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Addressing the Variable Selection Bias and Local Optimum Limitations of Longitudinal Recursive Partitioning with Time-Efficient Approximations
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Addressing the Variable Selection Bias and Local Optimum Limitations of Longitudinal Recursive Partitioning with Time-Efficient Approximations. |
| 개인저자 | Stegmann, Gabriela Maria. |
| 단체저자명 | Arizona State University. Psychology. |
| 발행사항 | [S.l.]: Arizona State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 103 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781085704250 |
| 학위논문주기 | Thesis (Ph.D.)--Arizona State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Grimm, Kevin. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it. I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data. |
| 일반주제명 | Quantitative psychology. |
| 언어 | 영어 |
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: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
