评估潜在分类分析中的信息标准:用于识别乳腺癌数据集的分类

Q4 Mathematics
Abdallah Abarda, Mohamed Dakkon, Khawla Asmi, Youssef Bentaleb
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引用次数: 0

摘要

在最近的研究中,潜在类分析(LCA)模型被提出作为标准分类方法的一种方便的替代方法。它已经成为一种流行的工具,可以根据受访者对一组分类变量的回答将他们分为同质子组。在人口研究中,没有一种普遍接受的统计指标来决定类别的数目,这是在应用最低分类法中尚未解决的主要问题之一。通常使用似然比检验来确定构成给定人口概况的类别的数量,但是这种方法的使用在理论上是不正确的。为了克服这一问题,我们提出了一种基于信息标准的经典潜在类模型选择方法的替代方案。本文旨在研究选择潜在类分析模型的信息标准的性能。在不同的样本量和模型维度下,比较了九种信息准则。我们还提出了一种应用集成电路来选择乳腺癌数据集的最佳模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating information criteria in latent class analysis: application to identify classes of breast cancer dataset
: In recent studies, latent class analysis (LCA) modelling has been proposed as a convenient alternative to standard classification methods. It has become a popular tool for clustering respondents into homogeneous subgroups based on their responses on a set of categorical variables. The absence of a common accepted statistical indicator for deciding the number of classes in the study of population represents one of the major unresolved issues in the application of the LCA. Determining the number of classes constituting the profiles of a given population is often done by using the likelihood ratio test, however the use of such methodology is not correct theoretically. To overcome this problem, we propose an alternative for the classical latent class models selection methods based on the information criteria. This article aims to investigate the performance of information criteria for selecting the latent class analysis models. Nine information criteria are compared under various sample sizes and model dimensionality. We propose also an application of ICs to select the best model of breast cancer dataset.
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来源期刊
International Journal of Data Analysis Techniques and Strategies
International Journal of Data Analysis Techniques and Strategies Decision Sciences-Information Systems and Management
CiteScore
1.20
自引率
0.00%
发文量
21
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