An algorithm based on discrete response regression models suitable to correct the bias of non-response in surveys with several capture tries
The use of survey plans, which contemplate several tries or call-backs when endeavouring to capture individual data, may supply unarguable information in certain sampling situations with non-ignorable non-response. This paper presents an algorithm whose final aim is the estimation of the individual non-response probabilities from a general perspective of discrete response regression models, which includes the well known probit and logit models. It will be assumed that the respondents supply all the variables of interest when they are captured. Nevertheless, the call-backs continue, even after previous captures, for a small number of tries, r, which has been fixed beforehand only for estimating purposes. The different retries or call-backs are supposed to be carried out with different capture intensities. As mentioned above, the response probabilities, which may vary from one individual to another, are sought by discrete response regression models, whose parameters are estimated from conditioned likelihoods evaluated on the respondents only. The algorithm, quick and easy to implement, may be used even when the capture indicator matrix has been partially recorded. Finally, the practical performance of the proposed procedure is tested and evaluated from empirical simulations whose results are undoubtedly encouraging.
conditional likelihood multivariate statistics estimation algorithms discrete response models non-ignorable non-response