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  2. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    A likelihood ratio is the ratio of any ... The art of choosing the fixed log-likelihood difference is to make the confidence acceptably high while keeping the region ...

  3. Likelihood ratios in diagnostic testing - Wikipedia

    en.wikipedia.org/wiki/Likelihood_ratios_in...

    A high likelihood ratio indicates a good test for a population, and a likelihood ratio close to one indicates that a test may not be appropriate for a population. For a screening test , the population of interest might be the general population of an area.

  4. Likelihood-ratio test - Wikipedia

    en.wikipedia.org/wiki/Likelihood-ratio_test

    In statistics, the likelihood-ratio test is a hypothesis test that involves comparing the goodness of fit of two competing statistical models, typically one found by maximization over the entire parameter space and another found after imposing some constraint, based on the ratio of their likelihoods.

  5. Risk matrix - Wikipedia

    en.wikipedia.org/wiki/Risk_matrix

    Risk is the lack of certainty about the outcome of making a particular choice. Statistically, the level of downside risk can be calculated as the product of the probability that harm occurs (e.g., that an accident happens) multiplied by the severity of that harm (i.e., the average amount of harm or more conservatively the maximum credible amount of harm).

  6. Pre- and post-test probability - Wikipedia

    en.wikipedia.org/wiki/Pre-_and_post-test_probability

    Diagram relating pre- and post-test probabilities, with the green curve (upper left half) representing a positive test, and the red curve (lower right half) representing a negative test, for the case of 90% sensitivity and 90% specificity, corresponding to a likelihood ratio positive of 9, and a likelihood ratio negative of 0.111.

  7. G-test - Wikipedia

    en.wikipedia.org/wiki/G-test

    We can derive the value of the G-test from the log-likelihood ratio test where the underlying model is a multinomial model.. Suppose we had a sample = (, …,) where each is the number of times that an object of type was observed.

  8. Maximum likelihood estimation - Wikipedia

    en.wikipedia.org/wiki/Maximum_likelihood_estimation

    In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data.This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable.

  9. Likelihoodist statistics - Wikipedia

    en.wikipedia.org/wiki/Likelihoodist_statistics

    Likelihoodist statistics or likelihoodism is an approach to statistics that exclusively or primarily uses the likelihood function.Likelihoodist statistics is a more minor school than the main approaches of Bayesian statistics and frequentist statistics, but has some adherents and applications.