Tests for constancy of model parameters Over time
摘要
Suppose that a sequence of data points follows a distribution of a certain parametric form, but that one or more of the underlying parameters may change over time. This paper addresses various natural questions in such a framework. We construct canonical monitoring processes which under the hypothesis of no change converge in distribution to independent Brownian bridges, and use these to construct natural goodness-of-fit statistics. Weighted versions of these are also studied, and optimal weight functions are derived to give maximum local power against alternatives of interest. We also discuss how our results can be used to pinpoint where and what type of changes have occurred, in the event that initial screening tests indicate that such exist. Our unified large-sample methodology is quite general and applies to all regular parametric models, including regression, Markov chains, and time series situations.
引用
@article{arxiv.2605.16335,
title = {Tests for constancy of model parameters Over time},
author = {Nils Lid Hjort and Alex J. Koning},
journal= {arXiv preprint arXiv:2605.16335},
year = {2026}
}
备注
23 pages, 3 figures. This is a Statistical Research Report, Department of Mathematics, University of Oslo, from 2001, containing some more material than for the published version, in Journal of Nonparametric Statistics, 2002, vol. 14, pages 113-132. NLH honours Alex Koning (1959-2022) by making these Hjort-Koning methods more visible, via arXiv and other channels