simul.lmp: 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.
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.
# NOT RUN {## 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)
# }