{"success":1,"msg":"","color":"rgb(28, 35, 49)","title":"A General Multiply Robust Framework for Combining Probability and Non-Probability Samples in Surveys<\/b>","description":"webinar","title2":"","start":"2021-06-04 14:30","end":"2021-06-04 15:30","responsable":"Isabelle Beaudry <\/i><\/a>","speaker":"David Haziza (University of Ottawa)","id":"44","type":"webinar","timezone":"America\/Santiago","activity":"https:\/\/zoom.us\/j\/93485841580?pwd=SHozNGFLYjU3cmpETzc0UGUzNEs5dz09\r\nPasscode: 357454","abstract":"In recent years, there has been an increased interest in combining probability and nonprobability samples. Non-probability samples are cheaper and quicker to conduct but the resulting estimators are vulnerable to bias as the participation probabilities are unknown. To adjust for the potential bias, estimation procedures based on parametric or nonparametric models have been discussed in the literature. However, the validity of the resulting estimators relies heavily on the validity of the underlying models. We propose a data integration approach by combining multiple outcome regression models and propensity score models. The proposed approach can be used for estimating general parameters including totals, means, distribution functions and percentiles. The resulting estimators are multiply robust in the sense that they remain consistent if all but one model are misspecified. I will present the results from a simulation study that show the benefits of the proposed method in terms of bias and efficiency."}