Results 11 to 20 of about 505,956 (309)
Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection [PDF]
Journal of Dairy Science, 2018 Genomic prediction is applicable to individuals of different breeds. Empirical results to date, however, show limited benefits in using information on multiple breeds in the context of genomic prediction. We investigated a multitask Bayesian model, presented previously by others, implemented in a Bayesian stochastic search variable selection (BSSVS ...
M.P.L. Calus +4 more
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Incorporation of Spatial Information in Bayesian Image Reconstruction: The Maximum Residual Likelihood Criterion [PDF]
Publications of the Astronomical Society of the Pacific, 1992 We have developed a new figure of merit, a "Maximum-Residual-Likelihood" (MRL) statistic, for the goodness of fit for Bayesian image resotration which explicitly incorporates spatial information. The MRL constraint provides a natural means of incorporating the prior knowledge that the residuals contal no spatial structure through teh autocorrelation ...
R. K. Piña, R. C. Puetter
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Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996 SUMMARY Residual maximum likelihood (REML) estimation is often preferred to maximum likelihood estimation as a method of estimating covariance parameters in linear models because it takes account of the loss of degrees of freedom in estimating the mean and produces unbiased estimating equations for the variance parameters.
Gordon K Smyth, Arunas P Verbyla
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Residual maximum likelihood (REML) methods for analysing hydrological data series
Journal of Hydrology, 1996 Abstract Much hydrological data can be displayed as two-way tables with observations classified (for example) by years (rows) and sites (columns), commonly with many missing entries; data classified by three factors or more (e.g. gauge sites within drainage basins; drainage basins; years) can also be put in this form.
Robin T. Clarke
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Estimation of Linear Models of Coregionalization by Residual Maximum Likelihood
European Journal of Soil Science, 2007 Summary Observations of ancillary soil properties spatially correlated to a soil property of interest may be used to increase the precision and reduce the sampling costs of a geostatistical survey. The relationship between such coregionalized properties must be expressed as a linear model of coregionalization but the conventional ...
B. P. Marchant, R. M. Lark
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Maximum Likelihood Estimation of Parameters of Autoregressive Processes with Moving Average Residuals and Other Covariance Matrices with Linear Structure [PDF]
The Annals of Statistics, 1975 The autoregressive process with moving average residuals is a stationary process $\{y_t\}$ satisfying $\sum^p_{s = 0} \beta_sy_{t - s} = \sum^q_{j = 0} \alpha_j\nu_{t - j}$, where the sequence $\{\nu_t\}$ consists of independently identically distributed (unobservable) random variables.
T. W. Anderson
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Maximum likelihood estimation of a stochastic frontier model with residual covariance [PDF]
, 2012 In theoretical literature on productivity, the disturbance terms of the stochastic frontier model are assumed to be independent random variables. In this paper, we consider a stochastic production frontier model with residuals that are both spatially and time-wise correlated.
Kisu Simwaka
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SymmetryAs a powerful tool for characterizing the time-evolution behavior of dynamic systems with delay characteristics, the parameter estimation problem for uncertain delay differential equations has always been a research hotspot in the field of uncertain statistics.
Han Wang, Zhiqiang Zhang, Haiyan Shi
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Description of program using maximum likelihood residual for macromolecular refinement, illustrated by several examples [PDF]
Acta Crystallographica Section A Foundations of Crystallography, 1996 E. J. Dodson +2 more
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Computers & Geosciences, 2006 The variogram is essential for local estimation and mapping of any variable by kriging. The variogram itself must usually be estimated from sample data. The sampling density is a compromise between precision and cost, but it must be sufficiently dense to encompass the principal spatial sources of variance.
R. Webster +3 more
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