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Let $Y$ be a random variable defined on the domain $[1;\infty)$ that is distributed according to the cdf $G_Y(y)$.

A Pareto distribution,
$$ G_Y(y) = 1 - y^{-\theta}$$ has the property that $$ P(Y>ay) = 1 - G_Y(ay) = a^{-\theta} (1-G_Y(y)).$$ Similarly, the exponential distribution, $$ G_Y(y) = 1 - \exp(-\theta (y-1)).$$ has the property that $$ P(Y>y+b) = 1- G_Y(y+b) = \exp(b)^{-\theta} (1-G_Y(y)).$$

Is there any distribution that combines these two properties, i.e. where $$ P(Y>ay+b) = z(a,b) (1-G_Y(y)),$$ where $z(a,b)$ is some function of $a$ and $b$?

I expect this distribution to work for any real parameters $a$ or $b$ where $a$ is strictly positive and $b$ can be zero or negative. I expect $z(a,b)$ to be a function of both $a$ and $b$.

The solution should hold for all possible values of Y that are in the domain $[1;\infty)$, and any combination of $a$ and $b$ where $a$ is strictly positive. Of course, if a solution can only found for a subset of these, then that's better than nothing. One might has to restrict $a$ and $b$ such that $aY+b$ has the same domain as $Y$.

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    $\begingroup$ This varying, ambiguous notation is confusing: how are "$G(y)$" and "$G(y\gt aY)$" related? What kinds of mathematical objects do "$y$" and "$Y$" represent? What are the possible values of $a$ and $b$? What are the implicit quantifiers in your statements? What properties of the function $z$ do you require? (After all, $z$ doesn't have to change when either of $a$ or $b$ change.) $\endgroup$
    – whuber
    Commented Apr 22, 2020 at 13:25
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    $\begingroup$ Thanks for your comments, whuber. I have edited the question. Please let me know if it is clearer now. $\endgroup$
    – Christian
    Commented Apr 23, 2020 at 14:32
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    $\begingroup$ What you call a "Fréchet distribution" can be rewritten $G_Y(y)=1-\exp(-\theta y).$ Thus, depending on what you conceive as being its domain, this is an exponential distribution either for $y$ or $-y.$ Is there perhaps a typographical error somewhere? Also, the properties you quote are ill-defined: what are the implicit quantifiers on $y,$ $a,$ and $b$? $\endgroup$
    – whuber
    Commented Apr 27, 2020 at 13:43
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    $\begingroup$ Do these conditions need to hold for all $a$ and $b$ and $y$ or just for some of them? "All" and "some" are the kinds of quantifiers you need to supply to clarify your question. $\endgroup$
    – whuber
    Commented Apr 28, 2020 at 17:57
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    $\begingroup$ Ok. It should hold for all possible values of $Y$ that are in the domain $[1;\infty)$, and any combination of $a$ and $b$ where $a$ is strictly positive. Of course, if a solution can only found for a subset of these, then that's better than nothing. $\endgroup$
    – Christian
    Commented Apr 28, 2020 at 20:07

1 Answer 1

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The only possible distribution on $[1,\infty)$ satisfying the key equation above of \begin{align} P[&Y>ay+b]=z(a,b)\,P[Y>y]\\ &\text{ whenever } a>0, b\le 0, y\ge 1 \end{align} is the distribution with $P[Y=1]=1$, concentrated entirely at $y=1$.

If the distribution is not concentrated entirely at $Y=1$, then:

  • Let $s$ be such that $s>1$ and $P[Y>s]$ is positive.

  • Let $t$ be such that $t>s$ and $P[Y>t]<P[Y>s]$.

  • Let $r = \sqrt{s}$, so $1<r<s<t$, and $P[Y>r]$ is also positive.

  • Solve for $a$ and $b$ such that

\begin{align} ar+b&=r\\ as+b&=t\\ \end{align} Subtracting these two equations gives $a(s-r)=(t-r)$, so $a>1$, and that combined with the first of them gives $b<0$. Now:

The key equation for $y=r$ gives $P[Y>r]=z(a,b)P[Y>r]$, so $z(a,b)=1$.

The key equation for $y=s$ gives $P[Y>t]=z(a,b)P[Y>s]$, so $z(a,b)<1$.

This is a contradiction, so the distribution must have been entirely concentrated at $Y=1$.

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