Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Naomichi Makino"'
Autor:
Akiko Furutani, Lyie Nitta, Seiko Mochida, Naomichi Makino, Yuki Nozawa, Yu Tahara, Shigenobu Shibata
Publikováno v:
Children, Vol 11, Iss 9, p 1083 (2024)
Background/Objectives: Irregular lifestyles, such as sleep deprivation and disordered eating, disrupt the circadian clock and are linked to health issues. This study investigates the relationship among chronotypes, social jet lag, night-snacking habi
Externí odkaz:
https://doaj.org/article/fc093383db34428492d05318cbd07591
Autor:
Mai Kuwahara, Yu Tahara, Lyie Nitta, Akiko Furutani, Seiko Mochida, Naomichi Makino, Yuki Nozawa, Shigenobu Shibata
Publikováno v:
Frontiers in Nutrition, Vol 10 (2023)
IntroductionIn Japan, breakfast styles are categorized into five groups; Japanese breakfast (JB; rice and miso soup), Western breakfast (WB; bread and milk), Japanese-Western breakfast (J-WB; alternative daily serving), cereal breakfast (CB), and bre
Externí odkaz:
https://doaj.org/article/da30ed0b7b194572b98ed1c88859ff7e
Publikováno v:
Attachment & Human Development. 25:311-321
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eecf6e3b430de9d24822533c02d29c8d
https://doi.org/10.1080/14616734.2023.2187851
https://doi.org/10.1080/14616734.2023.2187851
Publikováno v:
Advances in Principal Component Analysis
Nonlinear Principal Component Analysis and Its Applications ISBN: 9789811001574
Nonlinear Principal Component Analysis and Its Applications ISBN: 9789811001574
Principal components analysis (PCA) is a popular dimension reduction method and is applied to analyze quantitative data. For PCA to qualitative data, nonlinear PCA can be applied, where the data are quantified by using optimal scaling that nonlinearl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c896727362a61b3a46b46da242fa3080
https://mts.intechopen.com/articles/show/title/variable-selection-in-nonlinear-principal-component-analysis
https://mts.intechopen.com/articles/show/title/variable-selection-in-nonlinear-principal-component-analysis
Autor:
Takae Shinto, Yu Tahara, Aato Watabe, Naomichi Makino, Masataka Tomonaga, Haruo Kimura, Yuki Nozawa, Kazuki Kobayashi, Masaki Takahashi, Shigenobu Shibata
Publikováno v:
SLEEP Advances. 3
Study Objectives Sleep problems, such as accumulated sleep loss and social jetlag (SJL), which is characterized by a discrepancy in a person’s sleep pattern between the weekday and the weekend, are associated with physical and mental health problem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eafe374d925c5165cec34bb7cd678078
https://doi.org/10.1093/sleepadvances/zpac035
https://doi.org/10.1093/sleepadvances/zpac035
Autor:
Yudai Namiki, Yasuyo Hasegawa, Naomichi Makino, Nozomi Suda, Kiyora Kishimoto, Ikko Kawahashi
Publikováno v:
The Proceedings of the Annual Convention of the Japanese Psychological Association. 83:1B-038
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::727f4225f3eb1dc7be11ebff5836e50d
https://doi.org/10.4992/pacjpa.83.0_1b-038
https://doi.org/10.4992/pacjpa.83.0_1b-038
Autor:
Naomichi Makino
Publikováno v:
Psychometrika. 87(3)
Correspondence analysis (CA) is a statistical method for depicting the relationship between two categorical variables, and usually places an emphasis on graphical representations. In this study, we discuss a CA formulation based on canonical correlat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8727b18efb81343b110f6df5303d28ba
https://pubmed.ncbi.nlm.nih.gov/35031941
https://pubmed.ncbi.nlm.nih.gov/35031941
Publikováno v:
Nonlinear Principal Component Analysis and Its Applications ISBN: 9789811001574
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bdebf2548cbd86fded69b7d69746f641
https://doi.org/10.1007/978-981-10-0159-8_8
https://doi.org/10.1007/978-981-10-0159-8_8
Autor:
Naomichi Makino
Publikováno v:
Computational Statistics. 30:279-292
In this study, a recently proposed data-fitting factor analysis (DFFA) procedure is generalized for categorical variable analysis. For generalized DFFA (GDFFA), we develop an alternating least squares algorithm consisting of a multiple quantification
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d11cebf1434cede6207eecc0d7ccb0b2
https://doi.org/10.1007/s00180-014-0536-8
https://doi.org/10.1007/s00180-014-0536-8
This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinar