# heuristic: Heuristic method of estimation In DiscreteInverseWeibull: Discrete Inverse Weibull Distribution

## Description

Heuristic method for the estimation of parameters of the discrete inverse Weibull

## Usage

 `1` ```heuristic(x, beta1=1, z = 0.1, r = 0.1, Leps = 0.01) ```

## Arguments

 `x` a vector of sample values `beta1` launch value of the β parameter `z` initial value of width `r` initial value of rate `Leps` tolerance error for the likelihood function

## Details

For a detailed description of the method, have a look at the reference

## Value

a list containig the two estimates of q and β

## References

Jazi M.A., Lai C.-D., Alamatsaz M.H. (2010) A discrete inverse Weibull distribution and estimation of its parameters, Statistical Methodology, 7: 121-132

Drapella A. (1993) Complementary Weibull distribution: unknown or just forgotten, Quality Reliability Engineering International 9: 383-385

`estdiweibull`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```n<-50 q<-0.25 beta<-1.5 x<-rdiweibull(n, q, beta) # estimates using the heuristic algorithm par0<-heuristic(x) par0 # change the default values of some working parameters... par1<-heuristic(x, beta1=2) par1 par2<-heuristic(x, z=0.5) par2 par3<-heuristic(x, r=0.2) par3 par4<-heuristic(x, Leps=0.1) par4 # ...there should be just light differences among the estimates... # ... and among the corresponding values of the loglikelihood functions loglikediw(x, par0[1], par0[2]) loglikediw(x, par1[1], par1[2]) loglikediw(x, par2[1], par2[2]) loglikediw(x, par3[1], par3[2]) loglikediw(x, par4[1], par4[2]) ```