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The inverse cumulative distribution function (quantile function) of the logistic distribution is a generalization of the logit function. Its derivative is called the quantile density function. They are defined as follows: (;,) = + ().
The quantiles of a random variable are preserved under increasing transformations, in the sense that, for example, if m is the median of a random variable X, then 2 m is the median of 2 X, unless an arbitrary choice has been made from a range of values to specify a particular quantile. (See quantile estimation, above, for examples of such ...
the quantile function is (; ... The moment estimate of the scale parameter can then be found using the first moment equation as ... (PDF), Journal of Applied ...
The resulting log-metalog distribution is highly shape flexible, has simple closed form PDF and quantile function, can be fit to data with linear least squares, and subsumes the log-logistic distribution is special case.
In particular, the quantile is 1.96; therefore a normal random variable will lie outside the interval in only 5% of cases. The following table gives the quantile z p {\textstyle z_{p}} such that X {\textstyle X} will lie in the range μ ± z p σ {\textstyle \mu \pm z_{p}\sigma } with a specified probability p {\textstyle p} .
Quantile (; ) = Mean: Median ... (PDF) of the magnitude ... with integer shape parameter N and scale parameter . The Rice distribution is a noncentral ...
The following shows how to implement a location–scale family in a statistical package or programming environment where only functions for the "standard" version of a distribution are available. It is designed for R but should generalize to any language and library.
Quantile functions are used in both statistical applications and Monte Carlo methods. The quantile function is one way of prescribing a probability distribution, and it is an alternative to the probability density function (pdf) or probability mass function, the cumulative distribution function (cdf) and the characteristic function.