Assessing robustness of designs for random effects parameters for nonlinear mixed-effects modelsNonlinear mixed-effects modelsOptimal designRandom effectsRobust designsOptimal designs for nonlinear models are dependent on the choice of parameter values. Various methods have been proposed to provide designs ...
there are various ways to estimate the two-way random effect model in econometrics. Using the Grunfeld's data set <http://www.wiley.com/legacy/wileychi/baltagi3e/data_sets.html> the following -xtmixed- instruction produces estimates for parameters and standard deviations that are identical to th...
Random-effects contrasts, specified as the comma-separated pair consisting of'REContrast'and anm-by-qmatrixK, whereqis the number of random effects parameters inlme. The columns ofK(left to right) correspond to the rows of the random-effects best linear unbiased predictor vectorB(top to bottom...
Re: st: how to output random effects parameters using outreg2? From: Maarten Buis <maartenlbuis@gmail.com> Prev by Date: st: roctg Next by Date: Re: st: how to output random effects parameters using outreg2? Previous by thread: st: roctg Next by thread: Re: st: how to output...
Random-effects contrasts, specified as the comma-separated pair consisting of'REContrast'and anm-by-qmatrixK, whereqis the number of random effects parameters inlme. The columns ofK(left to right) correspond to the rows of the random-effects best linear unbiased predictor vectorB(top to bottom...
Error: number of observations (=1080) <= number of random effects (=1479) for term (1 + Index1 + LSI | ID); the random-effects parameters and the residual variance (or scale parameter) are probably unidentifiable I did test the within-person variance for the LSI variable and the ...
The individual values of the adjustments made to intercepts and slopes are calculated once the random-effects parameters have been estimated. Formally, these adjustments, referenced as Best Linear Unbiased Predictors (or blups), are not parameters of the model. Data analysis We illustrate mixed-...
The model is both motivated by and illustrated with a problem from analytical chemistry in which measurement errors are constant for near-zero concentrations and increase proportionally with higher concentrations. The parameters of the calibration curve that relate instrument responses to true concentration...
The model parameters are estimated by the MCMC method. The results show that the individual random effect model can provide more accurate parameter estimates and obtain a scale parameter to describe the distribution of respondents’ abilities, under different within-person variances. The new model has...
Although the AD(1) covariance matrix is parsimonious relative to an unstructured covariance matrix, it may still have too many parameters to be estimated efficiently when n is very large. A parsimonious version of the AD(1) covariance matrix known as the first- order structured antedependence [...