Distributional Characteristics: Just a Few More Moments.

The ability of statistical models to accurately characterize distributional characteristics such as skewness and kurtosis can impact the results of statistical analysis. This article compares the feasible skewness-kurtosis spaces for two generalizations of the lognormal, the inverse hyperbolic sine...

Descripción completa

Detalles Bibliográficos
Publicado en:American Statistician Vol. 65; no. 2; pp. 96 - 104
Autores principales: McDonald, James B., Turley, Patrick
Formato: Artículo
Publicado: American Statistical Association May 2011
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=508430711&site=ehost-live
header:
  @attributes:
    shortDbName: ssf
    uiTerm: 508430711
    longDbName: Social Sciences Full Text (H.W. Wilson)
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        00031305
        STT
      jtl: American Statistician
      issn: 00031305
      maglogo: N
    pubinfo:
      dt: May 2011
      vid: 65
      iid: 2
      pid: 543
      pub: American Statistical Association
    artinfo:
      ui:
        508430711
        10.1198/tast.2011.10022
      ppf: 96
      ppct: 8
      formats:
      tig:
        atl: Distributional Characteristics: Just a Few More Moments.
      aug:
        au:
          McDonald, James B.
          Turley, Patrick
      su: Distribution (Probability theory)
      sug:
        subj: Distribution (Probability theory)
      ab: The ability of statistical models to accurately characterize distributional characteristics such as skewness and kurtosis can impact the results of statistical analysis. This article compares the feasible skewness-kurtosis spaces for two generalizations of the lognormal, the inverse hyperbolic sine (IHS) and g-and- h probability density functions (pdf's), each of which can accommodate a wide variety of distributional characteristics. For h ≥ 0, the boundary of the skewness-kurtosis spaces for g-and- h and IHS coincides with that of a generalized (three-parameter) lognormal (LN*) distribution. The increased skewness-kurtosis flexibility of the g-and- h distribution, for h < 0, relative to the IHS is obtained by introducing vertical asymptotes, compact support, and possibly U-shaped pdf's. This increased coverage, however, may not be helpful if the data are unimodal. Empirical daily, weekly, and monthly stock return data are used to compare the descriptive ability of the IHS, g-and- h, and LN* distributions. Reprinted by permission of the publisher.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: N
    holdings:
      @attributes:
        islocal: N