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The old new data is analyzed using a Bayesian approach (Dennis, Lee, & Kinnell, 2008), which evaluates the evidence for both the null and the alternative hypothesis.
The study considers the distribution of the test statistic under both the null and the alternative hypothesis.
For robustness purposes, we also applied the generalized least squares (GLS -based unit root tests allowinGLS -basedbreak unitroottestse null allowingrnative hypotheses proposed by Carrión-i-Silvestre et al. (2009).
Unlike the Lumsdaine and Papell (1997) unit root test which is derived assuming no breaks under the null hypothesis, the unit root test of LS allows for breaks under both the null and alternative hypotheses.
As a function of the involved parameters, our study evaluated the test statistics under both the null and alternative hypotheses by estimation of false positives (including significant test results caused merely by changes in blood glucose) and the power to detect true differences in the tumor glucose uptake rate limit.
We generated plasmodes under both the null and alternative hypotheses from the obtained 12 raw datasets.
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We develop distribution theory for the proposed test statistic, both under the null and the alternative hypotheses, and devise a bootstrap scheme to approximate its null distribution.
Overall, the last three statistics, and especially the EB, show a good performance both under the null and the alternative hypotheses, being stable even when procedure rates change.
For both the null model and the alternative model, beta distribution parameters (α and β) were estimated via a maximum likelihood approach, using the R function optim.
We show that after appropriate standardization the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives.
These mice were genotyped for both the null allele and the absence of the Cre transgene, an outcome which indicates the null allele was present in the gamete.
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