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CHANGE‐POINT DETECTION WITH RANK STATISTICS IN LONG‐MEMORY TIME‐SERIES MODELS
Author(s) -
Wang Lihong
Publication year - 2008
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2008.00515.x
Subject(s) - mathematics , wilcoxon signed rank test , statistics , series (stratigraphy) , estimator , asymptotic distribution , monte carlo method , rank (graph theory) , long memory , econometrics , combinatorics , mann–whitney u test , volatility (finance) , paleontology , biology
Summary Wilcoxon‐type rank statistics are considered for testing a long‐memory time‐series model with a common distribution against the alternatives involving a change in the distribution at an unknown time point. The asymptotic properties of the test statistics and the change‐point estimators are studied. Finite‐sample behaviours are investigated in a small Monte Carlo simulation study. Data examples from hydrology and telecommunications illustrate the method.