Results 81 to 90 of about 4,106,343 (229)
Genetics Selection Evolution, 2005 We propose a general Bayesian approach to heteroskedastic error modeling for generalized linear mixed models (GLMM) in which linked functions of conditional means and residual variances are specified as separate linear combinations of fixed and random ...
Kizilkaya Kadir, Tempelman Robert J
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Precise asymptotics for linear mixed models with crossed random effects
Statistical Theory and Related FieldsWe obtain an asymptotic normality result that reveals the precise asymptotic behaviour of the maximum likelihood estimators of parameters for a very general class of linear mixed models containing cross random effects.
Jiming Jiang +2 more
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Forecasting interest rates: A Comparative assessment of some second generation non-linear model [PDF]
Modelling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary models such as ARMA and VAR, but only with moderate success.
Dilip M. Nachane, Jose G. Clavel
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Application of Mixed Linear Models in the Analysis of Road Surface Features
Lithuanian Journal of Statistics, 2015 The data were collected by researchers at the Road Research Institute, in a study investigating the impact of differentfactors on road surface strength.
Jurgita Židanavičiūtė +1 more
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FORECASTING INTEREST RATES - A COMPARATIVE ASSESSMENT OF SOME SECOND GENERATION NON-LINEAR MODELS [PDF]
Modelling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary models such as ARMA and VAR, but only with moderate success.
Dilip M. Nachane, Jose G. Clavel
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Small area estimation for spatially correlated populations - a comparison of direct and indirect model-based methods [PDF]
, 2007 Linear mixed models underpin many small area estimation (SAE) methods. In this paper we investigate SAE based on linear models with spatially correlated small area effects where the neighbourhood structure is described by a contiguity matrix. Such models
Chambers, Ray +2 more
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ON THE ESTIMATION AND PREDICTION IN MIXED LINEAR MODELS
Scientia Agricola, 1998 Beginning with the classical Gauss-Markov Linear Model for mixed effects and using the technique of the Lagrange multipliers to obtain an alternative method for the estimation of linear predictors. A structural method is also discussed in order to obtain
LÓPEZ L.A., IEMMA A.F.
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Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression
Journal of Statistical Software, 2014 Inference in quantile analysis has received considerable attention in the recent years. Linear quantile mixed models (Geraci and Bottai 2014) represent a ?exible statistical tool to analyze data from sampling designs such as multilevel, spatial, panel or
Marco Geraci
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Robust MM-Estimation and Inference in Mixed Linear Models [PDF]
Mixed linear models are used to analyse data in many settings. These models generally rely on the normality assumption and are often fitted by means of the maximum likelihood estimator (MLE) or the restricted maximum likelihood estimator (REML). However,
Samuel Copt, Stephane Heritier
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AnimalsCongenital developmental abnormalities in piglets, such as intersex and aproctia, adversely affect survival rates, growth performance, and genetic breeding efficiency in pig populations.
Yajun Li +15 more
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