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Interaction-Sensitive Tree-Based Sta...

Sun, Xiaotong,

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Interaction-Sensitive Tree-Based Statistical Models /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Interaction-Sensitive Tree-Based Statistical Models // Xiaotong Sun.
作者:	Sun, Xiaotong,
面頁冊數:	1 electronic resource (144 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
Contained By:	Dissertations Abstracts International86-08B.
標題:	Industrial engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31762093
ISBN:	9798302872005

Interaction-Sensitive Tree-Based Statistical Models /
Sun, Xiaotong,

Interaction-Sensitive Tree-Based Statistical Models /Xiaotong Sun. - 1 electronic resource (144 pages)

Source: Dissertations Abstracts International, Volume: 86-08, Section: B.

This dissertation introduces a tree-based framework to improve the interpretability and modeling of interaction effects among variables, essential in fields like biostatistics, healthcare, science and engineering. Traditional regression methods often fail to clearly capture complex interactions, while tree-based approaches, despite their interpretability, face performance limitations and overfitting concerns. Our proposed interaction-sensitive tree-based method, designed for seamless integration, combines various statistical techniques tailored to different data types, leveraging ensemble learning methods to enhance accuracy and mitigate overfitting. We present methods for regression, survival analysis, and classification, validated with case studies and benchmarked against traditional models using metrics like BIC and R-squared. The results highlight our framework's balance between interpretability and predictive performance, offering a robust solution for analyzing complex data interactions.

English

ISBN: 9798302872005Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Healthcare

Interaction-Sensitive Tree-Based Statistical Models /
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520 $a This dissertation introduces a tree-based framework to improve the interpretability and modeling of interaction effects among variables, essential in fields like biostatistics, healthcare, science and engineering. Traditional regression methods often fail to clearly capture complex interactions, while tree-based approaches, despite their interpretability, face performance limitations and overfitting concerns. Our proposed interaction-sensitive tree-based method, designed for seamless integration, combines various statistical techniques tailored to different data types, leveraging ensemble learning methods to enhance accuracy and mitigate overfitting. We present methods for regression, survival analysis, and classification, validated with case studies and benchmarked against traditional models using metrics like BIC and R-squared. The results highlight our framework's balance between interpretability and predictive performance, offering a robust solution for analyzing complex data interactions.
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650 4 $a Engineering. $3 586835
650 4 $a Biostatistics. $3 1002712
653 $a Healthcare
653 $a Traditional models
653 $a Survival analysis
653 $a Data interactions
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