{"success":1,"msg":"","color":"rgb(28, 35, 49)","title":"Nonparametric Model-Assisted Estimation Using Scrambled Responses from Complex Surveys<\/b>","description":"webinar","title2":"","start":"2021-12-03 14:00","end":"2021-12-03 15:00","responsable":"Isabelle Beaudry <\/i><\/a>","speaker":"Sayed Mostafa","id":"54","type":"webinar","timezone":"America\/Santiago","activity":"https:\/\/zoom.us\/j\/98511183800?pwd=bGFEYVRVV1Z0RFlLOWFQNnAyNmxvZz09\r\nPasscode: 710212","abstract":"The randomized response technique offers an effective way for reducing potential bias resulting from nonresponse and untruthful responses when asking questions about sensitive behaviors or beliefs. The technique is also used for conducting statistical disclosure control of public use data files as is commonly done by the U.S. Census Bureau. In both cases, the technique works by scrambling the actual survey responses using some known randomization model. In the case of asking sensitive survey questions, the scrambling of responses is done by survey respondents and only scrambled responses are collected, whereas in the case of disclosure control, the survey agency implements the randomization of responses after collecting the survey data and prior to releasing it for public use. In this talk, we will consider the problem of estimating the finite population mean of a study variable for which only scrambled responses are available from a complex sample survey. In addition to these scrambled responses, we assume that non-scrambled data about non-sensitive auxiliary variables is available for all population units from administrative records or any other source. We define and study a class of nonparametric model-assisted estimators that make efficient use of available auxiliary information and account for the complex survey design. Asymptotic properties of proposed estimators are derived, and the finite sample performance of these estimators is studied via extensive simulations accounting for a wide range of forms for the relationship between the study and auxiliary variables. The empirical results support the theoretical analyses and suggest that our proposed estimators are superior to existing estimators in most cases. We also discuss the problem of variance estimation and construction of confidence intervals. The proposed methods are illustrated using data from the U.S. Consumer Expenditure Survey."}