Hellinger distance based regression for count data
Hellinger distance based regression for count data.
hellinger.countreg(y, x, tol = 1e-07, maxiters = 100)
y |
The dependent variable, a numerical vector with integer valued data, counts. |
x |
A numerical matrix with the indendent variables. We add, internally, the first column of ones. |
tol |
The tolerance value to terminate the Newton-Raphson algorithm. |
maxiters |
The max number of iterations that can take place in each regression. |
We minimise the Hellinger distance instead of the ordinarily used divergence, the Kullback-Leibler. Both of them fall under the φ-divergence class models and hance this one produces asympottically normal regression coefficients as well.
A list including:
be |
The regression coefficients. |
seb |
The sandwich standard errors of the coefficients. |
covbe |
The sandwich covariance matrix of the regression coefficients. |
H |
The final Hellinger distance. |
iters |
The number of iterations required by Newton-Raphson. |
Michail Tsagris
R implementation and documentation: Michail Tsagris <mtsagris@uoc.gr>
y <- rpois(100, 10) x <- iris[1:100, 1] a <- hellinger.countreg(y, x)
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