A computational approach to optimal multivariate designs with respect to stratification and allocation is investigated under the assumptions of fixed total allocation, known number of strata, and the availability of administrative data correlated with thevariables of interest under coefficient-of-variation constraints. This approach uses a penalized objective function that is optimized by simulated annealing through exchanging sampling units and sample allocations among strata. Computational speed is improved through the use of a computationally efficient machine learning method such as K-means to create an initial stratification close to the optimal stratification. The numeric stability of the algorithm has been investigated and parallel processing has been employed where appropriate. Results are presented for both simulated data and USDA's June Agricultural Survey. An R package has also been made available for evaluation.
Nonresponse weighting adjustment using the response propensity score is a popular tool for handling unit nonresponse. Statistical inference after the nonresponse weighting adjustment is an important problem and Taylor linearization method is often used to reflect the effect of estimating the propensity score weights. In this article, we propose an approximate Bayesian approach to handle unit nonresponse with parametric model assumptions on the response probability, but without model assumptions for the outcome variable. The proposed Bayesian method is calibrated to the frequentist inference in that the credible region obtained from the posterior distribution asymptotically matches to the frequentist confidence interval obtained from the Taylor linearization method. The proposed Bayesian method is also extended to incorporate the auxiliary information from full sample. Results from limited simulation studies confirm the validity of the proposed methods. The proposed method is applied to data from a Korean longitudinal survey.
Item nonresponse is frequently encountered in practice. Ignoring missing data can lose efficiency and lead to misleading inference. Fractional imputation is a frequentist approach of imputation for handling missing data. However, the parametric fractional imputation of Kim (2011) may be subject to bias under model misspecification. In this paper, we propose a novel semiparametric fractional imputation method using Gaussian mixture models. The proposed method is computationally efficient and leads to robust estimation. The proposed method is further extended to incorporate the categorical auxiliary information. The asymptotic model consistency and √ n-consistency of the semiparametric fractional imputation estimator are also established. Some simulation studies are presented to check the finite sample performance of the proposed method.
This article has an error and related omission in the literature review, as well as some errors in the specification of the simulation. Neither of these errors affect any results in the paper or conclusions drawn. The error and related omission in the literature review occur on page 122, paragraph 2. The reference, Lavallée and Hidiroglou (1988) is incorrect and should be replaced with a reference to Hidiroglou (1986). Both papers are similar in that they provide methods to optimally stratify and allocate univariate populations into take-all, take-none, and takesome stratum under a coefficient of variation (CV) constraint. However, Lavallée and Hidiroglou (1988) improves on Hidiroglou (1986) by allowing for an arbitrary number of take-some strata. This important contribution should have been included on page 122 paragraph 2, revised below.
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