Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Bayesian Shrinkage for Functional Network Models, With Applications to Longitudinal Item Response Data

Authors
Jaewoo ParkYESEULJEONMinsuk ShinMinjeong JeonIck Hoon Jin
Issue Date
May-2022
Publisher
AMER STATISTICAL ASSOC
Keywords
Bayesian functional shrinkage; Doubly intractable distributions; Exponential random graph model; Ising graphical model; Longitudinal networks
Citation
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, v.31, no.2, pp.360 - 377
Journal Title
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume
31
Number
2
Start Page
360
End Page
377
URI
https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6295
DOI
10.1080/10618600.2021.1999823
ISSN
1061-8600
Abstract
Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is crucial for educational assessment or designing marketing strategies from survey questions. Although dynamic network models have been widely developed, we cannot apply them directly to item response data because there are multiple systems of nodes with various types of local interactions among items, resulting in multiplex network structures. We propose a new model to study these temporal interactions among items by embedding the functional parameters within the exponential random graph model framework. Inference on such models is difficult because the likelihood functions contain intractable normalizing constants. Furthermore, the number of functional parameters grows exponentially as the number of items increases. Variable selection for such models is not trivial because standard shrinkage approaches do not consider temporal trends in functional parameters. To overcome these challenges, we develop a novel Bayes approach by combining an auxiliary variable MCMC algorithm and a recently developed functional shrinkage method. We apply our algorithm to survey and review datasets, illustrating that the proposed approach can avoid the evaluation of intractable normalizing constants as well as the detection of significant temporal interactions among items. Through a simulation study under different scenarios, we examine the performance of our algorithm. Our method is, to our knowledge, the first attempt to select functional variables for models with intractable normalizing constants. Supplementary materials for this article are available online.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Commerce and Economics > Applied Statistics > 1. Journal Articles

qrcode

Items in Scholar Hub are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher ,  photo

,
College of Commerce and Economics (Applied Statistics)
Read more

Altmetrics

Total Views & Downloads

BROWSE