Bayes prediction in the linear model with spherically symmetric errors

  • S. Rao Jammalamadaka
  • , Ram C. Tiwari
  • , Siddhartha Chib

    Research output: Contribution to journalArticlepeer-review

    21 Scopus citations

    Abstract

    This paper is concerned with Bayes prediction in a linear regression model when the density of the observations is given by f(y|β,τ2)=∫z>0(2π)7minus; n 2τ2 n 2{ψ(z)-2} n 2 exp(- τ2 2ψ(z)-2||Y-Xβ||2)dG(z), where y ε{lunate} Rn, β ε{lunate} Rk, π2 > 0, Z is a positive random variable with distribution function G, ψ (.) is a positive function, and ||·|| denotes the Euclidean norm. We show that when prior information is objective or in the conjugate family, the Bayes prediction density is the same as that when the density of the observations is normal, for any Z.

    Original languageEnglish
    Pages (from-to)39-44
    Number of pages6
    JournalEconomics Letters
    Volume24
    Issue number1
    DOIs
    StatePublished - 1987

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