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Statistical agencies alter values of identifiers to protect re-spondents' confidentiality. When these identifiers are survey design variables , leaving the original survey weights on the file can be a disclosure risk. Additionally, the original weights may not correspond to the altered values, which impacts the quality of design-based (weighted) inferences.(More)
In 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(More)
To 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(More)
Epoxide hydrolases (EHs) of fungal origin have the ability to catalyze the enantioselective hydrolysis of epoxides to their corresponding diols. However, wild type fungal EHs are limited in substrate range and enantioselectivity. Additionally, the production of fungal epoxide hydrolase (EH) by wild-type strains is typically very low. In the present study,(More)
Stochastic search variable selection (SSVS) algorithms provide an appealing and widely used approach for searching for good subsets of predictors while simultaneously estimating posterior model probabilities and model-averaged predictive distributions. This article proposes a two-level generalization of SSVS to account for missing predictors while(More)
In many observational studies, analysts estimate causal effects using propensity scores, e.g. by matching, sub-classifying, or inverse probability weighting based on the scores. Estimation of propensity scores is complicated when some values of the covariates are missing. Analysts can use multiple imputation to create completed data sets from which(More)
Demand of low power circuits design is increasing due to the large growth in portable digital equipment. In this reference adiabatic structure are used that provides a dramatic reduction in power dissipation by recycling some of the energy from output load capacitor and saving power in upper half of the network instead of dissipated as heat. In this paper a(More)
In recent years, a plethora of adiabatic logic styles have been published in literature for ultra low power application, but only very few investigate and compare the performances of these adiabatic logic styles. This paper compares and analyzes the performance of transistor based imperative adiabatic logic styles, using 4-2 compressor circuit as a(More)
Multiple imputation is a commonly used approach to deal with missing values. In this approach, an imputer repeatedly imputes the missing values by taking draws from the posterior predictive distribution for the missing values conditional on the observed values, and releases these completed data sets to analysts. With each completed data set the analyst(More)
Leaf exudates from Aloe species, such as the Southern African Aloe ferox, are used in traditional medicines for both humans and livestock. This includes aloesin, a skin bleaching product that inhibits the synthesis of melanin. Aloesin, (a C-glycoside-5-methylchromone) can be released from aloeresin A, an ester of aloesin, through hydrolysis. The objective(More)