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Regression Mixture Models: An Invest...

Sherlock, Phillip.

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Regression Mixture Models: An Investigation of the Effects of Mixing Weights and Predictor Distributions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Regression Mixture Models: An Investigation of the Effects of Mixing Weights and Predictor Distributions./
Author:	Sherlock, Phillip.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	86 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Contained By:	Dissertations Abstracts International81-04A.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13904244
ISBN:	9781687953001

Regression Mixture Models: An Investigation of the Effects of Mixing Weights and Predictor Distributions.
Sherlock, Phillip.

Regression Mixture Models: An Investigation of the Effects of Mixing Weights and Predictor Distributions. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 86 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: A.

Thesis (Ph.D.)--University of South Carolina, 2019.

This item must not be sold to any third party vendors.

This study focused on understanding how several data characteristics associated with the investigation of effect heterogeneity (i.e., mixing weights, predictor distributions, and the inclusion of covariates) affected enumeration and parameter recovery with regression mixture models. The inclusion of C on X paths, where the latent class, C, is regressed on the predictor, X, allows predictor means to vary across classes, at two points in the model building process-during and after enumeration-was of interest. This main aim was accomplished by comparing the correct enumeration rates and parameter coverage rates with and without freely estimated predictor means across classes for models with two classes, considerable separation between groups, and a total sample size of 500. Findings from this study, in accordance with previous work, indicated that C on X paths, should only be included after enumeration (e.g., Nylund-Gibson & Maysen, 2014). Inclusion of C on X paths functionally frees the estimation of associated predictor means across classes. If these paths are included in the enumeration phase, over-extraction is typical when predictor variance differences are present. Results from this study supported findings from previous research that demonstrated the necessity of including the C on X path when predictor means vary across classes (Lamont, Vermunt, & Van Horn, 2016). Therefore, once the number of classes has been determined, C on X paths should be included in models just as researchers would freely estimate residual variances across classes.

ISBN: 9781687953001Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Differential Effects

Regression Mixture Models: An Investigation of the Effects of Mixing Weights and Predictor Distributions.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 86 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
500 $a Advisor: DiStefano, Christine;Habing, Brian.
502 $a Thesis (Ph.D.)--University of South Carolina, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a This study focused on understanding how several data characteristics associated with the investigation of effect heterogeneity (i.e., mixing weights, predictor distributions, and the inclusion of covariates) affected enumeration and parameter recovery with regression mixture models. The inclusion of C on X paths, where the latent class, C, is regressed on the predictor, X, allows predictor means to vary across classes, at two points in the model building process-during and after enumeration-was of interest. This main aim was accomplished by comparing the correct enumeration rates and parameter coverage rates with and without freely estimated predictor means across classes for models with two classes, considerable separation between groups, and a total sample size of 500. Findings from this study, in accordance with previous work, indicated that C on X paths, should only be included after enumeration (e.g., Nylund-Gibson & Maysen, 2014). Inclusion of C on X paths functionally frees the estimation of associated predictor means across classes. If these paths are included in the enumeration phase, over-extraction is typical when predictor variance differences are present. Results from this study supported findings from previous research that demonstrated the necessity of including the C on X path when predictor means vary across classes (Lamont, Vermunt, & Van Horn, 2016). Therefore, once the number of classes has been determined, C on X paths should be included in models just as researchers would freely estimate residual variances across classes.
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650 4 $a Educational psychology. $3 517650
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653 $a Differential Effects
653 $a Hidden Heterogeneity
653 $a Latent Class Regression
653 $a Monte Carlo Simulation
653 $a Regression Mixtures
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