Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "10403"
  #_source: array:26 [
    "id" => "10403"
    "slug" => "nonparametric-density-estimation-for-multivariate-bounded-data"
    "yearMonth" => "2010-01"
    "year" => "2010"
    "title" => "Nonparametric Density Estimation for Multivariate Bounded Data"
    "description" => "BOUEZMARNI, T. et ROMBOUTS, J. (2010). Nonparametric Density Estimation for Multivariate Bounded Data. <i>Journal of Statistical Planning and Inference</i>, 140(1), pp. 139-152."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "ROMBOUTS Jeroen"
        "bid" => "B00469813"
        "slug" => "rombouts-jeroen"
      ]
      1 => array:1 [
        "name" => "BOUEZMARNI Taoufik"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:6 [
      0 => "Asymmetric kernels"
      1 => "Multivariate boundary bias"
      2 => "Nonparametric multivariate density estimation"
      3 => "Asymptotic properties"
      4 => "Bandwidth selection"
      5 => "Least squares cross-validation"
    ]
    "updatedAt" => "2021-07-13 14:31:34"
    "publicationUrl" => "https://doi.org/10.1016/j.jspi.2009.07.013"
    "publicationInfo" => array:3 [
      "pages" => "139-152"
      "volume" => "140"
      "number" => "1"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "We propose a new nonparametric estimator for the density function of multivariate bounded data. As frequently observed in practice, the variables may be partially bounded (e.g. nonnegative) or completely bounded (e.g. in the unit interval). In addition, the variables may have a point mass. We reduce the conditions on the underlying density to a minimum by proposing a nonparametric approach. By using a gamma, a beta, or a local linear kernel (also called boundary kernels), in a product kernel, the suggested estimator becomes simple in implementation and robust to the well known boundary bias problem. We investigate the mean integrated squared error properties, including the rate of convergence, uniform strong consistency and asymptotic normality. We establish consistency of the least squares cross-validation method to select optimal bandwidth parameters. A detailed simulation study investigates the performance of the estimators. Applications using lottery and corporate finance data are provided."
      "en" => "We propose a new nonparametric estimator for the density function of multivariate bounded data. As frequently observed in practice, the variables may be partially bounded (e.g. nonnegative) or completely bounded (e.g. in the unit interval). In addition, the variables may have a point mass. We reduce the conditions on the underlying density to a minimum by proposing a nonparametric approach. By using a gamma, a beta, or a local linear kernel (also called boundary kernels), in a product kernel, the suggested estimator becomes simple in implementation and robust to the well known boundary bias problem. We investigate the mean integrated squared error properties, including the rate of convergence, uniform strong consistency and asymptotic normality. We establish consistency of the least squares cross-validation method to select optimal bandwidth parameters. A detailed simulation study investigates the performance of the estimators. Applications using lottery and corporate finance data are provided."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T08:21:48.000Z"
    "docTitle" => "Nonparametric Density Estimation for Multivariate Bounded Data"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/rombouts-jeroen">ROMBOUTS Jeroen</a>, BOUEZMARNI Taoufik"
    "docDescription" => "<span class="document-property-authors">ROMBOUTS Jeroen, BOUEZMARNI Taoufik</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2010</span>"
    "keywordList" => "<a href="#">Asymmetric kernels</a>, <a href="#">Multivariate boundary bias</a>, <a href="#">Nonparametric multivariate density estimation</a>, <a href="#">Asymptotic properties</a>, <a href="#">Bandwidth selection</a>, <a href="#">Least squares cross-validation</a>"
    "docPreview" => "<b>Nonparametric Density Estimation for Multivariate Bounded Data</b><br><span>2010-01 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1016/j.jspi.2009.07.013" target="_blank">Nonparametric Density Estimation for Multivariate Bounded Data</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.613594
  +"parent": null
}