Item response theory and its applications in educational measurement Part II: Theory and practices of test equating in item response theory
Item response theory (IRT) is a class of latent variable models, which are used to develop educational and psychological tests (e.g., standardized tests, personality tests, tests for licensure and certification). We offer readers with ...
Graphical representation of test score equating under non‐equivalent groups with anchor test design (NEAT design). Form N (blue) is equated to Form B (red). image image
Detecting clusters in multivariate response regression
Multivariate regression, which can also be posed as a multitask machine learning problem, is used to better understand multiple outputs based on a given set of inputs. Many methods have been proposed on how to utilize shared information about ...
A visual representation of the different options for detecting clusters in responses when using multivariate regression. image image
Integrative clustering methods for multi‐omics data
Integrative analysis of multi‐omics data has drawn much attention from the scientific community due to the technological advancements which have generated various omics data. Leveraging these multi‐omics data potentially provides a more ...
Three categories of integrative multi‐omics clustering methods. Multi‐omics data (e.g., DNA methylation, copy number variation, gene expression) are collected for each sample. Integrative multi‐omics clustering methods can be used to analyze such data and ...
Copulae: An overview and recent developments
Over the decades that have passed since they were introduced, copulae still remain a very powerful tool for modeling and estimating multivariate distributions. This work gives an overview of copula theory and it also summarizes the latest results. ...
Different distributions through different copulae and margins image image
Cluster‐scaled principal component analysis
Cluster‐scaled analysis means exploiting the cluster‐based scaling to conventional data analysis to obtain more accurate results or results that we cannot obtain by using ordinary analysis. Our target data is complex and large amounts of data. For ...
Objects 7 and 11 are close on the plain that was obtained due to principal component analysis; however, they are so far in the higher‐dimensional space in which these data exist. To solve this problem, cluster‐scaled analysis utilizes a result of fuzzy ...
