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

In a wide range of applications, the stochastic properties of the observedtime series change over time. The changes often occur gradually rather thanabruptly: the properties are (approximately) constant for some time and thenslowly start to change. In many cases, it is of interest to locate the timepoint where the properties start to vary. In contrast to the analysis of abruptchanges, methods for detecting smooth or gradual change points are lessdeveloped and often require strong parametric assumptions. In this paper, wedevelop a fully nonparametric method to estimate a smooth change point in alocally stationary framework. We set up a general procedure which allows us todeal with a wide variety of stochastic properties including the mean,(auto)covariances and higher moments. The theoretical part of the paperestablishes the convergence rate of the new estimator. In addition, we examineits finite sample performance by means of a simulation study and illustrate themethodology by two applications to financial return data.

Detecting gradual changes in locally stationary processes