# A Simple Point Estimator of the Power of Moments

## TL;DR

This paper introduces a simple estimator for the power of moments of a distribution, analyzes its asymptotic properties, and provides methods for hypothesis testing and practical examples.

## Contribution

It proposes a novel, straightforward estimator for the power of moments and investigates its consistency and asymptotic behavior under broad conditions.

## Key findings

- The estimator converges in probability to the true power of moments.
- Consistency holds under mild tail conditions on the distribution.
- Provides a formula for p-value calculation in hypothesis testing.

## Abstract

Let $X$ be an observable random variable with unknown distribution function $F(x) = \mathbb{P}(X \leq x), - \infty < x < \infty$, and let \[\ \theta = \sup\left \{ r \geq 0:~ \mathbb{E}|X|^{r} < \infty \right \}. \] We call $\theta$ the power of moments of the random variable $X$. Let $X_{1}, X_{2}, ..., X_{n}$ be a random sample of size $n$ drawn from $F(\cdot)$. In this paper we propose the following simple point estimator of $\theta$ and investigate its asymptotic properties: \[ \hat{\theta}_{n} = \frac{\log n}{\log \max_{1 \leq k \leq n} |X_{k}|}, \] where $\log x = \ln(e \vee x), ~- \infty < x < \infty$. In particular, we show that \[ \hat{\theta}_{n} \rightarrow_{\mathbb{P}} \theta~~\mbox{if and only if}~~ \lim_{x \rightarrow \infty} x^{r} \mathbb{P}(|X| > x) = \infty ~~\forall~r > \theta. \] This means that, under very reasonable conditions on $F(\cdot)$, $\hat{\theta}_{n}$ is actually a consistent estimator of $\theta$. Hypothesis testing for the power of moments is conducted and, as an application of our main results, the formula for finding the p-value of the test is given. In addition, a theoretical application of our main results is provided together with three illustrative examples.