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pro vyhledávání: '"BREIDT, F. JAY"'
Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the or
Externí odkaz:
http://arxiv.org/abs/2307.13627
Autor:
Ogle, Stephen M.1,2 (AUTHOR) Stephen.Ogle@colostate.edu, Breidt, F. Jay3,4 (AUTHOR), Del Grosso, Stephen5 (AUTHOR), Gurung, Ram2 (AUTHOR), Marx, Ernie2 (AUTHOR), Spencer, Shannon2 (AUTHOR), Williams, Stephen2 (AUTHOR), Manning, Dale6 (AUTHOR)
Publikováno v:
Scientific Reports. 11/20/2023, Vol. 13 Issue 1, p1-13. 13p.
Autor:
Armstrong, Samuel, Khandelwal, Paahuni, Padalia, Dhruv, Senay, Gabriel, Schulte, Darin, Andales, Allan, Breidt, F. Jay, Pallickara, Shrideep, Pallickara, Sangmi Lee
Publikováno v:
In Environmental Modelling and Software June 2022 152
Akademický článek
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Publikováno v:
Bernoulli 2012, Vol. 18, No. 4, 1361-1385
Consider informative selection of a sample from a finite population. Responses are realized as independent and identically distributed (i.i.d.) random variables with a probability density function (p.d.f.) f, referred to as the superpopulation model.
Externí odkaz:
http://arxiv.org/abs/1211.5468
Autor:
Breidt, F. Jay, Opsomer, Jean D.
Publikováno v:
Annals of Statistics 2008, Vol. 36, No. 1, 403-427
Post-stratification is frequently used to improve the precision of survey estimators when categorical auxiliary information is available from sources outside the survey. In natural resource surveys, such information is often obtained from remote sens
Externí odkaz:
http://arxiv.org/abs/0803.2100
Publikováno v:
Bernoulli, 2018 May 01. 24(2), 929-955.
Externí odkaz:
https://www.jstor.org/stable/26491968
Autor:
Breidt, F. Jay, Opsomer, Jean D.
Publikováno v:
Statistical Science 2007, Vol. 22, No. 2, 168-170
Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
Comment: Published in at http://dx.doi.org/10.1214/088342307000000195 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statis
Comment: Published in at http://dx.doi.org/10.1214/088342307000000195 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statis
Externí odkaz:
http://arxiv.org/abs/0710.5012
Publikováno v:
Annals of Statistics 2007, Vol. 35, No. 2, 844-869
An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models are useful for identifying an
Externí odkaz:
http://arxiv.org/abs/0708.1929
Publikováno v:
IMS Lecture Notes Monograph Series 2006, Vol. 52, 1-19
The first-order moving average model or MA(1) is given by $X_t=Z_t-\theta_0Z_{t-1}$, with independent and identically distributed $\{Z_t\}$. This is arguably the simplest time series model that one can write down. The MA(1) with unit root ($\theta_0=
Externí odkaz:
http://arxiv.org/abs/math/0702762