Weighted-average tail conjecture for independent random variables

From papers

Let XX and YY be independent random variables on R+\mathbb{R}_+ that are not compactly supported, and let λ(0,1)\lambda\in(0,1). Weighted-average tail conjecture.

lim supmP(λX+(1λ)Y>m)P(X>m)P(Y>m)=.\limsup_{m\to\infty}\frac{\mathbb{P}(\lambda X+(1-\lambda)Y>m)}{\mathbb{P}(X>m)\mathbb{P}(Y>m)}=\infty.

This conjecture is the main proposed extension from identically distributed variables to merely independent variables, and would imply the stated high-dimensional weighted-average result in the non-identically distributed case. The source gives no resolution, so the conjecture remains open.

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Sources & referencesView supporting material

Primary source

Naomi Dvora Feldheim and Ohad Noy Feldheim, “Mean and Minimum of Independent Random Variables”, arXiv:1609.03004 (2020).

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