Hellinger distance based regression for count data {Rfast2}

Hellinger distance based regression for count data

Description

Hellinger distance based regression for count data.

Usage

hellinger.countreg(y, x, tol = 1e-07, maxiters = 100)

Arguments

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.

Details

We minimise the Hellinger distance instead of the ordinarily used divergence, the Kullback-Leibler. Both of them fall under the \phi-divergence class models and hance this one produces asympottically normal regression coefficients as well.

Value

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.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

See Also

negbin.reg, gee.reg

Examples

y <- rpois(100, 10)
x <- iris[1:100, 1]
a <- hellinger.countreg(y, x)