They chose principal component analysis (PCA) to overcome the estimation errors inherent in
sample covariance matrices.
Not exact matches
The repeated measures analysis of
covariance required two postnatal growth measurements within the breastfeeding period, and therefore, the
sample size was reduced to 19 in the fluoxetine group and to 11 in the no medication group.
These include using the same model used to detect the planet instead to fit synthetic, planet - free data (with realistic
covariance properties, and time
sampling identical to the real data), and checking whether the «planet» is still detected; comparing the strength of the planetary signal with similar Keplerian signals injected into the original observations; performing Bayesian model comparisons between planet and no - planet models; and checking how robust the planetary signal is to datapoints being removed from the observations.
To test this hypothesis, we examined regional
covariance of thinning patterns across participants
sampled from the healthy adult lifespan (N = 248; 20 - 89 yrs).
Karspeck et al (submitted) analyse the residual difference between the observations and the Kaplan et al. (1997) analysis using local non-stationary
covariances and then draw a range of
samples from the analysis posterior distribution in order to provide consistent variance at all times and locations.
The main point is that a
sample moment (means, variances,
covariances, correlations) in a self - selected
sample is a biased estimator of the same
sample moment in the population, and we can not sign the bias without knowing the nature of the self - selection process.
if you've got a bivariate random parameters (intercepts and age effects) up and running, and so can estimate a correlation or
covariance between initial growth rate (the tree - specific random additive effect at age = 1) and tree - specific random age coefficient across the
sample, the significance of that correlation /
covariance would be a first crack at this without choosing an age at which to split the
sample.
Factor analyses of autistic traits in clinical ASD and community
samples, using a variety of ASD measurement tools, generally indicate that multiple factors account for the observed
covariance structure of ASD symptoms and traits (Happé and Ronald 2008; Mandy and Skuse 2008).
As expected given the very large
sample size, the χ2 test was significant (χ2 = 22996.62, 116 df, P <.001), indicating that the model estimates do not exactly reproduce the
sample variances and
covariances.
Model Fit comparison for the optimal two - factor ESEM model with
covariances in a validation and a cross-validation
sample.
In the main analyses, we first performed a multivariate test on the association between
sample status and all developmental outcomes in GLM multivariate analyses of
covariance.
The nature of significant interaction effects was determined by examining the main effects of
sample status separately for the different levels of the moderator in GLM analyses of
covariance, to statistically test the
sample differences within the subgroups.