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Risk analysis is the process of identifying and assessing risks that may jeopardize an organization's success. It typically fits into a larger risk management framework. Diligent risk analysis helps construct preventive measures to reduce the probability of incidents from occurring, as well as counter-measures to address incidents as they ...
One point is assigned for each of the following risk factors: [citation needed] Age greater than 60 years; Stage III or IV disease; Elevated serum LDH; ECOG/Zubrod performance status of 2, 3, or 4; More than 1 extranodal site; The sum of the points allotted correlates with the following risk groups: Low risk (0-1 points) - 5-year survival of 73%
Graphic breakdown of stratified random sampling. In statistics, stratified randomization is a method of sampling which first stratifies the whole study population into subgroups with same attributes or characteristics, known as strata, then followed by simple random sampling from the stratified groups, where each element within the same subgroup are selected unbiasedly during any stage of the ...
Real prognostic variables are usually known with some uncertainty, may be difficult to measure, and their correlation to the system's state of health may not be exact. Examples of prognostic variables are the age of a vehicle and its odometer reading: the older a car is, and the longer it has been driven, the more worn it can be expected to be ...
Risk-retention pools are technically retaining the risk for the group, but spreading it over the whole group involves transfer among individual members of the group. This is different from traditional insurance, in that no premium is exchanged between members of the group upfront, but instead, losses are assessed to all members of the group.
The log-rank test compares the survival times of two or more groups. This example uses a log-rank test for a difference in survival in the maintained versus non-maintained treatment groups in the aml data. The graph shows KM plots for the aml data broken out by treatment group, which is indicated by the variable "x" in the data.
In finance, risk factors are the building blocks of investing, that help explain the systematic returns in equity market, and the possibility of losing money in investments or business adventures. [ 1 ] [ 2 ] A risk factor is a concept in finance theory such as the capital asset pricing model , arbitrage pricing theory and other theories that ...
Data-driven prognostics usually use pattern recognition and machine learning techniques to detect changes in system states. [3] The classical data-driven methods for nonlinear system prediction include the use of stochastic models such as the autoregressive (AR) model, the threshold AR model, the bilinear model, the projection pursuit, the multivariate adaptive regression splines, and the ...