An exploratory assessment of a multidimensional healthcare and economic data on COVID-19 in Nigeria

通过多维标度法进行过程数据的潜在特征提取

Latent Feature Extraction for Process Data via Multidimensional Scaling

针对教育评估中计算机交互式题目所记录的反应过程数据，本文提出基于多维标度框架的探索性潜在变量分析方法，以提取潜在特征，并证明其在多样本外预测任务中比经典二元反应包含更多信息。

使用探索性项目反应理论和潜在类别项目反应理论模型估计种族和族裔医疗质量差异

Estimating Racial and Ethnic Healthcare Quality Disparities Using Exploratory Item Response Theory and Latent Class Item Response Theory Models

针对医疗质量数据的统计特征被忽视及种族差异估计中混杂因素的问题，提出结合探索性多维IRT、潜在类别IRT模型估计质量并通过优化匹配调整混杂的方法，以精神分裂症患者数据为例验证了该方法。

Density Estimation Trees in High Energy Physics

Density Estimation Trees can play an important role in exploratory data analysis for multidimensional, multi-modal data models of large samples. I briefly discuss the algorithm, a self-optimization technique based on kernel density estimation, and some applications in High Energy Physics.

A method for visual identification of small sample subgroups and potential biomarkers

In order to find previously unknown subgroups in biomedical data and generate testable hypotheses, visually guided exploratory analysis can be of tremendous importance. In this paper we propose a new dissimilarity measure that can be used within the Multidimensional Scaling framework to obtain a joint low-dimensional representation of both the samples and variables of a multivariate data set, thereby providing an alternative to conventional biplots. In comparison with biplots, the representations obtained by our approach are particularly useful for exploratory analysis of data sets where there…

Extracting relevant structures from self-determination theory questionnaires via Information Bottleneck method

In this paper, we introduce the application of Information Bottleneck as a method to investigate properties of questionnaires developed for the study of motivational profiles based on self-determination theory. Founded on information theory, the Information Bottleneck method compresses multidimensional categorical data into clusters by minimizing the loss of relevant information. It does not require linearity such as Pearson correlation. For exploratory data analysis, it allows investigating the structure of questionnaire items grouping with a hierarchical partition as perceived by the sample,…

Stochastic Approximation EM for Exploratory Item Factor Analysis

The stochastic approximation EM algorithm (SAEM) is described for the estimation of item and person parameters given test data coded as dichotomous or ordinal variables. The method hinges upon the eigenanalysis of missing variables sampled as augmented data; the augmented data approach was introduced by Albert's seminal work applying Gibbs sampling to Item Response Theory in 1992. Similar to maximum likelihood factor analysis, the factor structure in this Bayesian approach depends only on sufficient statistics, which are computed from the missing latent data. A second feature of the SAEM algor…