13 citations · 33 across the 13 of their papers we have counts for
13 papers
Clustering-based aggregate value regression
Kei Hirose, Hidetoshi Matsui, Hiroki Masuda
In various practical situations, forecasting of aggregate values rather than individual ones is often our main focus. For instance, electricity companies are interested in forecast…
Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data
Takashi Nicholas Maeda, Shohei Shimizu, Hidetoshi Matsui
We address the problem of inferring the causal direction between a continuous variable and a discrete variable from observational data. For the model , we adopt th…
Reconciling Functional Data Regression with Excess Bases
Tomoya Wakayama, Hidetoshi Matsui
As the development of measuring instruments and computers has accelerated the collection of massive amounts of data, functional data analysis (FDA) has experienced a surge of atten…
Variable screening using factor analysis for high-dimensional data with multicollinearity
Shuntaro Tanaka, Hidetoshi Matsui
Screening methods are useful tools for variable selection in regression analysis when the number of predictors is much larger than the sample size. Factor analysis is used to elimi…
Sparse estimation in ordinary kriging for functional data
Hidetoshi Matsui, Yuya Yamakawa
We introduce a sparse estimation in the ordinary kriging for functional data. The functional kriging predicts a feature given as a function at a location where the data are not obs…
Truncated estimation for varying-coefficient functional linear model
Hidetoshi Matsui
Varying-coefficient functional linear models consider the relationship between a response and a predictor, where the response depends not only the predictor but also an exogenous v…
Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability
Shotaro Watanabe, Hidetoshi Matsui
The binary classification problem has a situation where only biased data are observed in one of the classes. In this paper, we propose a new method to approach the positive and bia…
Sparse varying-coefficient functional linear model
Hidetoshi Matsui
We consider the problem of variable selection in varying-coefficient functional linear models, where multiple predictors are functions and a response is a scalar and depends on an…
Varying-coefficient functional additive models
Hidetoshi Matsui
We extend the varying coefficient functional linear model to the nonlinear model and propose a varying coefficient functional additive model. The proposed method can represent the…
Selection of variables and decision boundaries for functional data via bi-level selection
Hidetoshi Matsui
Sparsity-inducing penalties are useful tools for variable selection and they are also effective for regression settings where the data are functions. We consider the problem of sel…
Quadratic regression for functional response models
Hidetoshi Matsui
We consider the problem of constructing a regression model with a functional predictor and a functional response. We extend the functional linear model to the quadratic model, wher…
Model selection criteria for nonlinear mixed effects modeling
Hidetoshi Matsui
We consider constructing model selection criteria for evaluating nonlinear mixed effects models via basis expansions. Mean functions and random functions in the mixed effects model…
Varying-coefficient modeling via regularized basis functions
Hidetoshi Matsui, Toshihiro Misumi, Shuichi Kawano
We address the problem of constructing varying-coefficient models based on basis expansions along with the technique of regularization. A crucial point in our modeling procedure is…