We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Comprehensive studies of Grassmann manifold optimization and sequential candidate set algorithm in a principal fitted component model.
- Authors
Chaeyoung Lee; Jae Keun Yoo
- Abstract
In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.
- Subjects
GRASSMANN manifolds; DIFFERENTIAL topology; ALGORITHMS; DIMENSION reduction (Statistics); ALGORITHMIC randomness
- Publication
Communications for Statistical Applications & Methods, 2022, Vol 29, Issue 6, p721
- ISSN
2287-7843
- Publication type
Article
- DOI
10.29220/CSAM.2022.29.6.721