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This article presents an extensive Monte Carlo simulation study for problems with around a thousand observations and settings including large, moderate, and even "small", correlation ranges.
One thousand observations were drawn from a multivariate normal distribution which was parameterized with the coefficients and variance-covariance matrix from the respective probit estimations.
One thousand observations which are randomly assigned to clusters.
In addition, neural networks have a greater susceptibility to overfitting 45 and several thousand observations are typically required to fit a neural network with confidence.
For both our pairwise comparisons, the sample has less than one thousand observations, and therefore losing a substantial proportion of this already small sample could hurt the robustness of the model results.
In contrast to most modeling programs which only provide predictions at the discrete time points of observation, ADAPT 5 returns for each subject a richly sampled model fits containing a thousand observations (stored in *PLT.csv files).
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Two observations arise.
But two observations stood out.
To conclude with two observations.
Here are two (maybe three) observations.
But two observations can be made here.
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