An estimation method for nonignorable nonresponse - ARCHIVED

When a survey response mechanism depends on a variable of interest measured within the same survey and observed for only part of the sample, the situation is one of nonignorable nonresponse. In such a situation, ignoring the nonresponse can generate significant bias in the estimation of a mean or of a total. To solve this problem, one option is the joint modeling of the response mechanism and the variable of interest, followed by estimation using the maximum likelihood method. The main criticism levelled at this method is that estimation using the maximum likelihood method is based on the hypothesis of error normality for the model involving the variable of interest, and this hypothesis is difficult to verify. In this paper, the author proposes an estimation method that is robust to the hypothesis of normality, so constructed that there is no need to specify the distribution of errors. The method is evaluated using Monte Carlo simulations. The author also proposes a simple method of verifying the validity of the hypothesis of error normality whenever nonresponse is not ignorable.