Tail Lower Bounds using Moment Generating Functions Given a random variable $X>0$ with Moment Generating Function $m(s)=E[e^{sX}]$ I'm interested in finding a lower bound
$$\Pr[X \ge t] \ge 1-\varepsilon(t),$$
where $t>E[X]$.
A classic technique for finding upper bounds for $\Pr[X \ge t]$ is using Markov's inequality with the Moment Generating Function:
$$\Pr[X \ge t] \le E[e^{sX}]e^{-st},$$
for $s\ge 0$.
Since $E[e^{s X}]\ge e^{s E[X]}$ (Jensen's inequality), this bound is useful whenever $t>E[X]$. (I'm assuming $X$ is a positive random variable, so $E[X]>0$.)
Standard techniques for lower bounds include the second-moment method, such as Cantelli's inequality and Paley-Zygmund. However, they only cover the case $t\in[0,E[X]]$ and say nothing about the case $t>E[X]$.
My best bet so far has been Lévy's and Gil-Pelaez's inversion formulas for the Characteristic Function $m(i s)$:
$$\begin{align}\Pr[X \ge t] &= 1-\frac{1} {2\pi} \lim_{S \to \infty} \int_{-S}^{S} \frac{1 - e^{-ist}} {is}\, m(is)\, ds.
\\\text{and}\quad
\Pr[X \ge t] &= \frac{1}{2} + \frac{1}{\pi}\int_0^\infty \frac{\operatorname{Im}[e^{-ist}m(i s)]}{s}\,ds.\end{align}$$
However, I don't have any good ideas for bounding those in a useful way. It certainly seems a lot harder than the Markov method for the upper bound.
Am I missing a useful approach here?
 A: Actually this is not so hard to do with Paley-Zygmund. Not sure why I didn't see this before. We just substitute $X$ by $e^{s X}$ like in Chernoff's bound:
$$
\Pr[e^{sX} > \theta E[e^{sX}]]
 = \Pr[X > \log(\theta E[e^{sX}])/s]
 \ge (1-\theta)^2\frac{E[e^{s X}]}{E[e^{2 s X}]}.
$$
For an example with the normal distribution, where $E[e^{s X}]=e^{s^2/2}$ we get
$$
\Pr[X > \log(\theta)/s + s/2]
 \ge (1-\theta)^2 e^{-\tfrac{3}{2}s^2},
$$
so taking $\log(\theta)/s+s/2=t$ and $\theta=1/2$ we get
$$
\Pr[X > t]
 \ge \frac{1}{4} e^{-\left(\sqrt{t^2+\log (4)}+t\right)^2} \sim e^{-6t^2}.
$$
Indeed, the value of $\theta$ matters very little.
We can compare this result to the true asymptotics for the normal distribution
$
\Pr[X > t] \sim e^{-t^2/2}
$ to see that we lose roughly a factor 12 in the exponent.
Alternatively for $t\to0$ we get $\Pr[X>0]\ge1/16$.
We can improve the lower bound a bit to $\sim t^{O(1)} e^{-2t^2}$ using the $L^p$ version of Paley-Zygmund, but there's still a gap.
This differs from the situation of the upper bound (Markov/Chernoff) which is tight in the exponent.
If there's a way to get a tight exponent using moment generating functions, I'm still very interested.
