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Full-Text Articles in Physical Sciences and Mathematics
Robust Likelihood-Based Analysis Of Multivariate Data With Missing Values, Rod Little, An Hyonggin
Robust Likelihood-Based Analysis Of Multivariate Data With Missing Values, Rod Little, An Hyonggin
The University of Michigan Department of Biostatistics Working Paper Series
The model-based approach to inference from multivariate data with missing values is reviewed. Regression prediction is most useful when the covariates are predictive of the missing values and the probability of being missing, and in these circumstances predictions are particularly sensitive to model misspecification. The use of penalized splines of the propensity score is proposed to yield robust model-based inference under the missing at random (MAR) assumption, assuming monotone missing data. Simulation comparisons with other methods suggest that the method works well in a wide range of populations, with little loss of efficiency relative to parametric models when the latter …
Example Of The Impact Of Weights And Design Effects On Contingency Tables And Chi-Square Analysis, David A. Walker, Denise Y. Young
Example Of The Impact Of Weights And Design Effects On Contingency Tables And Chi-Square Analysis, David A. Walker, Denise Y. Young
Journal of Modern Applied Statistical Methods
Many national data sets used in educational research are not based on simple random sampling schemes, but instead are constructed using complex sampling designs characterized by multi-stage cluster sampling and over-sampling of some groups. Incorrect results are obtained from statistical analysis if adjustments are not made for the sampling design. This study demonstrates how the use of weights and design effects impact the results of contingency tables and chi-square analysis of data from complex sampling designs.
Incorporating Sampling Weights Into The Generalizability Theory For Large-Scale Analyses, Christopher W. T. Chiu, Ronald S. Fesco
Incorporating Sampling Weights Into The Generalizability Theory For Large-Scale Analyses, Christopher W. T. Chiu, Ronald S. Fesco
Journal of Modern Applied Statistical Methods
Large scale studies frequently use complex sampling procedures, disproportionate sampling weights, and adjustment techniques to account for potential bias due to nonresponses and to ensure that results from the sample can be generalized to a larger population. Survey researchers are concerned about measurement error and the use of weights in developing models. Consequently, multiple weighting factors are used and these weighting factors are manifested as a final survey (composite) weight available for analysis. We developed a method to incorporate an external weighting factor like this for analyses of measurement errors in the theory of generalizability to provide researchers with a …
Penalized Spline Nonparametric Mixed Models For Inference About A Finite Population Mean From Two-Stage Samples, Hui Zheng, Rod Little
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 …