I have stumbled upon a rather intriguing inequality involving the product of the scaled distribution and the scaled density of a random variable. The inequality has a very attractive form, and it resembles, somehow, Cauchy–Schwarz inequality. The problem goes as follows.


### The Inequality


Let $X$ be a continuous and non-negative random variable. Denote by $\bar F(x)= \mathbb P \{X>x\}$ the complementary cumulative distribution function (ccdf), and $f(x)$ its probability density function (pdf). Given two scaling factors $a\_1,a\_2\ge1$; consider the *scaled* version of the density function $f\_i(\cdot) = f(a\_i \cdot)$, and the complementary distribution $\bar F\_i(\cdot) = \bar F(a\_i \cdot)$.


We need to show that



> 
> $$
> \langle f\_1, \bar F\_1 \rangle \langle f\_2, \bar F\_2 \rangle \ge \langle f\_1, \bar F\_2 \rangle \langle f\_2, \bar F\_1 \rangle,
> $$
> 
> 
> 


where $\langle u, v \rangle = \int\_0^\infty u(x)v(x)w(x)\,dx$ is the inner product with weight $w(\cdot)\ge0$. We can assume that all functions are square integrable w.r.t. to the weight.


Has anybody problem seen such an inequality? It has quite an appealing form, but I could neither prove it nor find a counterexample. Any reference or ideas would be appreciated!! 


### Remarks


1. The latter holds when the hazard rate of the random variable $X$ is homogeneous, that is, the hazard rate $h(x) = f(x) / \bar F(x)$ satisfies $h(a x) = a^n h(x)$ for some $n$. In this case, it suffices to write $f\_i(\cdot) = a\_i^n h(\cdot) \bar F\_i(\cdot)$ and use Cauchy–Schwarz. The exponential, weibull, and pareto distributions have homogeneous hazard rates.
2. It tried numerically with other distributions and it seems to hold.


### Edit: Counter-example


Anthony Quas has provided an excellent counter-example (see his answer below) for general weights. Actually, I was a looking for a particular weight function, which is given by $$w(x) = x \exp(-c\_1 \bar F\_1(x) - c\_2 \bar F\_2(x)).$$ Since, the inequality holds for the case of homogeneous hazard rates, I was hoping that it will hold in full generality. Shame on me! Anyway, hope that somebody has some thoughts on this. It would be much appreciated.



A Cauchy–Schwarz Type Inequality Involving Scaled Distributions
What you're asking translates to the following:


Is it true that for all bounded differentiable decreasing functions $F(x)$ with $F(\infty)=0$, all positive weight functions $w(x)$ and all positive $a$ and $b$, that
$$
\int\_{-\infty}^\infty F(ax)|F'(ax)|w(x)\,dx\int\_{-\infty}^\infty F(bx)|F'(bx)|w(x)\,dx
$$


$$
\ge\int\_{-\infty}^\infty F(ax)|F'(bx)|w(x)\,dx\int\_{-\infty}^\infty F(bx)|F'(ax)|w(x)\,dx
$$


For that to be true for all $w(x)$, it would have to be true for all positive measures. We'll get a counterexample taking $w(x)dx=\delta\_1+\delta\_{10}$. Let's take $a=1$ and $b=2$. 


Then the left side is $[F(1)|F'(1)|+F(10)|F'(10)|][F(2)|F'(2)|+F(20)|F'(20)|]$ while the right side is $[F(1)|F'(2)|+F(10)|F'(20)|][F(2)|F'(1)|+F(20)|F'(10)|]$.


Let $F(1)=4$, $F(2)=3$, $F(10)=2$ and $F(20)=1$ (you can scale it later if you want to make it be a reverse cdf. Now choose the density so that $f(1)=3$, $f(2)=4$, $f(10)=1$ and $f(20)=2$. 


The left side is then $(12+2)\*(12+2)=196$ while the right side is $(16+4)(9+1)=200$


