ON DIRECT KERNEL ESTIMATOR OF DENSITY RATIO

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説明

Estimation for a density ratio has an important role in statistical inference. We can use the estimator for testing homogeneity of two samples, detecting change point etc. Let f(x) and g(x) denote probability density functions and g(x0) ≠ 0 (x0 ∈ R). There are several ways to estimate the density ratio f(x0)/g(x0). In this paper we discuss a kernel estimation that is a popular method in nonparametric statistical inference. A naive estimator is constituted from separate estimators of f(x0) and g(x0), which we call an indirect estimator. Another estimator is proposed by Ćwik and Mielniczuk (1989), which we call a direct estimator. Extending Ćwik and Mielniczuk (1989)'s method, we propose a new direct estimator, and derive an asymptotic mean squared error. We also prove central limit theorem of the new estimator, and compare mean squared errors of the proposed estimator and the direct estimator by simulation.

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詳細情報 詳細情報について

  • CRID
    1390009224849147136
  • NII論文ID
    120006620467
  • NII書誌ID
    AA10634475
  • DOI
    10.5109/2233857
  • ISSN
    2435743X
    0286522X
  • HANDLE
    2324/2233857
  • 本文言語コード
    en
  • 資料種別
    journal article
  • データソース種別
    • JaLC
    • IRDB
    • Crossref
    • CiNii Articles
    • KAKEN
    • OpenAIRE
  • 抄録ライセンスフラグ
    使用可

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