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Statistical inference for evolving periodic functions

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  • Marc G. Genton
  • Peter Hall
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    Abstract

    In the study of variable stars, where the light reaching an observer fluctuates over time, it can be difficult to explain the nature of the variation unless it follows a regular pattern. In this respect, so-called periodic variable stars are particularly amenable to analysis. There, radiation varies in a perfectly periodic fashion, and period length is a major focus of interest. We develop methods for conducting inference about features that might account for departures from strict periodicity. These include variation, over time, of the period or amplitude of radiation. We suggest methods for estimating the parameters of this evolution, and for testing the hypothesis that the evolution is present. This problem has some unusual features, including subtle issues of identifiability. Copyright 2007 Royal Statistical Society.

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    Bibliographic Info

    Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series B (Statistical Methodology).

    Volume (Year): 69 (2007)
    Issue (Month): 4 ()
    Pages: 643-657

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    Handle: RePEc:bla:jorssb:v:69:y:2007:i:4:p:643-657

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    Cited by:
    1. Michael Vogt & Oliver Linton, 2012. "Nonparametric estimation of a periodic sequence in the presence of a smooth trend," CeMMAP working papers CWP23/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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