Both parabolicity and stochastic completeness have been the subject of a systematic study which led to discovering not only sharp geometric conditions for their validity but also an incredible rich family of tools, techniques and equivalent concepts ranging from maximum principles at infinity, function theoretic tests (Khasʼminskii criterion), comparison techniques etc.
To additionally confirm our conclusion that GC content does not dominate the age-dependent codon usages observed, we performed the following novel information-theoretic test.
The optimal value of λ can be determined by cross validation on regression error, or via an information-theoretic test based on BIC.
We follow in the vein of graph-theoretic tests for homogeneity: Consider each observation to be a vertex of a complete, undirected, weighted graph, G, and assign interpoint distances as edge weights.
After that, there was an introduction in theoretic background and test procedures of the neuropsychological tests.
Theoretic precision and testing error analysis of the sensor has been pursued at the end of the paper.
Finally, the theoretic achievements are tested on the two three-link planar robot manipulators cooperatively handling a common object to illustrate the effectiveness of the proposed approach.
We used an information-theoretic approach to test a priori models that best explain group size and the proportion of females in groups for each species on the landscape.
In order to test this theoretic advantage in stability of the THA, we investigated the dislocation rate of a dual mobility cup used for revision in 49 patients (50 hips) with a history of recurrent dislocation of their THA.
Empirical permutation resampling (p < 0.001) and theoretic Mann–Whitney U test (p < 10−30) statistics indicate that mRNAs corresponding to complex disease SNPs via eQTL associations are likely to be regulated by a larger number of SNPs than expected.
2) Information theoretic methods: The LR test tends to select models with more parameters (e.g., the 2PL model) which are more complex models and may be a better fit to the assessment test data compared to the models with fewer parameters (e.g., 1PL model) (De Ayala 2009; Kang and Cohen 2007).
