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Concavity and Convexity of Order Statistics in Sample Size

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  • Mitchell Watt

Abstract

We show that the expectation of the $k^{\mathrm{th}}$-order statistic of an i.i.d. sample of size $n$ from a monotone reverse hazard rate (MRHR) distribution is convex in $n$ and that the expectation of the $(n-k+1)^{\mathrm{th}}$-order statistic from a monotone hazard rate (MHR) distribution is concave in $n$ for $n\ge k$. We apply this result to the analysis of independent private value auctions in which the auctioneer faces a convex cost of attracting bidders. In this setting, MHR valuation distributions lead to concavity of the auctioneer's objective. We extend this analysis to auctions with reserve values, in which concavity is assured for sufficiently small reserves or for a sufficiently large number of bidders.

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  • Mitchell Watt, 2021. "Concavity and Convexity of Order Statistics in Sample Size," Papers 2111.04702, arXiv.org, revised Jan 2022.
  • Handle: RePEc:arx:papers:2111.04702
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    References listed on IDEAS

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    1. Milgrom,Paul, 2004. "Putting Auction Theory to Work," Cambridge Books, Cambridge University Press, number 9780521536721.
    2. David, H. A., 1997. "Augmented order statistics and the biasing effect of outliers," Statistics & Probability Letters, Elsevier, vol. 36(2), pages 199-204, December.
    3. Roger B. Myerson, 1981. "Optimal Auction Design," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 58-73, February.
    4. Ramesh Gupta & N. Balakrishnan, 2012. "Log-concavity and monotonicity of hazard and reversed hazard functions of univariate and multivariate skew-normal distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(2), pages 181-191, February.
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