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All (3) ((3 results))
- Articles and reports: 11-522-X201300014263Description:
Collecting information from sampled units over the Internet or by mail is much more cost-efficient than conducting interviews. These methods make self-enumeration an attractive data-collection method for surveys and censuses. Despite the benefits associated with self-enumeration data collection, in particular Internet-based data collection, self-enumeration can produce low response rates compared with interviews. To increase response rates, nonrespondents are subject to a mixed mode of follow-up treatments, which influence the resulting probability of response, to encourage them to participate. Factors and interactions are commonly used in regression analyses, and have important implications for the interpretation of statistical models. Because response occurrence is intrinsically conditional, we first record response occurrence in discrete intervals, and we characterize the probability of response by a discrete time hazard. This approach facilitates examining when a response is most likely to occur and how the probability of responding varies over time. The nonresponse bias can be avoided by multiplying the sampling weight of respondents by the inverse of an estimate of the response probability. Estimators for model parameters as well as for finite population parameters are given. Simulation results on the performance of the proposed estimators are also presented.
Release date: 2014-10-31 - Articles and reports: 12-001-X201000211381Description:
Taylor linearization methods are often used to obtain variance estimators for calibration estimators of totals and nonlinear finite population (or census) parameters, such as ratios, regression and correlation coefficients, which can be expressed as smooth functions of totals. Taylor linearization is generally applicable to any sampling design, but it can lead to multiple variance estimators that are asymptotically design unbiased under repeated sampling. The choice among the variance estimators requires other considerations such as (i) approximate unbiasedness for the model variance of the estimator under an assumed model, and (ii) validity under a conditional repeated sampling framework. Demnati and Rao (2004) proposed a unified approach to deriving Taylor linearization variance estimators that leads directly to a unique variance estimator that satisfies the above considerations for general designs. When analyzing survey data, finite populations are often assumed to be generated from super-population models, and analytical inferences on model parameters are of interest. If the sampling fractions are small, then the sampling variance captures almost the entire variation generated by the design and model random processes. However, when the sampling fractions are not negligible, the model variance should be taken into account in order to construct valid inferences on model parameters under the combined process of generating the finite population from the assumed super-population model and the selection of the sample according to the specified sampling design. In this paper, we obtain an estimator of the total variance, using the Demnati-Rao approach, when the characteristics of interest are assumed to be random variables generated from a super-population model. We illustrate the method using ratio estimators and estimators defined as solutions to calibration weighted estimating equations. Simulation results on the performance of the proposed variance estimator for model parameters are also presented.
Release date: 2010-12-21 - Articles and reports: 11-522-X20050019450Description:
Taylor linearization is generally applicable to any sampling design, but it can lead to multiple variance estimators that are asymptotically design unbiased under repeated sampling. Demnati and Rao (2004) proposed a new approach to deriving Taylor linearization variance estimators that leads directly to a unique variance estimator that satisfies the above considerations for general designs.
Release date: 2007-03-02
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Articles and reports (3)
Articles and reports (3) ((3 results))
- Articles and reports: 11-522-X201300014263Description:
Collecting information from sampled units over the Internet or by mail is much more cost-efficient than conducting interviews. These methods make self-enumeration an attractive data-collection method for surveys and censuses. Despite the benefits associated with self-enumeration data collection, in particular Internet-based data collection, self-enumeration can produce low response rates compared with interviews. To increase response rates, nonrespondents are subject to a mixed mode of follow-up treatments, which influence the resulting probability of response, to encourage them to participate. Factors and interactions are commonly used in regression analyses, and have important implications for the interpretation of statistical models. Because response occurrence is intrinsically conditional, we first record response occurrence in discrete intervals, and we characterize the probability of response by a discrete time hazard. This approach facilitates examining when a response is most likely to occur and how the probability of responding varies over time. The nonresponse bias can be avoided by multiplying the sampling weight of respondents by the inverse of an estimate of the response probability. Estimators for model parameters as well as for finite population parameters are given. Simulation results on the performance of the proposed estimators are also presented.
Release date: 2014-10-31 - Articles and reports: 12-001-X201000211381Description:
Taylor linearization methods are often used to obtain variance estimators for calibration estimators of totals and nonlinear finite population (or census) parameters, such as ratios, regression and correlation coefficients, which can be expressed as smooth functions of totals. Taylor linearization is generally applicable to any sampling design, but it can lead to multiple variance estimators that are asymptotically design unbiased under repeated sampling. The choice among the variance estimators requires other considerations such as (i) approximate unbiasedness for the model variance of the estimator under an assumed model, and (ii) validity under a conditional repeated sampling framework. Demnati and Rao (2004) proposed a unified approach to deriving Taylor linearization variance estimators that leads directly to a unique variance estimator that satisfies the above considerations for general designs. When analyzing survey data, finite populations are often assumed to be generated from super-population models, and analytical inferences on model parameters are of interest. If the sampling fractions are small, then the sampling variance captures almost the entire variation generated by the design and model random processes. However, when the sampling fractions are not negligible, the model variance should be taken into account in order to construct valid inferences on model parameters under the combined process of generating the finite population from the assumed super-population model and the selection of the sample according to the specified sampling design. In this paper, we obtain an estimator of the total variance, using the Demnati-Rao approach, when the characteristics of interest are assumed to be random variables generated from a super-population model. We illustrate the method using ratio estimators and estimators defined as solutions to calibration weighted estimating equations. Simulation results on the performance of the proposed variance estimator for model parameters are also presented.
Release date: 2010-12-21 - Articles and reports: 11-522-X20050019450Description:
Taylor linearization is generally applicable to any sampling design, but it can lead to multiple variance estimators that are asymptotically design unbiased under repeated sampling. Demnati and Rao (2004) proposed a new approach to deriving Taylor linearization variance estimators that leads directly to a unique variance estimator that satisfies the above considerations for general designs.
Release date: 2007-03-02
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