Search results
Results From The WOW.Com Content Network
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.
The lack of appropriate WEPs would lead to confusion about the likelihood of an attack and to guessing about the period in which it was likely to occur. The language used in the memo lacks words of estimative probability that reduce uncertainty, thus preventing the President and his decisionmakers from implementing measures directed at stopping ...
Specifically, at each stage, after the removal of the highest ordered interaction, the likelihood ratio chi-square statistic is computed to measure how well the model is fitting the data. The highest ordered interactions are no longer removed when the likelihood ratio chi-square statistic becomes significant. [2]
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.
To be clear: These limitations on Wilks’ theorem do not negate any power properties of a particular likelihood ratio test. [3] The only issue is that a χ 2 {\displaystyle \chi ^{2}} distribution is sometimes a poor choice for estimating the statistical significance of the result.
Thus the likelihood-ratio test tests whether this ratio is significantly different from one, or equivalently whether its natural logarithm is significantly different from zero. The likelihood-ratio test, also known as Wilks test , [ 2 ] is the oldest of the three classical approaches to hypothesis testing, together with the Lagrange multiplier ...
There are some drawbacks to the likelihood ratio test. First, when there is a large sample size, even small discrepancies between the model and the data result in model rejection. [ 20 ] [ 21 ] [ 22 ] When there is a small sample size, even large discrepancies between the model and data may not be significant, which leads to underfactoring. [ 20 ]
Given a sample from a normal distribution, whose parameters are unknown, it is possible to give prediction intervals in the frequentist sense, i.e., an interval [a, b] based on statistics of the sample such that on repeated experiments, X n+1 falls in the interval the desired percentage of the time; one may call these "predictive confidence intervals".