regaep | R Documentation |
Estimates parameters of the multiple linear regression model through EM algorithm when error term follows AEP distribution. The regression model is given by
y_{i}=β_{0}+β_{1} x_{i1}+\cdots+ β_{k} x_{ik}+ν_{i},~ i=1,\cdots,n,
where {\boldsymbol{β}}=\bigl(β_{0},β_{1},\cdots,β_{k}\bigr)^{T} are the regression coefficients and ν_i is the error term follows a zero-location AEP distibution.
regaep(y, x)
y |
Vector of response observations of length n. |
x |
An n\times k array of covariate(s). |
A list of estimated regression coefficients, summary of residuals, F statistic, R-square (R^2), adjusted R-square, and inverted observed Fisher information matrix.
Mahdi Teimouri
A. P. Dempster, N. M. Laird, and D. B. Rubin, 1977. Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society Series B, 39, 1-38.
x <- seq(-5, 5, 0.1) y <- 2 + 2*x + raep( length(x), alpha = 1, sigma = 0.5, mu = 0, epsilon = 0.5) regaep(y, x)
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