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  2. de Moivre's law - Wikipedia

    en.wikipedia.org/wiki/De_Moivre's_law

    De Moivre's Law is a survival model applied in actuarial science, named for Abraham de Moivre. [ 1 ] [ 2 ] [ 3 ] It is a simple law of mortality based on a linear survival function . Definition

  3. Survival analysis - Wikipedia

    en.wikipedia.org/wiki/Survival_analysis

    Survival analysis is a branch of statistics for analyzing the expected duration of time until one event occurs, such as death in biological organisms and failure in mechanical systems. This topic is called reliability theory , reliability analysis or reliability engineering in engineering , duration analysis or duration modelling in economics ...

  4. Survival function - Wikipedia

    en.wikipedia.org/wiki/Survival_function

    The survival function is also known as the survivor function [2] or reliability function. [3] The term reliability function is common in engineering while the term survival function is used in a broader range of applications, including human mortality. The survival function is the complementary cumulative distribution function of the lifetime ...

  5. Life table - Wikipedia

    en.wikipedia.org/wiki/Life_table

    This is particularly the case in non-life insurance (e.g. the pricing of motor insurance can allow for a large number of risk factors, which requires a correspondingly complex table of expected claim rates). However the expression "life table" normally refers to human survival rates and is not relevant to non-life insurance.

  6. Actuarial science - Wikipedia

    en.wikipedia.org/wiki/Actuarial_science

    The computations of life insurance premiums and reserving requirements are rather complex, and actuaries developed techniques to make the calculations as easy as possible, for example "commutation functions" (essentially precalculated columns of summations over time of discounted values of survival and death probabilities). [24]

  7. Bayesian survival analysis - Wikipedia

    en.wikipedia.org/wiki/Bayesian_survival_analysis

    Survival analysis is normally carried out using parametric models, semi-parametric models, non-parametric models to estimate the survival rate in clinical research. However recently Bayesian models [1] are also used to estimate the survival rate due to their ability to handle design and analysis issues in clinical research.

  8. Kaplan–Meier estimator - Wikipedia

    en.wikipedia.org/wiki/Kaplan–Meier_estimator

    An example of a Kaplan–Meier plot for two conditions associated with patient survival. The Kaplan–Meier estimator, [1] [2] also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime data.

  9. Relative survival - Wikipedia

    en.wikipedia.org/wiki/Relative_survival

    The relative survival form of analysis is more complex than "competing risks" but is considered the gold-standard for performing a cause-specific survival analysis. It is based on two rates: the overall hazard rate observed in a diseased population and the background or expected hazard rate in the general or background population.