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of 114
pro vyhledávání: '"62J05 62J07"'
Autor:
Kurata, Sumito, Hirose, Kei
In the last two decades, sparse regularization methods such as the LASSO have been applied in various fields. Most of the regularization methods have one or more regularization parameters, and to select the value of the regularization parameter is es
Externí odkaz:
http://arxiv.org/abs/2407.16116
Autor:
Zuo, Yijun
Challenges with data in the big-data era include (i) the dimension $p$ is often larger than the sample size $n$ (ii) outliers or contaminated points are frequently hidden and more difficult to detect. Challenge (i) renders most conventional methods i
Externí odkaz:
http://arxiv.org/abs/2309.01666
Autor:
Tanaka, Shuntaro, Matsui, Hidetoshi
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 eliminate multicollinearity among predictors, which improves the variable s
Externí odkaz:
http://arxiv.org/abs/2306.05702
Autor:
Lee, Sungyoon, Lee, Sokbae
In recent years, there has been a significant growth in research focusing on minimum $\ell_2$ norm (ridgeless) interpolation least squares estimators. However, the majority of these analyses have been limited to an unrealistic regression error struct
Externí odkaz:
http://arxiv.org/abs/2305.12883
We propose a novel $\ell_1+\ell_2$-penalty, which we refer to as the Generalized Elastic Net, for regression problems where the feature vectors are indexed by vertices of a given graph and the true signal is believed to be smooth or piecewise constan
Externí odkaz:
http://arxiv.org/abs/2211.00292
Publikováno v:
Advances in Neural Information Processing Systems 35 (2022): 28776-28789
Kronecker regression is a highly-structured least squares problem $\min_{\mathbf{x}} \lVert \mathbf{K}\mathbf{x} - \mathbf{b} \rVert_{2}^2$, where the design matrix $\mathbf{K} = \mathbf{A}^{(1)} \otimes \cdots \otimes \mathbf{A}^{(N)}$ is a Kronecke
Externí odkaz:
http://arxiv.org/abs/2209.04876
Autor:
Matsui, Hidetoshi
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 variable. It then accounts for the relation of the predictors and the r
Externí odkaz:
http://arxiv.org/abs/2203.10268
Autor:
Matsui, Hidetoshi
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 exogenous variable. The varying-coefficient functional linear model is
Externí odkaz:
http://arxiv.org/abs/2110.12599
In this paper, we propose a novel variable selection approach in the framework of high-dimensional linear models where the columns of the design matrix are highly correlated. It consists in rewriting the initial high-dimensional linear model to remov
Externí odkaz:
http://arxiv.org/abs/2106.05454
Autor:
Yi, Yufei, Neykov, Matey
In this paper, we propose an abstract procedure for debiasing constrained or regularized potentially high-dimensional linear models. It is elementary to show that the proposed procedure can produce $\frac{1}{\sqrt{n}}$-confidence intervals for indivi
Externí odkaz:
http://arxiv.org/abs/2104.03464