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Masking variables in mixture modeling.

Rausch, Emilie.

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Masking variables in mixture modeling.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Masking variables in mixture modeling./
作者:	Rausch, Emilie.
面頁冊數:	36 p.
附註:	Source: Masters Abstracts International, Volume: 52-03.
Contained By:	Masters Abstracts International52-03(E).
標題:	Psychology, Psychometrics. -
電子資源:	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.
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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.
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