• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Scale and shape mixtures of multivariate skew-normal distributions

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    1-s2.0-S0047259X17303937-main.pdf
    Size:
    721.3Kb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Type
    Article
    Authors
    Arellano-Valle, Reinaldo B.
    Ferreira, Clécio S.
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2018-02-26
    Online Publication Date
    2018-02-26
    Print Publication Date
    2018-07
    Permanent link to this record
    http://hdl.handle.net/10754/627215
    
    Metadata
    Show full item record
    Abstract
    We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.
    Citation
    Arellano-Valle RB, Ferreira CS, Genton MG (2018) Scale and shape mixtures of multivariate skew-normal distributions. Journal of Multivariate Analysis. Available: http://dx.doi.org/10.1016/j.jmva.2018.02.007.
    Sponsors
    This research was supported by Fondecyt (Chile)1120121 and 1150325, and by the King Abdullah University of Science and Technology (KAUST) . We thank the Editor, Associate Editor and four anonymous reviewers for comments that improved the paper. We also thank Prof. Adelchi Azzalini for suggesting Proposition 1 during a seminar presentation of this work at the University of Padova and Prof. Mauricio Castro for some initial discussions on the topic of this paper.
    Publisher
    Elsevier BV
    Journal
    Journal of Multivariate Analysis
    DOI
    10.1016/j.jmva.2018.02.007
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S0047259X17303937
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jmva.2018.02.007
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      Characteristic functions of scale mixtures of multivariate skew-normal distributions

      Kim, Hyoung-Moon; Genton, Marc G. (Journal of Multivariate Analysis, Elsevier BV, 2011-08) [Article]
      We obtain the characteristic function of scale mixtures of skew-normal distributions both in the univariate and multivariate cases. The derivation uses the simple stochastic relationship between skew-normal distributions and scale mixtures of skew-normal distributions. In particular, we describe the characteristic function of skew-normal, skew-t, and other related distributions. © 2011 Elsevier Inc.
    • Thumbnail

      Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution

      Krupskii, Pavel; Joe, Harry; Lee, David; Genton, Marc G. (Journal of Multivariate Analysis, Elsevier BV, 2017-11-02) [Article]
      The multivariate Hüsler–Reiß copula is obtained as a direct extreme-value limit from the convolution of a multivariate normal random vector and an exponential random variable multiplied by a vector of constants. It is shown how the set of Hüsler–Reiß parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the Hüsler–Reiß copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the Hüsler–Reiß copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.
    • Thumbnail

      Normalized differential method for improving the signal-to-noise ratio of a distributed acoustic sensor

      Ashry, Islam; Mao, Yuan; Alias, Mohd Sharizal; Ng, Tien Khee; Hveding, Frode; Arsalan, Muhammad; Ooi, Boon S. (Applied Optics, The Optical Society, 2019-06-14) [Article]
      We experimentally introduce a normalized differential method to enhance the time domain signal-to-noise ratio (SNR) of an optical fiber distributed acoustic sensor (DAS). The reported method is calibrated against the typical differential method in noisy DAS systems, including those utilizing a relatively wide linewidth laser or few-mode fiber. In these two systems, the normalized differential method respectively identifies the position information of various vibration events with 1.7 dB and 0.53 dB SNR improvement. We further demonstrate the ability to locate positions along a fiber that are subjected to vibrations of frequencies higher than the theoretical maximum, but without determining these frequencies.
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.