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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 ...
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.
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.
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]
The Class VIII (ages 12–13) book (Punjab Textbook Board) on Islamic Studies reads: "Honesty for non-Muslims is merely a business strategy, while for Muslims it is a matter of faith." The Class V (ages 9–10) book (Punjab Textbook Board) on Social Studies says: "Religion plays a very important role in promoting national harmony.
The survival period is usually reckoned from date of diagnosis or start of treatment. Survival rates are important for prognosis, but because the rate is based on the population as a whole, an individual prognosis may be different depending on newer treatments since the last statistical analysis as well as the overall general health of the patient.
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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.