Nonparametric model-assisted estimators have been proposed to improve estimates of finite population parameters. More efficient estimators are obtained when the parametric model is misspecified due to the flexibility of nonparametric models. In this dissertation, we derive information criteria to select appropriate auxiliary variables to use in an additive model-assisted method....
When data are not missing at random, approaches to reduce nonresponse bias include subsampling nonresponding units and modeling. The objective of this thesis is to develop unbiased and precise model-assisted estimators of the population total that are applicable to data from a complex survey design with nonignorable nonresponse. When information...
Density dependence is an ecological concept concerning the mechanisms of change in the size of a population. The inability to census ecological populations confounds approaches to identify and quantify the level of density dependence. Statistical tests which ignore the presence of measurement error tend to result in misspeci fied type...
In this thesis we develop a theoretical framework for the identification of situations where the equal frequency (EF) or equal variance (EV) subclassification may produce lower bias and/or variance of the estimator. We conduct simulation studies to examine the EF and EV approaches under different types of model misspecification. We...