A Unified Approach of Parameter Estimation

TL;DR

This paper presents a novel distance measure for parameter estimation that simplifies computations, handles truncated data seamlessly, and discretizes continuous cases, offering a unified estimation framework.

Contribution

It introduces a new distance for parameter estimation, providing a unified approach that simplifies calculations and accommodates truncated data without additional complexity.

Findings

The new distance effectively estimates parameters in continuous and truncated data scenarios.

The approach discretizes continuous models, facilitating easier computation.

It maintains accuracy comparable to traditional methods while simplifying the estimation process.

Abstract

We introduce a new distance and we use it to parameter estimation purposes. We observe how it operates and we use in its place the usual methods of estimation which we call the methods of the new approach. We realize that we obtain a discretization of the continuous case. Moreover, when it is necessary to consider truncated data nothing is changed in computations.