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多元分类数据的贝叶斯联合编辑与插补AI 解读

In categorical data, it is typically the case that some combinations of variables are theoretically impossible, such as a 3-year-old child who is married or a man who is pregnant. In practice, however, reported values often include such structural zeros due to, for example, respondent mistakes or data processing errors. To purge data of such errors, many statistical organizations use a process known as edit-imputation. The basic idea is first to select reported values to…

不完整多变量数据的逐项条件独立无应答建模AI 解读

We introduce a nonresponse mechanism for multivariate missing data in which each study variable and its nonresponse indicator are conditionally independent given the remaining variables and their nonresponse indicators. This is a nonignorable missingness mechanism, in that nonresponse for any item can depend on values of other items that are themselves missing. We show that under this itemwise conditionally independent nonresponse assumption, one can define and identify…

通过具有局部依赖的贝叶斯混合模型多重插补分类与连续缺失值AI 解读

We present a nonparametric Bayesian joint model for multivariate continuous and categorical variables, with the intention of developing a flexible engine for multiple imputation of missing values. The model fuses Dirichlet process mixtures of multinomial distributions for categorical variables with Dirichlet process mixtures of multivariate normal distributions for continuous variables. We incorporate dependence between the continuous and categorical variables by (1)…

线性约束下缺失值或错误值的多重插补AI 解读

Many statistical agencies, survey organizations, and research centers collect data that suffer from item nonresponse and erroneous or inconsistent values. These data may be required to satisfy linear constraints, for example, bounds on individual variables and inequalities for ratios or sums of variables. Often these constraints are designed to identify faulty values, which then are blanked and imputed. The data also may exhibit complex distributional features, including…

利用混合隶属度模型估计身份披露风险AI 解读

Statistical agencies and other organizations that disseminate data are obligated to protect data subjects' confidentiality. For example, ill-intentioned individuals might link data subjects to records in other databases by matching on common characteristics (keys). Successful links are particularly problematic for data subjects with combinations of keys that are unique in the population. Hence, as part of their assessments of disclosure risks, many data stewards estimate the…

研究中途更换测量方法所致缺失数据的非参数贝叶斯多重插补AI 解读

Investigators often change how variables are measured during the middle of data-collection, for example, in hopes of obtaining greater accuracy or reducing costs. The resulting data comprise sets of observations measured on two (or more) different scales, which complicates interpretation and can create bias in analyses that rely directly on the differentially measured variables. We develop approaches based on multiple imputation for handling mid-study changes in measurement…

合成后抽样：发布人口普查公共使用微观数据的新方法AI 解读

Many statistical agencies disseminate samples of census microdata, that is, data on individual records, to the public. Before releasing the microdata, agencies typically alter identifying or sensitive values to protect data subjects’ confidentiality, for example by coarsening, perturbing, or swapping data. These standard disclosure limitation techniques distort relationships and distributional features in the original data, especially when applied with high intensity.…