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The information ratio is often annualized. While it is then common for the numerator to be calculated as the arithmetic difference between the annualized portfolio return and the annualized benchmark return, this is an approximation because the annualization of an arithmetic difference between terms is not the arithmetic difference of the annualized terms. [6]
In information retrieval, the positive predictive value is called precision, and sensitivity is called recall. Unlike the Specificity vs Sensitivity tradeoff, these measures are both independent of the number of true negatives, which is generally unknown and much larger than the actual numbers of relevant and retrieved documents.
Information sensitivity is the control of access to information or knowledge that might result in loss of an advantage or level of security if disclosed to others. [1]
In evidence-based medicine, likelihood ratios are used for assessing the value of performing a diagnostic test.They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists.
Factor analysis of information risk (FAIR) is a taxonomy of the factors that contribute to risk and how they affect each other. It is primarily concerned with establishing accurate probabilities for the frequency and magnitude of data loss events. It is not a methodology for performing an enterprise (or individual) risk assessment.
Sensitivity to 'market risk', the "S" in CAMELS is a complex and evolving measurement area. It was added in 1995 by Federal Reserve and the OCC primarily to address interest rate risk, the sensitivity of all loans and deposits to relatively abrupt and unexpected shifts in interest rates. In 1995 they were also interested in banks lending to ...
The relative risk (RR) or risk ratio is the ratio of the probability of an outcome in an exposed group to the probability of an outcome in an unexposed group. Together with risk difference and odds ratio , relative risk measures the association between the exposure and the outcome.
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. [1] [2] This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on the output.