Language:
English
繁體中文
Help
回圖書館首頁
手機版館藏查詢
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Neural network imputation: A new fa...
~
Amer, Safaa R.
Linked to FindBook
Google Book
Amazon
博客來
Neural network imputation: A new fashion or a good tool.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Neural network imputation: A new fashion or a good tool./
Author:
Amer, Safaa R.
Description:
157 p.
Notes:
Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3524.
Contained By:
Dissertation Abstracts International65-07B.
Subject:
Statistics. -
Online resource:
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.
LDR
:02331nmm 2200277 4500
001
1840638
005
20050802071628.5
008
130614s2004 eng d
020
$a
049685612X
035
$a
(UnM)AAI3138451
035
$a
AAI3138451
040
$a
UnM
$c
UnM
100
1
$a
Amer, Safaa R.
$3
1928966
245
1 0
$a
Neural network imputation: A new fashion or a good tool.
300
$a
157 p.
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.
590
$a
School code: 0172.
650
4
$a
Statistics.
$3
517247
690
$a
0463
710
2 0
$a
Oregon State University.
$3
625720
773
0
$t
Dissertation Abstracts International
$g
65-07B.
790
1 0
$a
Lesser, Virginia M.,
$e
advisor
790
$a
0172
791
$a
Ph.D.
792
$a
2004
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3138451
based on 0 review(s)
Location:
ALL
電子資源
Year:
Volume Number:
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
W9190152
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login