Sentences with phrase «statistical model specifications»

Tested multiple statistical model specifications to identify optimal models for propensity scoring marketing leads.

Not exact matches

Any results that are reported to constitute a blinded, independent validation of a statistical model (or mathematical classifier or predictor) must be accompanied by a detailed explanation that includes: 1) specification of the exact «locked down» form of the model, including all data processing steps, algorithm for calculating the model output, and any cutpoints that might be applied to the model output for final classification, 2) date on which the model or predictor was fully locked down in exactly the form described, 3) name of the individual (s) who maintained the blinded data and oversaw the evaluation (e.g., honest broker), 4) statement of assurance that no modifications, additions, or exclusion were made to the validation data set from the point at which the model was locked down and that neither the validation data nor any subset of it had ever been used to assess or refine the model being tested

Important topics include design and task specification, planned group comparisons, behavioral performance metrics, imaging details, data pre-processing, intersubject registration, statistical modeling details for both the individual and group level, and statistical inference including approach to multiple comparisons correction.

The raw MIT model, observational and AOGCM control run data was processed to match the specifications of the three diagnostics before the statistical inference methods were applied.

«Across all model specifications, we find no statistical evidence that home prices near wind turbines were affected -LSB-...] There is no statistical evidence that homes in either the PAPC [post-announcement, pre-construction] or PC [post-construction] periods that sold near turbines (i.e., within a mile or even a half mile) did so for less than similar homes that sold between 3 and 10 away miles in the same period.»

it is important to recognize that an inherent difficulty of testing null hypotheses is that one can not confirm (statistically) the hypothesis of no effect.While robustness checks (reported in the appendix), as well as p values that never approach standard levels of statistical significance, provide some confidence that the results do not depend on model specification or overly strict requirements for statistical significance, one can not entirely dismiss the possibility of a Type II error.

The incomplete specification model, whereby if the influence of one factor in a statistical relationship is politically awkward, then feel free just to leave it out.

I surely wasn't comparing my 4 - parameter and 3 starting values statistical specification, based on some 55 observations, with GC models, that rely on (probably) hundreds of parameters, many many more observations and God knows how much CPU time.

You ably demonstrated yourself that your «rejection» of a constant in the difference term in your model specification was based on a test with low statistical power.