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Contributions to Parametric and Semi...

Xu, Yifei,

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Contributions to Parametric and Semiparametric Structural Equation Modeling Methods With Applications in the Biomedical Sciences /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Contributions to Parametric and Semiparametric Structural Equation Modeling Methods With Applications in the Biomedical Sciences // Yifei Xu.
Author:	Xu, Yifei,
Description:	1 electronic resource (121 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
Subject:	Biostatistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31143197
ISBN:	9798382221908

Contributions to Parametric and Semiparametric Structural Equation Modeling Methods With Applications in the Biomedical Sciences /
Xu, Yifei,

Contributions to Parametric and Semiparametric Structural Equation Modeling Methods With Applications in the Biomedical Sciences /Yifei Xu. - 1 electronic resource (121 pages)

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

This research work commences by discussing two commonly used fit indices: root mean squared error of approximation (RMSEA) and goodness-of-fit index (GFI), which are employed to assess the goodness of fit of model to the data. Chi-square distribution in the traditional RMSEA may vary with different estimation methods or types of data, leading to inaccurate RMSEA values, therefore, we propose an alternative RMSEA that is not sensitive to changes in degrees of freedom. Further-more, the traditional GFI tends to underperform on dichotomous data, particularly with small sample sizes. The method to mitigate the effect of a mis-specified fit function in the real world is integrated with a modified version of the GFI, namely, the corrected GFI (CGFI). A simulation study is employed to compare the performance of improved RMSEA and GFI with the previously modified RMSEA and GFI, respectively. Furthermore, as there is no previous research considering the non-linear relationship between confounding variables, manifest variables, and latent variables, the proposed SSEM introduces nonparametric link functions, assumed to be monotone/non-monotone, to classical SEM instead of constructing a mixture multivariate model based on latent variables. Four types of regressions are used to predict the functional forms in the simulation study. Two metabolic syndrome datasets are used to demonstrate the application of the methods.

English

ISBN: 9798382221908Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Categorical data

Contributions to Parametric and Semiparametric Structural Equation Modeling Methods With Applications in the Biomedical Sciences /
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520 $a This research work commences by discussing two commonly used fit indices: root mean squared error of approximation (RMSEA) and goodness-of-fit index (GFI), which are employed to assess the goodness of fit of model to the data. Chi-square distribution in the traditional RMSEA may vary with different estimation methods or types of data, leading to inaccurate RMSEA values, therefore, we propose an alternative RMSEA that is not sensitive to changes in degrees of freedom. Further-more, the traditional GFI tends to underperform on dichotomous data, particularly with small sample sizes. The method to mitigate the effect of a mis-specified fit function in the real world is integrated with a modified version of the GFI, namely, the corrected GFI (CGFI). A simulation study is employed to compare the performance of improved RMSEA and GFI with the previously modified RMSEA and GFI, respectively. Furthermore, as there is no previous research considering the non-linear relationship between confounding variables, manifest variables, and latent variables, the proposed SSEM introduces nonparametric link functions, assumed to be monotone/non-monotone, to classical SEM instead of constructing a mixture multivariate model based on latent variables. Four types of regressions are used to predict the functional forms in the simulation study. Two metabolic syndrome datasets are used to demonstrate the application of the methods.
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