Sentence examples for model selection hypothesis from inspiring English sources

In this article, we propose novel causal model selection hypothesis tests and compare their performance to the AIC and BIC model selection criteria and to a causality inference test (CIT) proposed by Millstein et al. (2009).

Several modifications of the birth-death process originally described by Nee et al. [ 9] were implemented in the MCMC algorithm to describe different patterns of diversification and allow model selection and hypothesis testing.

Because each model is associated with a biological hypothesis, model selection identifies the hypothesis that is best supported by the data.

The ability of ecologists to integrate knowledge across scales in a way that is useful to management has improved dramatically as a result of technological advances, innovations in statistical analysis and study design, and a shift in the philosophy of science favoring model selection over traditional hypothesis testing.

GLMMs are more appropriate than the independent-contrasts approach [36] in situations where a complete phylogeny of the study taxon is unavailable, when categorical variables are included in the analysis, and when model selection, rather than hypothesis testing, is the statistical paradigm used.

Moreover, these approaches go beyond simple phylogeny inference, providing a convenient statistical framework for further model selection and biological hypothesis testing.

First, in contrast to null hypothesis testing, Bayesian model selection offers the possibility to formulate and evaluate informative hypotheses based on prior knowledge by using equality and inequality constraints between groups and compare competing hypotheses that represent specific expectations or views (Klugkist, Laudy, & Hoijtink, 2005).

We used generalized linear model selection to test hypotheses about sea otters' spatial relationship to resources and heterospecifics.

Note that in the model selection approach, should the hypothesis not be rejected, then one should test alternative models and use an information criterion to identify the best model.

Observational studies, such as ours, are better suited to the model selection than to null hypothesis testing [66] [68].

This is because model selection involves cryptic multiple hypothesis testing, a fact that has only rarely been acknowledged or quantified.