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Medientyp:
E-Artikel
Titel:
Inference for Multiple Change Points in Time Series via Likelihood Ratio Scan Statistics
Beteiligte:
Yau, Chun Yip;
Zhao, Zifeng
Erschienen:
Oxford University Press (OUP), 2016
Erschienen in:
Journal of the Royal Statistical Society Series B: Statistical Methodology, 78 (2016) 4, Seite 895-916
Sprache:
Englisch
DOI:
10.1111/rssb.12139
ISSN:
1369-7412;
1467-9868
Entstehung:
Anmerkungen:
Beschreibung:
<jats:title>Summary</jats:title>
<jats:p>We propose a likelihood ratio scan method for estimating multiple change points in piecewise stationary processes. Using scan statistics reduces the computationally infeasible global multiple-change-point estimation problem to a number of single-change-point detection problems in various local windows. The computation can be efficiently performed with order O{npt log (n)}. Consistency for the estimated numbers and locations of the change points are established. Moreover, a procedure is developed for constructing confidence intervals for each of the change points. Simulation experiments and real data analysis are conducted to illustrate the efficiency of the likelihood ratio scan method.</jats:p>