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• 1. Semiparametric quantile regression imputation for a complex survey with application to the Conservation Effects Assessment Project

  Articles and reports: 12-001-X201900200001

  Description:

  Development of imputation procedures appropriate for data with extreme values or nonlinear relationships to covariates is a significant challenge in large scale surveys. We develop an imputation procedure for complex surveys based on semiparametric quantile regression. We apply the method to the Conservation Effects Assessment Project (CEAP), a large-scale survey that collects data used in quantifying soil loss from crop fields. In the imputation procedure, we first generate imputed values from a semiparametric model for the quantiles of the conditional distribution of the response given a covariate. Then, we estimate the parameters of interest using the generalized method of moments (GMM). We derive the asymptotic distribution of the GMM estimators for a general class of complex survey designs. In simulations meant to represent the CEAP data, we evaluate variance estimators based on the asymptotic distribution and compare the semiparametric quantile regression imputation (QRI) method to fully parametric and nonparametric alternatives. The QRI procedure is more efficient than nonparametric and fully parametric alternatives, and empirical coverages of confidence intervals are within 1% of the nominal 95% level. An application to estimation of mean erosion indicates that QRI may be a viable option for CEAP.

  Release date: 2019-06-27

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• 2. Replication variance estimation under two-phase sampling Archived

  Articles and reports: 12-001-X201100111448

  Description:

  In two-phase sampling for stratification, the second-phase sample is selected by a stratified sample based on the information observed in the first-phase sample. We develop a replication-based bias adjusted variance estimator that extends the method of Kim, Navarro and Fuller (2006). The proposed method is also applicable when the first-phase sampling rate is not negligible and when second-phase sample selection is unequal probability Poisson sampling within each stratum. The proposed method can be extended to variance estimation for two-phase regression estimators. Results from a limited simulation study are presented.

  Release date: 2011-06-29

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• 3. A comparison of sample set restriction procedures Archived

  Articles and reports: 12-001-X201000111249

  Description:

  For many designs, there is a nonzero probability of selecting a sample that provides poor estimates for known quantities. Stratified random sampling reduces the set of such possible samples by fixing the sample size within each stratum. However, undesirable samples are still possible with stratification. Rejective sampling removes poor performing samples by only retaining a sample if specified functions of sample estimates are within a tolerance of known values. The resulting samples are often said to be balanced on the function of the variables used in the rejection procedure. We provide modifications to the rejection procedure of Fuller (2009a) that allow more flexibility on the rejection rules. Through simulation, we compare estimation properties of a rejective sampling procedure to those of cube sampling.

  Release date: 2010-06-29

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Articles and reports (3) ((3 results))

• 1. Semiparametric quantile regression imputation for a complex survey with application to the Conservation Effects Assessment Project

  Articles and reports: 12-001-X201900200001

  Description:

  Development of imputation procedures appropriate for data with extreme values or nonlinear relationships to covariates is a significant challenge in large scale surveys. We develop an imputation procedure for complex surveys based on semiparametric quantile regression. We apply the method to the Conservation Effects Assessment Project (CEAP), a large-scale survey that collects data used in quantifying soil loss from crop fields. In the imputation procedure, we first generate imputed values from a semiparametric model for the quantiles of the conditional distribution of the response given a covariate. Then, we estimate the parameters of interest using the generalized method of moments (GMM). We derive the asymptotic distribution of the GMM estimators for a general class of complex survey designs. In simulations meant to represent the CEAP data, we evaluate variance estimators based on the asymptotic distribution and compare the semiparametric quantile regression imputation (QRI) method to fully parametric and nonparametric alternatives. The QRI procedure is more efficient than nonparametric and fully parametric alternatives, and empirical coverages of confidence intervals are within 1% of the nominal 95% level. An application to estimation of mean erosion indicates that QRI may be a viable option for CEAP.

  Release date: 2019-06-27

  ---

• 2. Replication variance estimation under two-phase sampling Archived

  Articles and reports: 12-001-X201100111448

  Description:

  In two-phase sampling for stratification, the second-phase sample is selected by a stratified sample based on the information observed in the first-phase sample. We develop a replication-based bias adjusted variance estimator that extends the method of Kim, Navarro and Fuller (2006). The proposed method is also applicable when the first-phase sampling rate is not negligible and when second-phase sample selection is unequal probability Poisson sampling within each stratum. The proposed method can be extended to variance estimation for two-phase regression estimators. Results from a limited simulation study are presented.

  Release date: 2011-06-29

  ---

• 3. A comparison of sample set restriction procedures Archived

  Articles and reports: 12-001-X201000111249

  Description:

  For many designs, there is a nonzero probability of selecting a sample that provides poor estimates for known quantities. Stratified random sampling reduces the set of such possible samples by fixing the sample size within each stratum. However, undesirable samples are still possible with stratification. Rejective sampling removes poor performing samples by only retaining a sample if specified functions of sample estimates are within a tolerance of known values. The resulting samples are often said to be balanced on the function of the variables used in the rejection procedure. We provide modifications to the rejection procedure of Fuller (2009a) that allow more flexibility on the rejection rules. Through simulation, we compare estimation properties of a rejective sampling procedure to those of cube sampling.

  Release date: 2010-06-29

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