Maximum likelihood estimate of the standard triangle distribution mode

Maximum likelihood estimate of the standard triangle distribution mode

Usage

standard_triangle_mle(x, debug = FALSE)

Arguments

x

sample from a triangle distribution

debug

if TRUE then the function will check the input parameters and print calculation information

Value

an object of S3 class triangle_mle containing a list with the call, coefficients, variance co-variance matrix, minimum negative log likelihood, number of observations, and the sample

References

Samuel Kotz and Johan Rene van Dorp. Beyond Beta doi:10.1142/5720

Examples

xtest <- c(0.1, 0.25, 0.3, 0.4, 0.45, 0.6, 0.75, 0.8)
standard_triangle_mle(xtest)
#> Triangle Maximum Likelihood Estimates
#> 
#> Call:  standard_triangle_mle(x = xtest) 
#> 
#> Estimates:
#>   Estimate Std.Err
#> a      0.0  0.0000
#> b      1.0  0.0000
#> c      0.3  0.0871
#> 
#> Convergence Code:  NA
#> 	 

xtest <- rtriangle(20, 0, 1, 0.63)
standard_triangle_mle(xtest)
#> Triangle Maximum Likelihood Estimates
#> 
#> Call:  standard_triangle_mle(x = xtest) 
#> 
#> Estimates:
#>   Estimate Std.Err
#> a  0.00000  0.0000
#> b  1.00000  0.0000
#> c  0.67182  0.0556
#> 
#> Convergence Code:  NA
#>