In outcome-dependent sampling, the continuous or binary outcome variable in a regression model is available in advance to guide selection of a sample on which explanatory variables are then measured. Selection probabilities may either be a smooth function of the outcome variable or be based on a stratiﬁcation of the outcome. In many cases, only data from the ﬁnal sample is accessible to the analyst. A maximum likelihood approach for this data conﬁguration is developed here for the ﬁrst time....[Show more]
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