Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Yoshimasa Uematsu"'
Autor:
Hajime Takayasu, Kiyoshi Tanaka, Ken-ichiro Konishi, Yoshimasa Uematsu, Takuji Tomari, Yusuke Kumamoto
Publikováno v:
Journal of Pediatric Surgery Open, Vol 8, Iss , Pp 100179- (2024)
Purpose: Despite recent WHO recommendations, antibiotic prophylaxis is routinely continued for several days after surgery. We conducted a retrospective study to evaluate the safety and efficacy of antibiotic prophylaxis cessation within 24 h after ab
Externí odkaz:
https://doaj.org/article/e39711439a7c48f49a1842157c75c1aa
Autor:
Shoko Ogawa, Ken-ichiro Konishi, Kiyoshi Tanaka, Hajime Takayasu, Yoshimasa Uematsu, Takashi Ito, Hiroyuki Takahashi, Yusuke Kumamoto
Publikováno v:
Frontiers in Pediatrics, Vol 12 (2024)
We successfully treated a 4-year-old girl with short bowel syndrome and eosinophilic enterocolitis with teduglutide, a glucagon-like peptide-2 analog. Her eosinophilic enterocolitis was cured without relapse, and we were able to increase enteral nutr
Externí odkaz:
https://doaj.org/article/ae7d4acf93cb480d8199d3c7c38199b7
Autor:
Takashi Yamagata, Yoshimasa Uematsu
Publikováno v:
Journal of Business & Economic Statistics. 41:213-227
This paper investigates estimation of sparsity-induced weak factor (sWF) models, with large cross-sectional and time-series dimensions (N and T, respectively). It assumes that the kth largest eigenvalue of a data covariance matrix grows proportionall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::65a3f596d4241fbefa0fad2337a02595
https://doi.org/10.1080/07350015.2021.2008405
https://doi.org/10.1080/07350015.2021.2008405
Autor:
Yoshimasa Uematsu, Takashi Yamagata
Publikováno v:
Journal of Business & Economic Statistics. 41:126-139
In this paper, we consider statistical inference for high-dimensional approximate factor models. We posit a weak factor structure, in which the factor loading matrix can be sparse and the signal eigenvalues may diverge more slowly than the cross-sect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f7699f3ebe8aa9df7093502224b2e5b6
https://doi.org/10.1080/07350015.2021.2003203
https://doi.org/10.1080/07350015.2021.2003203
Autor:
Yoshimasa Uematsu, Shinya Tanaka
Publikováno v:
The Econometrics Journal. 22:34-56
Summary This study examines high-dimensional forecasting and variable selection via folded-concave penalized regressions. The penalized regression approach leads to sparse estimates of the regression coefficients and allows the dimensionality of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d88734a1e813f5c8a6aff5659dfe2104
https://doi.org/10.1111/ectj.12117
https://doi.org/10.1111/ectj.12117
Autor:
Yoshimasa Uematsu, Takashi Yamagata
Publikováno v:
SSRN Electronic Journal.
In this paper, we consider statistical inference for high-dimensional approximate factor models. We posit a weak factor structure, in which the factor loading matrix can be sparse and the signal eigenvalues may diverge more slowly than the cross-sect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2ec0c3b59dae7d095e573e9e98fbf50
https://doi.org/10.2139/ssrn.3556275
https://doi.org/10.2139/ssrn.3556275
Publikováno v:
IEEE Trans Inf Theory
Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understanding the large-scale response-predictor association network structures
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b948d972b7cfdda0d0d129e840dd6ae2
https://europepmc.org/articles/PMC7970712/
https://europepmc.org/articles/PMC7970712/
Autor:
Takashi Yamagata, Yoshimasa Uematsu
Publikováno v:
SSRN Electronic Journal.
This paper investigates estimation of sparsity-induced weak factor (sWF) models, with large cross-sectional and time-series dimensions (N and T, respectively). It assumes that the kth largest eigenvalue of a data covariance matrix grows proportionall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9c1e37a19c63a1a04232b915457b5368
https://doi.org/10.2139/ssrn.3374750
https://doi.org/10.2139/ssrn.3374750
Publikováno v:
J Am Stat Assoc
Interpretability and stability are two important features that are desired in many contemporary big data applications arising in economics and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d066fdf8860834630f55ef985826e585
http://arxiv.org/abs/1809.05032
http://arxiv.org/abs/1809.05032
Autor:
Shinya Tanaka, Yoshimasa Uematsu
Publikováno v:
SSRN Electronic Journal.
This paper studies macroeconomic forecasting and variable selection using a folded-concave penalized regression with a very large number of predictors. The penalized regression approach leads to sparse estimates of the regression coefficients, and is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fedd989214fe620b132c42e70c5b18f5
https://doi.org/10.2139/ssrn.2927876
https://doi.org/10.2139/ssrn.2927876