Kaniadakis Erlang distribution
The Kaniadakis Erlang distribution is a family of continuous statistical distributions, which is a particular case of the κ-Gamma distribution, when and positive integer. The first member of this family is the κ-exponential distribution of Type I. The κ-Erlang is a κ-deformed version of the Erlang distribution. It is one example of a Kaniadakis distribution.
Characterization
Probability density function
The Kaniadakis κ-Erlang distribution has the following probability density function:valid for and, where is the entropic index associated with the Kaniadakis entropy.
The ordinary Erlang Distribution is recovered as.
Cumulative distribution function
The cumulative distribution function of κ-Erlang distribution assumes the form:valid for, where. The cumulative Erlang distribution is recovered in the classical limit.
Survival distribution and hazard functions
The survival function of the κ-Erlang distribution is given by:The survival function of the κ-Erlang distribution enables the determination of hazard functions in closed form through the solution of the κ-rate equation:where is the hazard function.Family distribution
A family of κ-distributions arises from the κ-Erlang distribution, each associated with a specific value of, valid for and. Such members are determined from the κ-Erlang cumulative distribution, which can be rewritten as:where
with
First member
The first member of the κ-Erlang family is the κ-Exponential distribution of type I, in which the probability density function and the cumulative distribution function are defined as:Second member
The second member of the κ-Erlang family has the probability density function and the cumulative distribution function defined as:Third member
The second member has the probability density function and the cumulative distribution function defined as:Related distributions
- The κ-Exponential distribution of type I is a particular case of the κ-Erlang distribution when ;
- A κ-Erlang distribution corresponds to am ordinary exponential distribution when and ;