On the estimation of a parameter with incomplete knowledge on a nuisance parameter

TL;DR

This paper explores methods for estimating a parameter when the nuisance parameter's information varies from complete knowledge to only median knowledge, introducing a new criterion for the median case.

Contribution

It introduces a novel estimation criterion based on the median of the likelihood, addressing a gap where classical tools are lacking.

Findings

Classical tools work for known and prior-distributed nuisance parameters.

Maximum entropy principle helps in cases with limited moments.

A new median-based criterion is proposed for incomplete knowledge scenarios.

Abstract

In this paper we consider the problem of estimating a parameter of a probability distribution when we have some prior information on a nuisance parameter. We start by the very simple case where we know perfectly the value of the nuisance parameter. The complete likelihood is the classical tool in this case. Then, progressively, we consider the case where we are given a prior probability distribution on this nuisance parameter. The marginal likelihood is then the classical tool in this case. Then, we consider the case where we only have a fixed number of its moments. Here, we may use the maximum entropy (ME) principle to assign a prior law and thus go back to the previous case. Finally, we consider the case where we know only its median. In our knowledge, there is not any classical tool for this case. We propose then a new tool for this case based on a recently proposed alternative distribution to the marginal probability distribution. This new criterion is obtained by first remarking that the marginal distribution can be considered as the mean value of the original distribution over the prior probability law of the nuisance parameter, and then, by using the median in place of the mean. In this paper, we first summarize the classical tools used for the three first cases, then we give the precise definition of this new criterion and its properties and, finally, present a few examples to show the differences of these cases. Key Words: Nuisance parameter, Bayesian inference, Maximum Entropy, Marginalization, Incomplete knowledge, Mean and Median of the Likelihood over the prior distribution