Evaluating Synthetically Generated Data from Small Sample Sizes: An Experimental Study

In this paper, we propose a method for measuring the similarity low sample tabular data with synthetically generated data with a larger number of samples than original. This process is also known as data augmentation. But significance levels obtained from non-parametric tests are suspect when sample size is small. Our method uses a combination of geometry, topology and robust statistics for hypothesis testing in order to compare the validity of generated data. We also compare the results with common global metric methods available in the literature for large sample size data

Priv Stat Databases

In this paper we propose a method for statistical disclosure limitation of categorical variables that we call Conditional Group Swapping. This approach is suitable for design and strata-defining variables, the cross-classification of which leads to the formation of important groups or subpopulations. These groups are considered important because from the point of view of data analysis it is desirable to preserve analytical characteristics within them. In general data swapping can be quite distorting ([12, 18, 15]), especially for the relationships between the variables not only within the subpopulations but for the overall data. To reduce the damage incurred by swapping, we propose to choose the records for swapping using conditional probabilities which depend on the characteristics of the exchanged records. In particular, our approach exploits the results of propensity scores methodology for the computation of swapping probabilities. The experimental results presented in the paper show good utility properties of the method.CC999999/ImCDC/Intramural CDC HHS/United States2020-03-23T00:00:00Z32206763PMC70874077412vault:3515

Stop or Continue Data Collection: A Nonignorable Missing Data Approach for Continuous Variables

We present an approach to inform decisions about nonresponse follow-up sampling. The basic idea is (i) to create completed samples by imputing nonrespondents' data under various assumptions about the nonresponse mechanisms, (ii) take hypothetical samples of varying sizes from the completed samples, and (iii) compute and compare measures of accuracy and cost for different proposed sample sizes. As part of the methodology, we present a new approach for generating imputations for multivariate continuous data with nonignorable unit nonresponse. We fit mixtures of multivariate normal distributions to the respondents' data, and adjust the probabilities of the mixture components to generate nonrespondents' distributions with desired features. We illustrate the approaches using data from the 2007 U. S. Census of Manufactures