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The Robustness of EBIC Glasso to Dis...
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Slipetz, Lindley R.
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The Robustness of EBIC Glasso to Distributional Misspecification.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
The Robustness of EBIC Glasso to Distributional Misspecification./
作者:
Slipetz, Lindley R.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:
65 p.
附註:
Source: Masters Abstracts International, Volume: 83-01.
Contained By:
Masters Abstracts International83-01.
標題:
Psychology. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28499051
ISBN:
9798516079450
The Robustness of EBIC Glasso to Distributional Misspecification.
Slipetz, Lindley R.
The Robustness of EBIC Glasso to Distributional Misspecification.
- Ann Arbor : ProQuest Dissertations & Theses, 2021 - 65 p.
Source: Masters Abstracts International, Volume: 83-01.
Thesis (M.A.)--Wake Forest University, 2021.
This item must not be sold to any third party vendors.
Network analysis is gaining in popularity as a method for modeling psychopathology. EBIC glasso is a common method of estimating such networks. An assumption of EBIC glasso is that the residuals follow a normal distribution. Thusfar, no one has assessed the robustness of EBIC glasso to distributional misspecification: assuming the data is normal when it is actually nonnormal. Using a 5 x 4 x 4 simulation design with 500 replications, the robustness of EBIC glasso is tested against such misspecifications. The conditions are compared via standardized bias and multilevel metamodels. The current study finds that misspecification in the case of assuming data is normal when it, in fact, is not, does have an effect of the parameter's deviation from the true values of the model in that underestimation of the true values occurs. This effect varies across condition and centrality measure. The paper suggests great care being taken when using nonnormal data with EBIC glasso.
ISBN: 9798516079450Subjects--Topical Terms:
519075
Psychology.
Subjects--Index Terms:
Network analysis
The Robustness of EBIC Glasso to Distributional Misspecification.
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Network analysis is gaining in popularity as a method for modeling psychopathology. EBIC glasso is a common method of estimating such networks. An assumption of EBIC glasso is that the residuals follow a normal distribution. Thusfar, no one has assessed the robustness of EBIC glasso to distributional misspecification: assuming the data is normal when it is actually nonnormal. Using a 5 x 4 x 4 simulation design with 500 replications, the robustness of EBIC glasso is tested against such misspecifications. The conditions are compared via standardized bias and multilevel metamodels. The current study finds that misspecification in the case of assuming data is normal when it, in fact, is not, does have an effect of the parameter's deviation from the true values of the model in that underestimation of the true values occurs. This effect varies across condition and centrality measure. The paper suggests great care being taken when using nonnormal data with EBIC glasso.
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