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Neural network imputation: A new fa...

Amer, Safaa R.

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Neural network imputation: A new fashion or a good tool.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neural network imputation: A new fashion or a good tool./
作者:	Amer, Safaa R.
面頁冊數:	157 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3524.
Contained By:	Dissertation Abstracts International65-07B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3138451
ISBN:	049685612X

Neural network imputation: A new fashion or a good tool.
Amer, Safaa R.

Neural network imputation: A new fashion or a good tool. - 157 p.

Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3524.

Thesis (Ph.D.)--Oregon State University, 2004.

Most statistical surveys and data collection studies encounter missing data. A common solution to this problem is to discard observations with missing data while reporting the percentage of missing observations in different output tables. Imputation is a tool used to fill in the missing values. This dissertation introduces the missing data problem as well as traditional imputation methods (e.g. hot deck, mean imputation, regression, Markov Chain Monte Carlo, Expectation-Maximization, etc.). The use of artificial neural networks (ANN), a data mining technique, is proposed as an effective imputation procedure. During ANN imputation, computational effort is minimized while accounting for sample design and imputation uncertainty. The mechanism and use of ANN in imputation for complex survey designs is investigated.

ISBN: 049685612XSubjects--Topical Terms:

517247
Statistics.

Neural network imputation: A new fashion or a good tool.
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500 $a Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3524.
500 $a Adviser: Virginia M. Lesser.
502 $a Thesis (Ph.D.)--Oregon State University, 2004.
520 $a Most statistical surveys and data collection studies encounter missing data. A common solution to this problem is to discard observations with missing data while reporting the percentage of missing observations in different output tables. Imputation is a tool used to fill in the missing values. This dissertation introduces the missing data problem as well as traditional imputation methods (e.g. hot deck, mean imputation, regression, Markov Chain Monte Carlo, Expectation-Maximization, etc.). The use of artificial neural networks (ANN), a data mining technique, is proposed as an effective imputation procedure. During ANN imputation, computational effort is minimized while accounting for sample design and imputation uncertainty. The mechanism and use of ANN in imputation for complex survey designs is investigated.
520 $a Imputation methods are not all equally good, and none are universally good. However, simulation results and applications in this dissertation show that regression, Markov chain Monte Carlo, and ANN yield comparable results. Artificial neural networks could be considered as implicit models that take into account the sample design without making strong parametric assumptions. Artificial neural networks make few assumptions about the data, are asymptotically good and robust to multicollinearity and outliers. Overall, ANN could be time and resources efficient for an experienced user compared to other conventional imputation techniques.
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