For several learning problems, estimates of the inverse population covariance
are required and often obtained by inverting the model covariance matrix.
According to Hu and Bentler (1998), the SRMR is most sensitive to misspecified factor covariances, while the RMSEA is an indication of a lack of fit of the model to the population covariance matrix.
A non-significant [chi square] indicates that the model fits the data and that the model can reproduce the population covariance matrix (Kelloway, 1998).
The root mean squared error of approximation (RMSEA; Steiger, 1990), which is a measure of the estimated discrepancy between the population and model implied population covariance
matrices per degree of freedom.
It asks the question <<How well would the model, with unknown but optimally chosen parameter values, fit the population covariance
matrix if it were available?
99 indicates that 99% of the sample covariance matrix fits the population covariance
We test the hypothesis that the population covariance
of observed variables equals the covariance matrix implied by a particular model.
Chen's (1979) approach was to assume a given structural form for [Omega], the mean of the prior distribution of the population covariance
matrix [Sigma], and to estimate the posterior mode (or mean, as a result of symmetry), given the prior structural model.
n] equal to the inverse of an estimator of the population covariance
matrix of [S.
The estimates of the cumulative net selection gradients were greater for the assumption of a genetic architecture similar to the modern populations than for the assumption of a genetic architecture similar to the overall population covariance
matrix (Table 5).
Compared to two other variations, the hot-deck distance method performed better in retaining the population covariance
structure in imputed samples.
A non-significant [chi square], as mentioned before, indicates that the model fits the data and that the model can reproduce the population covariance
matrix (Kelloway, 1998).