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Abstract Details
Activity Number:
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519
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #301251 |
Title:
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On the Autopersistence Functions and the Autopersistence Graphs of Binary Autoregressive Time Series
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Author(s):
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Chao Wang*+ and Wai Keung Li
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Companies:
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University of Hong Kong and University of Hong Kong
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Address:
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Room 518, Meng Wah Complex, Hong Kong, , China
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Keywords:
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autopersistence function ;
autopersistence graph ;
binary time series ;
Markov chain ;
consistency ;
asymptotic normality
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Abstract:
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The classical autocorrelation function may not be an effective and informative means in revealing the dependence features of a binary time series, which, without loss of generality, is assumed to take values 0 and 1. Recently, the autopersistence functions, defined as the functions of the lag k with the value being the conditional probability with the observation at time t being 1 given the particular observation at time t-k, have been proposed as alternatives to the autocorrelation function for binary time series. In this article we consider the theoretical autopersistence functions and their natural sample analogues, the autopersistence graphs, under a binary autoregressive model framework. Some properties of the autopersistence functions and the asymptotic properties of the autopersistence graphs are discussed. The results have potential application in the modelling of binary time series.
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