The package smicd supports the estimation of linear and linear mixed regression models (random slope and random intercept models) with interval censored dependent variable. Parameter estimates are obtain by a stochastic expectation maximization (SEM) algorithm (Walter et al., 2017). Standard errors are estimated by a non-parametric bootstrap in the linear regression model and by a parametric bootstrap in the linear mixed regression model. To handle departures from the model assumptions transformations (log and Box-Cox) of the interval censored dependent variable are incorporated into the algorithm (Walter et al., 2017). Furthermore, the package smicd has implemented a non-parametric kernel density algorithm for the direct (without covariates) estimation of statistical indicators from interval censored data (Walter and Weimer, 2018; Gross et al., 2017). The standard errors of the statistical indicators are estimated by a non-parametric bootstrap.
The two estimation functions for the linear and linear mixed regression model
are called semLm and semLme. So far, only random
intercept and random slope models are implemented. For both functions
the following methods are available: summary.sem,
print.sem and plot.sem.
The function for the direct estimation of statistical indicators is called
kdeAlgo. The following methods are available:
print.kdeAlgo and plot.kdeAlgo.
An overview of all currently provided functions can be requested by
library(help=smicd).
Walter, P., Gross, M., Schmid, T. and Tzavidis, N. (2017). Estimation of Linear and Non-Linear Indicators using Interval Censored Income Data. FU-Berlin School of Business & Economics, Discussion Paper. Walter, P. and Weimer, K. (2018). Estimating Poverty and Inequality Indicators using Interval Censored Income Data from the German Microcensus. FU-Berlin School of Business & Economics, Discussion Paper. Gro<U+00DF>, M., U. Rendtel, T. Schmid, S. Schmon, and N. Tzavidis (2017). Estimating the density of ethnic minorities and aged people in Berlin: Multivariate Kernel Density Estimation applied to sensitive georeferenced administrative data protected via measurement error. Journal of the Royal Statistical Society: Series A (Statistics in Society), 180.