東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Masking variables in mixture modeling.

Rausch, Emilie.

Linked to FindBook

Google Book

Amazon

博客來

Masking variables in mixture modeling.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Masking variables in mixture modeling./
Author:	Rausch, Emilie.
Description:	36 p.
Notes:	Source: Masters Abstracts International, Volume: 52-03.
Contained By:	Masters Abstracts International52-03(E).
Subject:	Psychology, Psychometrics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1524409
ISBN:	9781303550409

Masking variables in mixture modeling.
Rausch, Emilie.

Masking variables in mixture modeling. - 36 p.

Source: Masters Abstracts International, Volume: 52-03.

Thesis (M.A.)--University of Missouri - Columbia, 2013.

Finite normal mixture modeling is a popular technique for clustering individuals into distinct subpopulations. A characteristic of mixture modeling that researchers may be unaware of is its tendency to arrive at locally, rather than globally, optimal solutions, and these local solutions can lead to an incorrect partition of the individuals into groups. This paper examines the behavior of mixture modeling under different conditions, specifically focusing on when data contain noise and when the mixtures' means are various distances (effect sizes) from one another. Monte Carlo simulations were conducted and it was found that the ability of the mixture model to obtain correct partitions and stable parameter estimates degenerates when noise is added. In addition, the results suggest that as the distance between clusters increases, recovery only reaches a moderate level at almost three times a large effect size.

ISBN: 9781303550409Subjects--Topical Terms:

1017742
Psychology, Psychometrics.

Masking variables in mixture modeling.
LDR:01715nam a2200265 4500 001 1960377
005 20140616133305.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303550409
035 $a (MiAaPQ)AAI1524409
035 $a AAI1524409
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rausch, Emilie. $3 2096019
245 1 0 $a Masking variables in mixture modeling.
300 $a 36 p.
500 $a Source: Masters Abstracts International, Volume: 52-03.
500 $a Adviser: Douglas Steinley.
502 $a Thesis (M.A.)--University of Missouri - Columbia, 2013.
520 $a Finite normal mixture modeling is a popular technique for clustering individuals into distinct subpopulations. A characteristic of mixture modeling that researchers may be unaware of is its tendency to arrive at locally, rather than globally, optimal solutions, and these local solutions can lead to an incorrect partition of the individuals into groups. This paper examines the behavior of mixture modeling under different conditions, specifically focusing on when data contain noise and when the mixtures' means are various distances (effect sizes) from one another. Monte Carlo simulations were conducted and it was found that the ability of the mixture model to obtain correct partitions and stable parameter estimates degenerates when noise is added. In addition, the results suggest that as the distance between clusters increases, recovery only reaches a moderate level at almost three times a large effect size.
590 $a School code: 0133.
650 4 $a Psychology, Psychometrics. $3 1017742
690 $a 0632
710 2 $a University of Missouri - Columbia. $b Pscyhological Sciences. $3 2096020
773 0 $t Masters Abstracts International $g 52-03(E).
790 $a 0133
791 $a M.A.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1524409