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We present consistent or robust variance estimation, Wald tests and Hansen's (1982) over-identification tests for the second step GMM that properly reflect the first-step estimated functions and the weak dependence of the data.
Next we prove that, for the regularized problem, we have stable dependence of the data.
And they claim a much better fit to the data (specifically, to the dependence of the data on the temperature) using the quantum model than the classical one.
A number of DIF analyses have been done on the TIMSS data, and rarely had these analyses accounted for the dual dependence of the data.
A number of DIF analyses have been done on the TIMSS data, and rarely had these analyses accounted for the dual dependence of the data (e.g., Innabi and Dodeen 2006; Klieme and Baumert 2001; Wu and Ercikan 2006).
This treatment is currently based on an ad hoc argument and can be improved by accounting for the time dependence of the data densities as well as a more direct estimate of the noise at each observatory (e.g. Finlay et al. (2012)).
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Another limitation of this study lies in dependence on the data collection of the RDP database.
The demonstration of the results' dependence on the data set emphasises the need for direct comparison on the same set of lesions.
Since there are several thousand samples, we preferred the majority voting strategy to lessen the dependence of the data-driven methods on the orders of the samples.
We ask whether linear, L∞ distance estimates remain valid when the Lipschitz continuity hypothesis governing t-dependence of the data is relaxed.
The relevance of distance estimates for state constrained differential inclusions permitting fractional-power time dependence is illustrated by an example in engineering design, where we encounter an isolated, square-root type singularity, concerning the t-dependence of the data.
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CEO of Professional Science Editing for Scientists @ prosciediting.com