Lomax distribution and asymptotical ML estimations based on record values for probability density function and cumulative distribution function

TL;DR

This paper compares maximum likelihood estimations based on record values and random samples for the Lomax distribution, providing asymptotic theorems to understand their behavior.

Contribution

It introduces asymptotic theorems for ML estimations based on record values for the Lomax distribution, a novel comparison with estimations from random samples.

Findings

ML estimations based on record values are asymptotically consistent.

Theorems describe the asymptotic behavior of these estimations.

Comparison highlights differences between record-based and sample-based estimations.

Abstract

Here in this paper, it is tried to obtain and compare the ML estimations based on upper record values and a random sample. In continue, some theorems have been proven about the behavior of these estimations asymptotically.