Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Yasuo Amemiya"'
Publikováno v:
Journal of Forecasting. 34:1-14
We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state-space model for the parameters of each of the candidat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::634f71bc34af7992041576ca1552572d
https://doi.org/10.1002/for.2315
https://doi.org/10.1002/for.2315
Publikováno v:
Journal of the American Statistical Association. 105(491):1030-1041
Temperature control for a large data center is both important and expensive. On the one hand, many of the components produce a great deal of heat, and on the other hand, many of the components require temperatures below a fairly low threshold for rel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f2f2db14672293d43adc00d19ee1b31
http://pubs.amstat.org/doi/abs/10.1198/jasa.2010.ap09236
http://pubs.amstat.org/doi/abs/10.1198/jasa.2010.ap09236
Autor:
Tenko Raykov, Yasuo Amemiya
Publikováno v:
Structural Equation Modeling: A Multidisciplinary Journal. 15:449-461
A structural equation modeling method for examining time-invariance of variable specificity in longitudinal studies with multiple measures is outlined, which is developed within a confirmatory factor-analytic framework. The approach represents a like
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5609fff9b0e8574816290c478ffed1e4
https://doi.org/10.1080/10705510802154315
https://doi.org/10.1080/10705510802154315
Autor:
Jens C. Eickhoff, Yasuo Amemiya
Publikováno v:
Communications in Statistics - Theory and Methods. 34:1991-2008
Latent variable modeling is commonly used in behavioral, social, and medical science research. The models used in such analysis relate all observed variables to latent common factors. In many applications, the observations are highly non normal or di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7abab630bfa8aa8b5cfa8a9ba79b51c9
https://doi.org/10.1080/03610920500203109
https://doi.org/10.1080/03610920500203109
Publikováno v:
Biostatistics. 7:145-163
SUMMARY In the research of public health, psychology, and social sciences, many research questions investigate the relationship between a categorical outcome variable and continuous predictor variables. The focus of this paper is to develop a model t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1e4d0849c6d2952d2a22d909c6ba7bb
https://doi.org/10.1093/biostatistics/kxi046
https://doi.org/10.1093/biostatistics/kxi046
Publikováno v:
Statistics & Probability Letters. 67:161-171
When the conditional expectation of a complete-data likelihood in an EM algorithm is analytically intractable, Monte Carlo integration is often used to approximate the E-step. While the resulting Monte Carlo EM algorithm (MCEM) is flexible, assessing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a74015e2af94d377b77d4772782c334b
https://doi.org/10.1016/j.spl.2004.01.004
https://doi.org/10.1016/j.spl.2004.01.004
Autor:
William F. Christensen, Yasuo Amemiya
Publikováno v:
Journal of Statistical Planning and Inference. 115:543-564
Factor analysis of multivariate spatial data is considered. A systematic approach for modeling the underlying structure of potentially irregularly spaced, geo-referenced vector observations is proposed. Statistical inference procedures for selecting
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea3ca72da7607ad715ff64089719ccc2
https://doi.org/10.1016/s0378-3758(02)00173-8
https://doi.org/10.1016/s0378-3758(02)00173-8
Autor:
Sock‐Cheng Lewin‐Koh, Yasuo Amemiya
Publikováno v:
Biometrika. 90:85-97
SUMMARY Two moment-based model-fitting procedures for the heteroscedastic factor analysis model are introduced and compared. The procedures produce consistent parameter estimators and asymptotically valid inferences for heteroscedasticity without spe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5f5957e35446e606b4bd78a1cc8682de
https://doi.org/10.1093/biomet/90.1.85
https://doi.org/10.1093/biomet/90.1.85
Publikováno v:
Statistica Sinica.
This paper introduces a spatio-temporal statistical analysis approach ap- propriate for monitoring or managing a physical system in which measurements are taken over dense time resolution but at sparse locations. The proposed approach is designed for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bbfb976e7b4f7530d57edd8227fb5861
https://doi.org/10.5705/ss.2013.262w
https://doi.org/10.5705/ss.2013.262w