Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Masaru Kanba"'
Autor:
Masaru Kanba, Kanta Naito
Publikováno v:
Journal of Data Science. 9:549-564
This paper discusses the selection of the smoothing parameter necessary to implement a penalized regression using a nonconcave penalty function. The proposed method can be derived from a Bayesian viewpoint, and the resultant smoothing parameter is gu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3dcd139a9e68d508e673d0764754bdf
https://doi.org/10.6339/jds.201110_09(4).0005
https://doi.org/10.6339/jds.201110_09(4).0005
Autor:
Akio Tsuji, Hideo Suzuki, Yuichi Koretaka, Masaru Kanba, Toshiyuki Okumura, Stephen Marcella, Shin Kurosawa
Publikováno v:
Journal of Physics D: Applied Physics. 55:243002
Artificial intelligence (AI) will develop further in the area of material technology similar to how it has advanced in the pharmaceutical industry. In this article, we explain how AI is applied in the pharmaceutical industry and in the materials scie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f954c27d46dcea23e2b669149f5561ab
https://doi.org/10.1088/1361-6463/ac3a48
https://doi.org/10.1088/1361-6463/ac3a48
Publikováno v:
Computational Statistics. 25:463-484
This paper is concerned with model selection in spline-based generalized linear mixed model. Exploiting the fact that smoothing parameters can be expressed as the reciprocal ratio of the variances of random effect under the setting of estimation by r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b73e7cd53da546059a7bb1be4a37d36d
https://doi.org/10.1007/s00180-010-0187-3
https://doi.org/10.1007/s00180-010-0187-3