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Mathematica > Data Manipulation > Statistical Data Analysis > Probability & Statistics > Statistical Distribution Functions > SurvivalFunction >
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SurvivalFunction

SurvivalFunction
gives the survival function for the symbolic distribution dist evaluated at x.
SurvivalFunction
gives the multivariate survival function for the symbolic distribution dist evaluated at .
SurvivalFunction[dist]
gives the survival function as a pure function.
MORE INFORMATION
  • SurvivalFunction gives the probability that an observed value is greater than x.
EXAMPLESCLOSE ALL
Basic Examples   (4)
A survival function for a continuous univariate distribution:
A survival function for a discrete univariate distribution:
A survival function for a continuous multivariate distribution:
A survival function for a discrete multivariate distribution:
A survival function for a continuous univariate distribution:
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A survival function for a discrete univariate distribution:
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A survival function for a continuous multivariate distribution:
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A survival function for a discrete multivariate distribution:
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Scope   (19)
Obtain exact numeric results:
Obtain a machine-precision result:
Obtain a result at any precision for a continuous distribution:
Obtain a result at any precision for a discrete distribution with inexact parameters:
Survival function for a multivariate distribution:
Obtain a symbolic expression for the survival function:
Survival function for nonparametric distributions:
Compare with the value for the underlying parametric distribution:
Plot the survival function for a histogram distribution:
Closed form expression for the survival function of a kernel mixture distribution:
Plot of the survival function of a bivariate smooth kernel distribution:
Product of independent distributions:
Component mixture distribution:
Quadratic transformation of a discrete distribution:
Censored distribution:
Truncated distribution:
Parameter mixture distribution:
Copula distribution:
Formula distributions defined by its PDF:
Defined by its CDF:
Defined by its survival function:
Marginal distribution:
Properties & Relations   (2)
The sum of the survival function and the CDF is 1:
Compositions of SurvivalFunction and InverseSurvivalFunction give step functions for a discrete distribution:
Possible Issues   (2)
Symbolic closed forms do not exist for some distributions:
Numerical evaluation works:
Substitution of invalid values into symbolic outputs gives results that are not meaningful:
Passing it as an argument, it stays unevaluated:
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