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  2. Empirical distribution function - Wikipedia

    en.wikipedia.org/wiki/Empirical_distribution...

    In statistics, an empirical distribution function (commonly also called an empirical cumulative distribution function, eCDF) is the distribution function associated with the empirical measure of a sample. [1] This cumulative distribution function is a step function that jumps up by 1/n at each of the n data points. Its value at any specified ...

  3. CDF-based nonparametric confidence interval - Wikipedia

    en.wikipedia.org/wiki/CDF-based_nonparametric...

    In statistics, cumulative distribution function (CDF)-based nonparametric confidence intervals are a general class of confidence intervals around statistical functionals of a distribution. To calculate these confidence intervals, all that is required is an independently and identically distributed (iid) sample from the distribution and known ...

  4. Cumulative distribution function - Wikipedia

    en.wikipedia.org/wiki/Cumulative_distribution...

    This is called the complementary cumulative distribution function (ccdf) or simply the tail distribution or exceedance, and is defined as ¯ = ⁡ (>) = (). This has applications in statistical hypothesis testing , for example, because the one-sided p-value is the probability of observing a test statistic at least as extreme as the one observed.

  5. Weibull distribution - Wikipedia

    en.wikipedia.org/wiki/Weibull_distribution

    Its complementary cumulative distribution function is a stretched exponential function. The Weibull distribution is related to a number of other probability distributions; in particular, it interpolates between the exponential distribution ( k = 1) and the Rayleigh distribution ( k = 2 and λ = 2 σ {\displaystyle \lambda ={\sqrt {2}}\sigma } ).

  6. Energy distance - Wikipedia

    en.wikipedia.org/wiki/Energy_distance

    Energy distance is a statistical distance between probability distributions.If X and Y are independent random vectors in R d with cumulative distribution functions (cdf) F and G respectively, then the energy distance between the distributions F and G is defined to be the square root of

  7. Fréchet distribution - Wikipedia

    en.wikipedia.org/wiki/Fréchet_distribution

    It has the cumulative distribution function ( ) = > . where α > 0 is a shape parameter. It can be generalised to include a location parameter m (the minimum) and a scale parameter s > 0 with the cumulative distribution function

  8. Johnson's SU-distribution - Wikipedia

    en.wikipedia.org/wiki/Johnson's_SU-distribution

    Instead of fitting moments, QPDs are typically fit to empirical CDF data with linear least squares. Johnson's S U {\displaystyle S_{U}} -distribution is also used in the modelling of the invariant mass of some heavy mesons in the field of B-physics .

  9. Q-function - Wikipedia

    en.wikipedia.org/wiki/Q-function

    The Q-function is well tabulated and can be computed directly in most of the mathematical software packages such as R and those available in Python, MATLAB and Mathematica. Some values of the Q -function are given below for reference.