Fu, Ran, authorOpsomer, Jean D., advisorBreidt, F. Jay, committee memberKokoszka, Piotr, committee memberMushinski, David, committee member2017-09-142019-09-122017https://hdl.handle.net/10217/184007We consider three topics in this dissertation: 1) Nonresponse weighting adjustment using penalized spline regression; 2) Improving survey estimators through weight smoothing; and 3) An investigation of weight smoothing estimators under mixed model specifications. In the first topic, we propose a new survey estimator under nonresponse, which only assumes that the response propensity is a smooth function of a known covariate, and we estimate the propensity function by fitting a nonparametric logistic model using penalized spline regression. We obtain the linearization of the nonresponse weighting adjustment estimator with respect to the sampling design and the random response mechanism, allowing us to perform asymptotically correct inference. In a simulation study, we show that the nonparametric estimator remains competitive with a linear logistic estimator when the response propensity function follows a linear logistic model, but performs significantly better when the response propensity function is nonlinear. Beaumont (2008) proposed model-based weight smoothing as a way to improve the efficiency of survey estimators. In the second topic, we extend this work by obtaining the asymptotic properties of this approach with respect to the sampling design and the weight model. The latter is taken to be a lognormal linear regression model. We derive the asymptotic distribution of the estimator and propose a consistent estimator of the asymptotic variance. A Hájek version of the estimator is considered, as well as a replication variance estimator, both of which improve the robustness of weight smoothing against model misspecification. In the third topic, the results from the second topic are extended to models with random effects. Two versions of the estimator are proposed, depending on whether the random effects are predicted or integrated out, and their practical performance is compared through a simulation study.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text