3 papers
math.ST2026
Asymptotic analysis of the Gaussian kernel matrix for partially noisy data in high dimensions
Kensuke Aishima
The Gaussian kernel is one of the most important kernels, applicable to many research fields, including scientific computing and data science. In this paper, we present asymptotic…
math.ST2023
Strong consistency of an estimator by the truncated singular value decomposition for an errors-in-variables regression model with collinearity
Kensuke Aishima
In this paper, we prove strong consistency of an estimator by the truncated singular value decomposition for a multivariate errors-in-variables linear regression model with colline…
math.NA2023
Consistent estimation with the use of orthogonal projections for a linear regression model with errors in the variables
Kensuke Aishima
In this paper, we construct an estimator of an errors-in-variables linear regression model. The regression model leads to a constrained total least squares problems with row and co…