hassan & Alejandro L'opez-Vera, 2021. "Semi-parametric estimation of the EASI model: Welfare implications of taxes identifying clusters due to unobserved preference heterogeneity," Papers 2109.07646, arXiv.org.
    17. Chen, Tao & Martin, Elaine & Montague, Gary, 2009. "Robust probabilistic PCA with missing data and contribution analysis for outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3706-3716, August.
    18. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Kiel Working Papers 1799, Kiel Institute for the World Economy (IfW Kiel).
    19. Mai Dao & Lam Nguyen, 2025. "Variable selection in macroeconomic stress test: a Bayesian quantile regression approach," Empirical Economics, Springer, vol. 68(3), pages 1113-1169, March.
    20. Michael T. Owyang, 2002. "Modeling Volcker as a non-absorbing state: agnostic identification of a Markov-switching VAR," Working Papers 2002-018, Federal Reserve Bank of St. Louis.

    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:stpapr:v:66:y:2025:i:1:d:10.1007_s00362-024-01658-7. 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.