simul.lmp {normalp}

Simulation planning for a linear regression model with errors distributed as an exponential power distribution

Description

This function performs a Monte Carlo simulation to compare least squares estimators and Maximum Likelihood estimators for a linear regression model with errors distributed as an exponential power distribution. The regressors are drawn from an Uniform distribution.

Usage

simul.lmp(n, m, q, data, int=0, sigmap=1, p=2, lp=FALSE)

Arguments

n

Sample size.

m

Number of samples.

q

Number of regressors.

data

A vector of coefficients.

int

Value of the intercept.

sigmap

The scale parameter.

p

The shape parameter.

lp

Logical. If TRUE, it evaluates the coefficients with p known.

Value

The function simul.lmp returns an object of class "simul.lmp". A component of this object is a table of means and variances of the m estimates of the regression coefficients and of the scale paramenter \sigma_p. The summary shows this table and the arguments of the simulation plan. The function plot returns the histograms of the computed estimates.

Author(s)

Angelo M. Mineo

References

Mineo, A.M. (1995) Stima dei parametri di regressione lineare semplice quando gli errori seguono una distribuzione normale di ordine p (p incognito). Annali della Facolt\‘a di Economia dell’Universit\'a di Palermo (Area Statistico-Matematica), pp. 161-186.

Examples

## Simulation of 50 samples of size 10 for a linear regression model with 1 regressor.
simul.lmp(10,50,1,data=1.5,int=1,sigmap=1,p=3,lp=FALSE)