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The robust variance estimator was used to correct variances for correlations of repeated measures from the same state.
Schonlau et al. (2009) used the US Health and Retirement study to compare an Internet sample and general sample and found that adjustments helped to correct variances in sample means but that differences remained large on a number of parameters.
Schonlau et al. (2009) Methodological US Health and Retirement study Adjustments help to correct variances in sample means but differences between a general sample and an internet sample remain large on a number of parameters.
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Appropriate selection of covariates is pertinent to correct variance modeling and selecting the appropriate covariates and variance function is vital to correlation structure selection.
Therefore, the relatively slow convergence to a correct variance for the prior is understandable.
This analysis shows that human subjects converge to the correct variance of the prior with a timescale on the order of a hundred trials.
Also, stabilizing the variance and controlling the influence of individual concentrations (especially at the extremes) satisfies the requirements for correct variance component analysis (and other analysis of variance methodology).
Subjects acquired the correct variance over a slower time-scale, statistically indistinguishable from the first 250 trials of experiment 1 (in a bin-by-bin comparison, there were no significant differences, two sample t-tests, p>0.1).
Based on this we designed a Bayesian inference model that knew the prior's correct variance, but had to infer which of the two means was currently being used.
To calculate statistical parameters and their 95% confidence intervals [CI], we used SAS procedures specific for handling complex sample designs to obtain correct variance estimates (SAS version 9.1, PROC SURVEYFREQ, PROC SURVEYMEAN, PROC SURVEYREG and PROC SURVEYLOGISTIC [20].
For this reason, we used the nnmatch STATA command, which calculates the correct variance and CIs [ 17].
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