In a dual frame survey, samples are drawn independently from two overlapping frames that are assumed to cover the population of interest. This article considers the case when at least one of the samples is selected by a complex design involving, e.g., multistage sampling. A "pseudo"-maximum likelihood estimator of a population total or a mean for such dual frame surveys is proposed. An advantage of the proposed estimator is that the same weights are used for all the variables, unlike the estimators of Hartley and Fuller and Burmeister. Asymptotic properties of the estimator are studied, including its efficiency. An alternative "single frame" estimator, based on the design induced by the two separate designs, is also studied. Results of a limited simulation study indicate that our estimator is essentially as efficient as those of Hartley and Fuller and Burmeister and can lead to significant efficiency gains over the single frame estimator.
