# MLEs: Maximum likelihood estimates of the stress-strength model R =... In ProbYX: Inference for the Stress-Strength Model R = P(Y<X)

## Description

Compute maximum likelihood estimates of R, considered as the parameter of interest. Maximum likelihood estimates of the nuisance parameter are also supplied.

## Usage

 `1` ```MLEs(ydat, xdat, distr) ```

## Arguments

 `ydat` data vector of the sample measurements from Y. `xdat` data vector of the sample measurements from X. `distr` character string specifying the type of distribution assumed for Y and X. Possible choices for `distr` are "exp" (default) for the one-parameter exponential, "norm_EV" and "norm_DV" for the Gaussian distribution with, respectively, equal or unequal variances assumed for the two random variables.

## Details

The two independent random variables Y and X with given distribution `distr` are measurements of a certain characteristics on two different populations. For the relationship of the parameter of interest (R) and nuisance parameters with the original parameters of `distr`, look at the details in `loglik`.

## Value

Vector of estimetes of the nuisance parameters and the R quantity (parameter of interest), respectively.

Giuliana Cortese

## References

Kotz S, Lumelskii Y, Pensky M. (2003). The Stress-Strength Model and its Generalizations. Theory and Applications. World Scientific, Singapore.

## See Also

`loglik`, `Prob`

## Examples

 ```1 2 3 4 5 6``` ``` # data from the first population Y <- rnorm(15, mean=5, sd=1) # data from the second population X <- rnorm(10, mean=7, sd=1.5) # vector of MLEs for the nuisance parameters and the quantity R MLEs(Y, X, "norm_DV") ```

ProbYX documentation built on May 30, 2017, 8:12 a.m.