12 citations · 42 across the 12 of their papers we have counts for
12 papers
Fast same-step forecast in SUTSE model and its theoretical properties
Wataru Yoshida, Kei Hirose
We consider the problem of forecasting multivariate time series by a Seemingly Unrelated Time Series Equations (SUTSE) model. The SUTSE model usually assumes that error variables a…
Hierarchical clustered multiclass discriminant analysis via cross-validation
Kei Hirose, Kanta Miura, Atori Koie
Linear discriminant analysis (LDA) is a well-known method for multiclass classification and dimensionality reduction. However, in general, ordinary LDA does not achieve high predic…
Sparse multivariate regression with missing values and its application to the prediction of material properties
Keisuke Teramoto, Kei Hirose
In the field of materials science and engineering, statistical analysis and machine learning techniques have recently been used to predict multiple material properties from an expe…
Interpretable modeling for short- and medium-term electricity load forecasting
Kei Hirose
We consider the problem of short- and medium-term electricity load forecasting by using past loads and daily weather forecast information. Conventionally, many researchers have dir…
Robust relative error estimation
Kei Hirose, Hiroki Masuda
Relative error estimation has been recently used in regression analysis. A crucial issue of the existing relative error estimation procedures is that they are sensitive to outliers…
Simple structure estimation via prenet penalization
Kei Hirose, Yoshikazu Terada
We propose a (oduct lastic ), which is a new penalization method for factor analysis models. The penalty is based on the product of a pair of elements in each…
Robust sparse Gaussian graphical modeling
Kei Hirose, Hironori Fujisawa, Jun Sese
Gaussian graphical modeling has been widely used to explore various network structures, such as gene regulatory networks and social networks. We often use a penalized maximum likel…
Full information maximum likelihood estimation in factor analysis with a lot of missing values
Kei Hirose, Sunyong Kim, Yutaka Kano +3
We consider the problem of full information maximum likelihood (FIML) estimation in a factor analysis model when a majority of the data values are missing. The expectation-maximiza…
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…
Readouts for Echo-state Networks Built using Locally Regularized Orthogonal Forward Regression
Ján Dolinský, Kei Hirose, Sadanori Konishi
Echo state network (ESN) is viewed as a temporal non-orthogonal expansion with pseudo-random parameters. Such expansions naturally give rise to regressors of various relevance to a…
Efficient algorithm to select tuning parameters in sparse regression modeling with regularization
Kei Hirose, Shohei Tateishi, Sadanori Konishi
In sparse regression modeling via regularization such as the lasso, it is important to select appropriate values of tuning parameters including regularization parameters. The choic…