Efficient Estimation and Model Selection for Grouped Data with Local Moments

• Hitomi Kohtaro

  Kyoto Institute of Technology

• Liu Qing-Feng

  Kyoto University

• Nishiyama Yoshihiko

  Kyoto University

• Sueishi Naoya

  University of Wisconsin-Madison

抄録

This paper proposes efficient estimation methods of unknown parameters when frequencies as well as local moments are available in grouped data. Assuming the original data is an i.i.d. sample from a parametric density with unknown parameters, we obtain the joint density of frequencies and local moments, and propose a maximum likelihood (ML) estimator. We further compare it with the generalized method of moments (GMM) estimator and prove these two estimators are asymptotically equivalent in the first order. Based on the ML method, we propose to use the Akaike information criterion (AIC) for model selection. Monte Carlo experiments show that the estimators perform remarkably well, and AIC selects the right model with high frequency.

収録刊行物

• ＪＯＵＲＮＡＬ　ＯＦ　ＴＨＥ　ＪＡＰＡＮ　ＳＴＡＴＩＳＴＩＣＡＬ　ＳＯＣＩＥＴＹ

  ＪＯＵＲＮＡＬ　ＯＦ　ＴＨＥ　ＪＡＰＡＮ　ＳＴＡＴＩＳＴＩＣＡＬ　ＳＯＣＩＥＴＹ 38 (1), 131-143, 2008

  日本統計学会

キーワード

• AIC
• GMM
• grouped data
• local moments
• MLE
• model selection

詳細情報

• CRID: 1390282680263162624
• NII論文ID: 130002113047
• NII論文ID: 110006835618
• NII書誌ID: AA11510536
• ISSN: 03895602
• ISSN: 13486365
• ISSN: 18822754
• DOI: 10.14490/jjss.38.131
• MRID: 2458324
• NDL書誌ID: 9598556
• Web Site: http://hdl.handle.net/10212/1865
• Web Site: http://hdl.handle.net/10252/3252
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I9598556
• Web Site: https://www.jstage.jst.go.jp/article/jjss/38/1/38_1_131/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • IRDB
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可