English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Models with multiple change points are used in many fields; however, thetheoretical properties of maximum likelihood estimators of such models havereceived relatively little attention. The goal of this paper is to establishthe asymptotic properties of maximum likelihood estimators of the parameters ofa multiple change-point model for a general class of models in which the formof the distribution can change from segment to segment and in which, possibly,there are parameters that are common to all segments. Consistency of themaximum likelihood estimators of the change points is established and the rateof convergence is determined; the asymptotic distribution of the maximumlikelihood estimators of the parameters of the within-segment distributions isalso derived. Since the approach used in single change-point models is noteasily extended to multiple change-point models, these results require theintroduction of those tools for analyzing the likelihood function in a multiplechange-point model.

Asymptotic properties of maximum likelihood estimators in models with multiple change points