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  2. Pre- and post-test probability - Wikipedia

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

    It is possible to do a calculation of likelihood ratios for tests with continuous values or more than two outcomes which is similar to the calculation for dichotomous outcomes. For this purpose, a separate likelihood ratio is calculated for every level of test result and is called interval or stratum specific likelihood ratios. [4]

  3. Likelihood-ratio test - Wikipedia

    en.wikipedia.org/wiki/Likelihood-ratio_test

    The finite-sample distributions of likelihood-ratio statistics are generally unknown. [ 10 ] The likelihood-ratio test requires that the models be nested – i.e. the more complex model can be transformed into the simpler model by imposing constraints on the former's parameters.

  4. Likelihood ratios in diagnostic testing - Wikipedia

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

    Likelihood Ratio: An example "test" is that the physical exam finding of bulging flanks has a positive likelihood ratio of 2.0 for ascites. Estimated change in probability: Based on table above, a likelihood ratio of 2.0 corresponds to an approximately +15% increase in probability.

  5. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    The likelihood ratio is central to likelihoodist statistics: the law of likelihood states that degree to which data (considered as evidence) supports one parameter value versus another is measured by the likelihood ratio. In frequentist inference, the likelihood ratio is the basis for a test statistic, the so-called likelihood-ratio test.

  6. Bartlett's test - Wikipedia

    en.wikipedia.org/wiki/Bartlett's_test

    Bartlett's test is a modification of the corresponding likelihood ratio test designed to make the approximation to the distribution better (Bartlett, 1937). Notes [ edit ]

  7. Log-linear analysis - Wikipedia

    en.wikipedia.org/wiki/Log-linear_analysis

    To compare effect sizes of the interactions between the variables, odds ratios are used. Odds ratios are preferred over chi-square statistics for two main reasons: [1] 1. Odds ratios are independent of the sample size; 2. Odds ratios are not affected by unequal marginal distributions.

  8. Wilks' theorem - Wikipedia

    en.wikipedia.org/wiki/Wilks'_theorem

    For example: If the null model has 1 parameter and a log-likelihood of −8024 and the alternative model has 3 parameters and a log-likelihood of −8012, then the probability of this difference is that of chi-squared value of (()) = with = degrees of freedom, and is equal to .

  9. Neyman–Pearson lemma - Wikipedia

    en.wikipedia.org/wiki/Neyman–Pearson_lemma

    In practice, the likelihood ratio is often used directly to construct tests — see likelihood-ratio test.However it can also be used to suggest particular test-statistics that might be of interest or to suggest simplified tests — for this, one considers algebraic manipulation of the ratio to see if there are key statistics in it related to the size of the ratio (i.e. whether a large ...