Facilitating an expectation-maximization (EM) algorithm to solve an integrated choice and latent variable (ICLV) model with fully correlated latent variables
- Authors
- Chae, Dasol; Jung, Jaeyoung; Sohn, Keemin
- Issue Date
- Mar-2018
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Choice model; Latent variable; Fully connected structural equation; Expectation-maximization (EM) algorithm; Seemingly unrelated regression (SUR)
- Citation
- JOURNAL OF CHOICE MODELLING, v.26, pp 64 - 79
- Pages
- 16
- Journal Title
- JOURNAL OF CHOICE MODELLING
- Volume
- 26
- Start Page
- 64
- End Page
- 79
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/1128
- DOI
- 10.1016/j.jocm.2017.08.001
- ISSN
- 1755-5345
- Abstract
- It is well known that estimating the parameters of an integrated choice and latent variable (ICLV) model is not a trivial undertaking. The log-likelihood of an ICLV model cannot be evaluated analytically, and can only be evaluated by a simulation that requires large numbers of sample draws. While conducting simulation-based model estimations, researchers often encounter an estimation failure. Sohn (2017) suggests a novel estimation method to circumvent the problem by using an expectation-maximization algorithm (EM). However, a drawback of this method continues to be the requirement of a huge amount of computer memory to deal with an augmented covariance matrix. In the present study, this problem was overcome by connecting each latent variable in a structural equation to all individual specific variables. This restriction did not hamper the utility of an ICLV model during empirical experimentation. The main contribution of this study is to introduce a simple method devised to solve large-scale ICLV models.
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Collections - College of Engineering > ETC > 1. Journal Articles
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