Analysis of integrated data
著者
書誌事項
Analysis of integrated data
CRC Press, c2019
大学図書館所蔵 全3件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references (p. 240-245) and index
内容説明・目次
内容説明
The advent of "Big Data" has brought with it a rapid diversification of data sources, requiring analysis that accounts for the fact that these data have often been generated and recorded for different reasons. Data integration involves combining data residing in different sources to enable statistical inference, or to generate new statistical data for purposes that cannot be served by each source on its own. This can yield significant gains for scientific as well as commercial investigations.
However, valid analysis of such data should allow for the additional uncertainty due to entity ambiguity, whenever it is not possible to state with certainty that the integrated source is the target population of interest. Analysis of Integrated Data aims to provide a solid theoretical basis for this statistical analysis in three generic settings of entity ambiguity: statistical analysis of linked datasets that may contain linkage errors; datasets created by a data fusion process, where joint statistical information is simulated using the information in marginal data from non-overlapping sources; and estimation of target population size when target units are either partially or erroneously covered in each source.
Covers a range of topics under an overarching perspective of data integration.
Focuses on statistical uncertainty and inference issues arising from entity ambiguity.
Features state of the art methods for analysis of integrated data.
Identifies the important themes that will define future research and teaching in the statistical analysis of integrated data.
Analysis of Integrated Data is aimed primarily at researchers and methodologists interested in statistical methods for data from multiple sources, with a focus on data analysts in the social sciences, and in the public and private sectors.
目次
1. Introduction - Ray Chambers 2. On secondary analysis of datasets that cannot be linked without errors - Li-Chun Zhang 3. Capture-recapture methods in the presence of linkage errors - Loredana di Congsiglio, Tiziana Tuoto, Li-Chun Zhang 4. An overview on uncertainty and estimation in statistical matching - Maruo Scanu, Pier Luigi Conti, Daniela Marella 5. Auxiliary variable selection in a statistical matching problem - Marcello D'Orazio, Marco Di Zio, Mauro Scanu 6. Minimal inference from incomplete 2 x 2-tables - Li-Chun Zhang, Raymond L. Chambers 7. Dual and multiple system estimation with fully and partially observed covariates - Van der Heijden et al. 8. Estimating population size in multiple record systems with uncertainty of state identification - Davide Di Cecco 9. Log-linear models of erroneous list data - Li-Chun Zhang 10. Sampling design and analysis using geo-referenced data - Danila Filipponi, Federica Piersimoni, Roberto Benedetti, Maria Michela Dickson, Giuseppe Espa, Diego Giuliani
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