Results 271 to 280 of about 3,367,709 (291)

A RESTRICTED MAXIMUM LIKELIHOOD PROCEDURE FOR ESTIMATING THE VARIANCE FUNCTION OF AN IMMUNOASSAY

Australian & New Zealand Journal of Statistics, 2008
SummaryRestricted maximum likelihood (REML) is a procedure for estimating a variance function in a heteroscedastic linear model. Although REML has been extended to non‐linear models, the case in which the data are dominated by replicated observations with unknown values of the independent variable of interest, such as the concentration of a substance ...
O'Malley, A. James, Smith, Murray H., Sadler, William A.   +2 more
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Restricted maximum likelihood estimation of joint mean‐covariance models

Canadian Journal of Statistics, 2012
AbstractThe class of joint mean‐covariance models uses the modified Cholesky decomposition of the within subject covariance matrix in order to arrive to an unconstrained, statistically meaningful reparameterisation. The new parameterisation of the covariance matrix has two sets of parameters that separately describe the variances and correlations. Thus,
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Asymptotic Optimality of Restricted Maximum Likelihood Estimates for the Mixed Model

Calcutta Statistical Association Bulletin, 1979
In this paper we study the asymptotic optimality of the restricted maximum likelihood estimates of variance components in the mixed model of analysis of variance. Using conceptual design sequences of Miller (1977), under slightly stronger conditions, we show that the restricted maximum likelihood estimates are not only asymptotically normal, but also ...
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An improved approximation to the precision of fixed effects from restricted maximum likelihood

Computational Statistics & Data Analysis, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michael G. Kenward, James H. Roger
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On the Efficiency of Generally Balanced Designs Analysed by Restricted Maximum Likelihood

2001
Restricted maximum likelihood (reml) is commonly used in the analysis of incomplete block designs. With this method, treatment contrasts are estimated by generalized least squares, using an estimated variance-covariance matrix of the observations as if it were known.
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A Ridge Restricted Maximum Likelihood Approach to Spatial Models

2011
Ridge restricted maximum likelihood (RREML) is a new method for regression analysis in linear models with dependent errors. Assume the linear model where the stochastic error terms are not independent, and the covariance structure is a function of some covariance parameter, in this case a spatial covariance parameter.
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Bias Reduction of Autoregressive Estimates in Time Series Regression Model through Restricted Maximum Likelihood

Journal of the American Statistical Association, 2000
Gregory C Reinsel
exaly   +2 more sources

A MAXIMUM LIKELIHOOD ESTIMATOR FOR A MODEL IDENTIFIED WITH VARIANCE‐COVARIANCE RESTRICTIONS

Metroeconomica, 1973
Smith, V. Kerry, Patton, R. A.
openaire   +1 more source

Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2011
Simon N Wood
exaly  

Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model ()

Open Journal of Statistics, 2015
Varathan Nagarajah, Pushpa Wijekoon
exaly