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  1. B200 工学部/工学研究科
  2. B200a 雑誌掲載論文
  3. 学術雑誌

複雑な内生抽出法に基づく標本への離散選択モデルの適用

http://hdl.handle.net/2237/8636
6df7e55d-7341-45e3-b00c-f9672dc23fd6
名前 / ファイル ライセンス アクション
v667_p103-111.pdf v667_p103-111.pdf (671.1 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2007-08-07
タイトル
タイトル 複雑な内生抽出法に基づく標本への離散選択モデルの適用
その他のタイトル
その他のタイトル WEIGHTED ESTIMATION OF DISCRETE CHOICE MODELS WITH DATA FROM COMPLEX ENDOGENOUS SAMPLING
著者 北村, 隆一

× 北村, 隆一

WEKO 18651

北村, 隆一

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KITAMURA, Ryuichi

× KITAMURA, Ryuichi

WEKO 18652

KITAMURA, Ryuichi

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酒井, 弘

× 酒井, 弘

WEKO 18653

酒井, 弘

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SAKAI, Hiroshi

× SAKAI, Hiroshi

WEKO 18654

SAKAI, Hiroshi

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山本, 俊行

× 山本, 俊行

WEKO 18655

山本, 俊行

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YAMAMOTO, Toshiyuki

× YAMAMOTO, Toshiyuki

WEKO 18656

YAMAMOTO, Toshiyuki

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権利
権利情報 土木編集第07010号
キーワード
主題Scheme Other
主題 weighting
キーワード
主題Scheme Other
主題 endogenous sampling
キーワード
主題Scheme Other
主題 roadside surveys
キーワード
主題Scheme Other
主題 WESMI
キーワード
主題Scheme Other
主題 covariance estimator
抄録
内容記述 本論文では内生抽出標本の重み付け理論を拡張し、加重層別標本抽出法や多次元選択肢別標本抽出法による標本への適用を論じる。これらの複雑な内生標本抽出法により得られた標本に適用可能な一意的な重みが存在することを示し、従来一般的になされてきた重み付け法の問題点を指摘するとともに、その改良を提案する。さらに、重み付きと重み無しで離散選択モデルを推定した場合の係数値とt-値を比較し、非分散行列を適切に推定することの重要性を示す。The methodology for weighting endogenous samples is extended in this study and applied to a multi-dimensional choice-based sample. It is shown that a unified weight exists for samples obtained from complex endogenous sampling schemes. Problems with conventional weighting methods for samples from endogenous sampling, such as roadside surveys, are pointed out and an improved weighting scheme is proposed. Coefficient estimates of discrete choice models with and without weights are compared, and the importance of applying an appropriate covariance estimator is pointed out.
内容記述タイプ Abstract
出版者
出版者 土木学会
言語
言語 jpn
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
ISSN
収録物識別子タイプ ISSN
収録物識別子 0289-7806
書誌情報 土木学会論文集

巻 667, 号 Ⅳ-50, p. 103-111, 発行日 2001-01
フォーマット
application/pdf
著者版フラグ
値 publisher
URI
識別子 http://hdl.handle.net/2237/8636
識別子タイプ HDL
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