A comparison of unconditional and conditional solutions to the maximum likelihood estimation of a change-point.
type: Item
instance of: scholarly article
title: A comparison of unconditional and conditional solutions to the maximum likelihood estimation of a change-point.
zbMATH Open document ID: 1043.62016
DOI: 10.1016/S0167-9473(99)00102-4
author: N. E. Evaggelopoulos
author: Venkata Jandhyala
author: Stergios Fotopoulos
published in: Computational Statistics and Data Analysis
publication date: 2000-10-26T00:00:00Z
review text: We compare the performance of unconditional and conditional solutions to the problem of estimating an unknown change-point by the method of maximum likelihood estimation. We begin by presenting some asymptotic results for the first two moments of the unconditional solution. Then, considering the cases of normal and exponential distributions, we evaluate through simulations the accuracy of large sample distribution theory results for the above two solutions through bias, mean square error, and graphical displays. In both normal and exponential cases, the unconditional solution shows better performance whenever the amount of change is small and when the true parameters are unknown. The differences are particular sharp when the true change-point is towards the tail of the data. When the amount of change is large, both methods perform at similar levels of accuracy. Finally, we illustrate the two solutions through data on British coal mine explosions.
Mathematics Subject Classification ID: 62F12
zbMATH DE Number: 1521706
zbMATH Keywords: Change-point
zbMATH Keywords: maximum likelihood estimate
zbMATH Keywords: random walk
MaRDI profile type: Publication
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full work available at URL: s0167-9473(99)00102-4
OpenAlex ID: W1999854570
description: scientific article; zbMATH DE number 1521706