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Simulations show that numerical improvements are acquired in light of a higher percentage of convergence, and smaller standard errors and mean square errors of parameter estimates.
Formulae for the standard errors of parameter estimates in factor analysis are complicated or may not be expressed in closed forms.
The standard errors of parameter values in Equations 5a and 5b are in parentheses below the estimate and P < 0.0001.
The merit of least squares method as described above in any error analysis is its ability to amplify big errors and diminish small errors of parameter.
A jackknife method was used to calculate standard errors of parameter estimates.
Multicollinearity among predictors was also assessed by examination of standard errors of parameter estimates.
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The uncertainty of experimental results may be originated from measuring errors of parameters such as flow rates, temperatures, and etc.
The results show that, the errors of parameters estimated based on small sample size can be considerably large.
Asymptotic standard errors of parameters were derived from the covariance matrix followed by calculation of confidence intervals.
The constructed observer could guarantee the error of state and the error of parameter estimation to asymptotically converge to zero.
Moreover, we also introduce a suboptimal design approach to optimizing the upper bound of the mean-square error of parameter estimation.
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