On the likelihood ratio for source identification

On likelihood ratio tests

Likelihood ratio tests are intuitively appealing. Nevertheless, a number of examples are known in which they perform very poorly. The present paper discusses a large class of situations in which this is the case, and analyzes just how intuition misleads us; it also presents an alternative approach which in these situations is optimal. 1. The popularity of likelihood ratio tests Faced with a new...

Likelihood Ratio Tests for

Implementation of the Likelihood Ratio framework for camera identification based on sensor noise patterns

It is investigated how implementing the Likelihood Ratio (LR) framework works out in the case of camera identification based on image sensor specific noise patterns. Two typical case scenarios are considered, one with images of low quality, the other with images of high quality. In both cases, it is possible to obtain statistical distributions having a good fit with the reference data both for ...

An Evaluation of an Adaptive Generalized Likelihood Ratio Charts for Monitoring the Process Mean

When the objective is quick detection both small and large shifts in the process mean with normal distribution, the generalized likelihood ratio (GLR) control charts have better performance as compared to other control charts. Only the fixed parameters are used in Reynolds and Lou’s presented charts. According to the studies, using variable parameters, detect process shifts faster than fixed pa...

Drop the Likelihood Ratio

Objective: To present a geometric ruler to determine predictive values of binary diagnostic test results from prevalence, sensitivity and specificity without calculation. Methods: On logarithmic scale the relationships between pre-/post-test odds and the likelihood ratio as well as sensitivity, speci ficity and the likelihood ratio appear as simple linear (additive) equations. Results: Each of ...