183,194 research outputs found
A comparison of two methods of estimating propensity scores after multiple imputation
No full textIn many observational studies, analysts estimate treatment effects using propensity scores, e.g. by matching or sub-classifying on the scores. When some values of the covariates are missing, analysts can use multiple imputation to fill in the missing data, estimate propensity scores based on the m completed datasets, and use the propensity scores to estimate treatment effects. We compare two approaches to implement this process. In the first, the analyst estimates the treatment effect using propensity score matching within each completed data set, and averages the m treatment effect estimates. In the second approach, the analyst averages the m propensity scores for each record across the completed datasets, and performs propensity score matching with these averaged scores to estimate the treatment effect. We compare properties of both methods via simulation studies using artificial and real data. The simulations suggest that the second method has greater potential to produce substantial bias reductions than the first, particularly when the missing values are predictive of treatment assignment
R. R. Reiter, ed., Toward an Anthropology of Women
No full textWard Gailey Christine. R. R. Reiter, ed., Toward an Anthropology of Women. In: L'Homme, 1979, tome 19 n°3-4. Les catégories de sexe en anthropologie sociale. pp. 235-236
Further studies on radioactive fallout: progress report no. 2, September 1965
No full textIncludes bibliographical references.Sponsored by U.S. Atomic Energy Commission AT(11-1)-1340.Heavy iodine-131 fallout over the midwestern United States, May 1962 / E. R. Reiter and J. D. Mahlman -- Case study of mass transport from stratosphere to troposphere, not associated with surface fallout / E. R. Reiter and J. D. Mahlman -- Relation of tropopause-level index changes to radioactive fallout fluctuations / J. D. Mahlman -- Behavior of jet streams in potential fallout situations / E. R. Reiter -- Development of computer programs for computation of Montgomery stream functions and plotting of thermodynamic diagrams / J. D. Mahlman and W. Kamm
Estimating risks of identification disclosure in partially synthetic data
No full textTo limit disclosures, statistical agencies and other data disseminators can release partially synthetic, public use microdata sets. These comprise the units originally surveyed, but some collected values, for example sensitive values at high risk of disclosure or values of key identifiers, are replaced with multiple draws from statistical models. Because the original records are on the file, there remain risks of identifications. In this paper, we describe how to evaluate identification disclosure risks in partially synthetic data, accounting for released information from the multiple datasets, the model used to generate synthetic values, and the approach used to select values to synthesize. We illustrate the computations using the Survey of Youths in Custody
Wind forecasting techniques for input into an automatic air traffic control (ATC) system: final report
No full textJuly 1962.CER62ERR51.Prepared by Colorado State University for the Research Division of the Systems Research and Development Service, Federal Aviation Agency under Contract ARDS-450.A. Introduction: purpose and scope of project / Elmar R. Reiter -- B. Checking and preparing of input data / Ben Duran, Genevieve S. Garst, and Elmar R. Reiter -- C. Current status of numerical analysis / Ferdinand Baer -- D. Forecasting experiments with a kinematic extrapolation technique / Elmar R. Reiter and Patricia White -- E. Outlook for future work / Elmar R. Reiter
Estimating propensity scores with missing covariate data using general location mixture models
No full textIn many observational studies, researchers estimate causal effects using propensity scores, e.g., by matching or sub-classifying on the scores. Estimation of propensity scores is complicated when some values of the covariates aremissing. We propose to use multiple imputation to create completed datasets, from which propensity scores can be estimated, with a general location mixture model. The model assumes that the control units are a latent mixture of (i)units whose covariates are drawn from the same distributions as the treated units’ covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated units’ region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations and better balance in the true covariate distributions. We illustrate the benefits of 1 the latent class modeling approach with simulations and with an observationalstudy of the effect of breast feeding on children’s cognitive abilities
Characterization of two new fusion genes in the 8p11 myeloproliferative syndrome (EMS) generated by disruption of FGFR
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