7 citations · 14 across the 5 of their papers we have counts for
5 papers
Functional Multiple-Set Canonical Correlation Analysis Revisited: From Finite-Dimensional Samples to Infinite-Dimensional Populations
Michio Yamamoto, Yoshikazu Terada
We develop a population-level formulation of functional multiple-set canonical correlation analysis (P-FMCCA) for multivariate functional data in an infinite-dimensional Hilbert sp…
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Yan Zeng, Shohei Shimizu, Ruichu Cai +3
Discovering causal structures among latent factors from observed data is a particularly challenging problem. Despite some efforts for this problem, existing methods focus on the si…
Functional Factorial K-means Analysis
Michio Yamamoto, Yoshikazu Terada
A new procedure for simultaneously finding the optimal cluster structure of multivariate functional objects and finding the subspace to represent the cluster structure is presented…
Estimation of oblique structure via penalized likelihood factor analysis
Kei Hirose, Michio Yamamoto
We consider the problem of sparse estimation via a lasso-type penalized likelihood procedure in a factor analysis model. Typically, the model estimation is done under the assumptio…
Sparse estimation via nonconcave penalized likelihood in a factor analysis model
Kei Hirose, Michio Yamamoto
We consider the problem of sparse estimation in a factor analysis model. A traditional estimation procedure in use is the following two-step approach: the model is estimated by max…