rcbr.fit.KW2 {RCBR}

NPMLE fitting for random coefficient binary response model

Description

Exact NPMLE fitting requires that the uv argument contain a matrix whose rows represent points in the interior of the locally maximal polytopes determined by the hyperplane arrangement of the observations. If it is not provided it will be computed afresh here; since this can be somewhat time consuming, uv is included in the returned object so that it can be reused if desired. Approximate NPMLE fitting can be achieved by specifying an equally spaced grid of points at which the NPMLE can assign mass using the arguments u and v. If the design matrix X contains only 2 columns, so we have the Cosslett, aka current status, model then the polygons in the prior description collapse to intervals and the default method computes the locally maximal count intervals and passes their interior points to the optimizer of the log likelihood. Alternatively, as in the bivariate case one can specify a grid to obtain an approximate solution.

Usage

rcbr.fit.KW2(x, y, control)

Arguments

x

the design matrix expected to have an intercept column of ones as the first column, the last column is presumed to contain values of the covariate that is designated to have coefficient one.

y

the binary response.

control

is a list of parameters for the fitting, see KW.control for further details.

Value

a list with components:

• uv evaluation points for the fitted distribution

• W estimated mass associated with the uv points

• logLik the loglikelihood value of the fit

• status mosek solution status

Author(s)

Jiaying Gu and Roger Koenker

References

Gu, J. and R. Koenker (2018) Nonparametric maximum likelihood estimation of the random coefficients binary choice model, preprint.