Examples of using Parameter estimates in English and their translations into Indonesian
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Ecclesiastic
Parameter estimates from the selected model.
Points that areless likely to actually conform to the local model have less influence on the local model parameter estimates.
Parameter estimates from the selected model.
However, if the errors are not normally distributed,a central limit theorem often nonetheless implies that the parameter estimates will be approximately normally distributed so long as the sample is reasonably.
The idea is to choose the parameter estimates so that the theoretical relation is satisfied as closely as possible.
We can use this method to firstly tell us the best order$p$ of the model(as determined by the AIC above)and provide us with parameter estimates for the$\alpha_i$, which we can then use to form confidence intervals.
Where, and are parameter estimates and is the error term.
A related misconception commonly made using generalized estimation equations(GEE) and mixed models on repeated measures(i.e., for fitting cross-sectional regression) is that the workingcorrelation structure only influences variance of the parameter estimates.
When the parameter estimates have been made, the model is then carefully assessed to determine if the underlying assumptions of the analysis appear plausible.
Inferring is easy when assuming that the errors follow a normal distribution,consequently implying that the parameter estimates and residuals will also be normally distributed conditional on the values of the independent variables.
Optimal'' means that the parameter estimates are found by minimizing some cost function that quantifies the model fitting error, and jointly that the solution is simultaneously optimal with respect to both structure and camera variations.
However, if the errors are not normally distributed,a central limit theorem often nonetheless implies that the parameter estimates will be approximately normally distributed so long as the sample is reasonably The most important application is in data fitting.
Monte Carlo features include saving parameter estimates from the analysis of real data to be used as population and/or coverage values for data generation in a Monte Carlo simulation study.
Inference is particularly straightforward if the errors are assumed to follow a normal distribution,which implies that the parameter estimates and residuals will also be normally distributed conditional on the values of the independent variables.
Results of the statistical testing, and also parameter estimates and standard errors for the numerical coefficients in the linear equations are reported.
Other special features that can be used with MonteCarlo simulation studies include saving parameter estimates from the analysis of real data to be used as population parameter and/or coverage values for data generation in a Monte Carlo simulation study.
Other special features that can be used with MonteCarlo simulation studies include saving parameter estimates from the analysis of real data to be used as population parameter and/or coverage values for data generation in a Monte Carlo simulation study.
The confidence level is the probability that the parameter estimate is within the confidence interval.
This is computed as the Parameter Estimate divided by the Standard Error.
One of the main concerns in the field ofstatistics is how accurately a statistic estimates a parameter.
The key control parameter is the estimated cost(or time) at completion.
Volatility is the most difficult parameter to estimate(all the other parameters are more or less given).
In the population forecast presented at the Gaidar Forum, this parameter is estimated to be higher in 2050- 80 years.
Surely this isn't just a coincidence, surely, somehow or the other,Mikoto had found a way to beat the estimates, the infallible Parameter List.
K= total number of estimated parameters.
The model's parameters are estimated using Bayesian methods.
Model parameters are estimated preferably by maximum likelihood techniques.