Time Series Analysis under Not Missing at Random

DOI Open Access
  • Baba Yuki
    九州大学大学院数理学府
  • Hirose Kei
    九州大学マス・フォア・インダストリ研究所

Bibliographic Information

Other Title
  • ランダムでない欠測を含む時系列モデリング

Description

<p>Time series data often include missing values, and statistical modeling that deals with missing values is needed. Typically, a state-space model is used to impute missing values. However, this approach implicitly assumes that the missing mechanism is missing at random; thus, the estimator may be biased when the missing mechanism is not missing at random. In this study, we construct and incorporate the missing mechanism to reduce the bias of the estimator. The model parameter is estimated by the Monte Carlo Expectation-Maximization (MCEM) algorithm. Monte Carlo simulation is conducted to investigate the effectiveness of our proposed procedure.</p>

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Details 詳細情報について

  • CRID
    1390299318848814336
  • DOI
    10.11329/jjssj.53.275
  • ISSN
    21891478
    03895602
  • Text Lang
    ja
  • Article Type
    journal article
  • Data Source
    • JaLC
    • KAKEN
  • Abstract License Flag
    Disallowed

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