Zobrazeno 1 - 10
of 132
pro vyhledávání: '"Shayle R. Searle"'
Autor:
Shayle R. Searle, Andre I. Khuri
A thoroughly updated guide to matrix algebra and it uses in statistical analysis and features SAS®, MATLAB®, and R throughout This Second Edition addresses matrix algebra that is useful in the statistical analysis of data as well as within statisti
Autor:
Shayle R. Searle, Marvin H. J. Gruber
Provides an easy-to-understand guide to statistical linear models and its uses in data analysis This book defines a broad spectrum of statistical linear models that is useful in the analysis of data. Considerable rewriting was done to make the book m
Publikováno v:
Journal of Applied Mathematics and Decision Sciences, Vol 2007 (2007)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19bca126ff51f045b4cf5932873aff2e
https://doi.org/10.1155/2007/57619
https://doi.org/10.1155/2007/57619
Autor:
Shayle R. Searle
Publikováno v:
CHANCE. 11:20-25
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4552dead0b0cceb70f51a171e4bc6e3b
https://doi.org/10.1080/09332480.1998.10542073
https://doi.org/10.1080/09332480.1998.10542073
Autor:
Shayle R. Searle
Publikováno v:
Linear Algebra and its Applications. 264:291-311
The mixed model of analysis of variance is a linear model in which some terms that would otherwise be unknown constants are, in fact, unobservable realizations of random variables. Estimation procedures for the constants and for the realized random v
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05d843254d534dd9c0263145b7d9e57f
Autor:
Shayle R. Searle
Publikováno v:
CHANCE. 10:15-18
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8eb61a77260f0b27692c605af8746b63
https://doi.org/10.1080/09332480.1997.10542016
https://doi.org/10.1080/09332480.1997.10542016
Autor:
Shayle R. Searle
Publikováno v:
Metrika. 42:215-230
Variance components estimation originated with estimating error variance in analysis of variance by equating error mean square to its expected value. This equating procedure was then extended to random effects models, first for balanced data (for whi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::26fc20e9fe168ff18946347012e54919
https://doi.org/10.1007/bf01894301
https://doi.org/10.1007/bf01894301
Autor:
Shayle R. Searle
Publikováno v:
Statistics and Computing. 5:103-107
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::868f17b7f6fec91c987fefeade784a6d
https://doi.org/10.1007/bf00143939
https://doi.org/10.1007/bf00143939
Autor:
Shayle R. Searle, Charles E. McCulloch
Publikováno v:
The American Statistician. 49:39-42
Difficulties associated with deriving AX = X as a necessary condition from Ay + c being an unbiased estimator of Xβ are discussed in terms of the linear model y ∼ (Xβ, V). We demonstrate two instances in which AX = X and c = 0 are not necessary c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ceb236a841b6662040294670ef630cbe
https://doi.org/10.1080/00031305.1995.10476110
https://doi.org/10.1080/00031305.1995.10476110
Autor:
Shayle R. Searle
Publikováno v:
Linear Models ISBN: 9781118491782
Linear Models
Linear Models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f98c7103e53af328f1366291bb128fb
https://doi.org/10.1002/9781118491782.ch3
https://doi.org/10.1002/9781118491782.ch3