Dr. Wade Herndon (UCSB) 

Title: Testing and Adjusting for Informative Selection in Survey Data

Abstract: When sampling from a finite population, a combination of cost, efficiency, and logistical concerns often leads to a complex sample selection mechanism. Sampling weights are routinely used when analyzing survey data to account for the selection mechanism, and reflect the fact that some subgroups of the population are over or underrepresented in the sample. Due to the selection mechanism, the distribution of the data in the sample may be different from the distribution of the data in the target population. This is known as informative selection. When fitting statistical models to survey data, the sampling weights can be used to account for informative selection. One approach is to construct weighted estimates of model parameters that are consistent under proper model specification, whether or not informative selection is present. Another approach is to use the sampling weights to assess whether or not informative selection is present, and to adjust the model if it is present. This talk will focus on the latter approach to using the weights: assessing the presence of informative selection with a new likelihood ratio test, and adjusting for informative selection with a new semiparametric procedure. Asymptotic theory for the likelihood ratio test under the null hypothesis of noninformative selection and under local alternatives is described, along with a bootstrap version and applications to survey data. The semiparametric approach to adjusting for informative selection uses standard, model-based approaches for fitting linear models combined with nonparametric, weighted estimators to account for the informative sampling design.