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Articles 61 - 66 of 66

Full-Text Articles in Design of Experiments and Sample Surveys

Weighting Adjustments For Unit Nonresponse With Multiple Outcome Variables, Sonya L. Vartivarian, Rod Little Nov 2003

Weighting Adjustments For Unit Nonresponse With Multiple Outcome Variables, Sonya L. Vartivarian, Rod Little

The University of Michigan Department of Biostatistics Working Paper Series

Weighting is a common form of unit nonresponse adjustment in sample surveys where entire questionnaires are missing due to noncontact or refusal to participate. Weights are inversely proportional to the probability of selection and response. A common approach computes the response weight adjustment cells based on covariate information. When the number of cells thus created is too large, a coarsening method such as response propensity stratification can be applied to reduce the number of adjustment cells. Simulations in Vartivarian and Little (2002) indicate improved efficiency and robustness of weighting adjustments based on the joint classification of the sample by two …

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Inference For The Population Total From Probability-Proportional-To-Size Samples Based On Predictions From A Penalized Spline Nonparametric Model, Hui Zheng, Rod Little Aug 2003

Inference For The Population Total From Probability-Proportional-To-Size Samples Based On Predictions From A Penalized Spline Nonparametric Model, Hui Zheng, Rod Little

The University of Michigan Department of Biostatistics Working Paper Series

Inference about the finite population total from probability-proportional-to-size (PPS) samples is considered. In previous work (Zheng and Little, 2003), penalized spline (p-spline) nonparametric model-based estimators were shown to generally outperform the Horvitz-Thompson (HT) and generalized regression (GR) estimators in terms of the root mean squared error. In this article we develop model-based, jackknife and balanced repeated replicate variance estimation methods for the p-spline based estimators. Asymptotic properties of the jackknife method are discussed. Simulations show that p-spline point estimators and their jackknife standard errors lead to inferences that are superior to HT or GR based inferences. This suggests that nonparametric …

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Semiparametric Regression Models With Missing Data: The Mathematics In The Work Of Robins Et Al., Menggang Yu, Bin Nan May 2003

Semiparametric Regression Models With Missing Data: The Mathematics In The Work Of Robins Et Al., Menggang Yu, Bin Nan

The University of Michigan Department of Biostatistics Working Paper Series

This review is an attempt to understand the landmark papers of Robins, Rotnitzky, and Zhao (1994) and Robins and Rotnitzky (1992). We revisit their main results and corresponding proofs using the theory outlined in the monograph by Bickel, Klaassen, Ritov, and Wellner (1993). We also discuss an illustrative example to show the details of applying these theoretical results.

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Assessing Left-Handedness With Exact And Approximate Confidence Intervals Appropriate For Samples Of Different Sizes, Virginia Gardner May 2003

Assessing Left-Handedness With Exact And Approximate Confidence Intervals Appropriate For Samples Of Different Sizes, Virginia Gardner

Honors Capstones

Capstone submitted as a graduation requirement for the BSU Honors Program.

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Penalized Spline Nonparametric Mixed Models For Inference About A Finite Population Mean From Two-Stage Samples, Hui Zheng, Rod Little Mar 2003

Penalized Spline Nonparametric Mixed Models For Inference About A Finite Population Mean From Two-Stage Samples, Hui Zheng, Rod Little

The University of Michigan Department of Biostatistics Working Paper Series

Samplers often distrust model-based approaches to survey inference due to concerns about model misspecification when applied to large samples from complex populations. We suggest that the model-based paradigm can work very successfully in survey settings, provided models are chosen that take into account the sample design and avoid strong parametric assumptions. The Horvitz-Thompson (HT) estimator is a simple design-unbiased estimator of the finite population total in probability sampling designs. From a modeling perspective, the HT estimator performs well when the ratios of the outcome values and the inclusion probabilities are exchangeable. When this assumption is not met, the HT estimator …

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Assessing The Accuracy Of A New Diagnostic Test When A Gold Standard Does Not Exist, Todd A. Alonzo, Margaret S. Pepe Oct 1998

Assessing The Accuracy Of A New Diagnostic Test When A Gold Standard Does Not Exist, Todd A. Alonzo, Margaret S. Pepe

UW Biostatistics Working Paper Series

Often the accuracy of a new diagnostic test must be assessed when a perfect gold standard does not exist. Use of an imperfect test biases the accuracy estimates of the new test. This paper reviews existing approaches to this problem including discrepant resolution and latent class analysis. Deficiencies with these approaches are identified. A new approach is proposed that combines the results of several imperfect reference tests to define a better reference standard. We call this the composite reference standard (CRS). Using the CRS, accuracy can be assessed using multistage sampling designs. Maximum likelihood estimates of accuracy and expressions for …

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