Logistic generalized regression estimators

In this paper we study the model-assisted estimation of class frequencies of a discrete response variable by a new survey estimation method, which is closely related to generalized regression estimation. In generalized regression estimation the available auxiliary data are incorporated in the estimation procedure by a linear model fit. Instead of using a linear model for the class indicators, we describe the joint distribution of the class indicators by a multinomial logistic model. Logistic generalized regression estimators are introduced for class frequencies in a population and domains. Monte Carlo experiments were carried out for simulated data and for real data taken from the Labour Force Survey conducted monthly by Statistics Finland. The logistic generalized regression estimation yielded better results than the ordinary regression estimation for small domains and particularly for small class frequencies.