Bayesian non‐parametric generation of fully synthetic multivariate categorical data in the presence of structural zeros

2018 ◽  
Vol 181 (3) ◽  
pp. 635-647 ◽  
Author(s):  
Daniel Manrique‐Vallier ◽  
Jingchen Hu
Keyword(s):  
Categorical Data ◽  
Parametric Generation ◽  
Structural Zeros ◽  
Multivariate Categorical ◽  
Non Parametric

scholarly journals MGLM: An R Package for Multivariate Categorical Data Analysis

The R Journal ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 73 ◽  
Author(s):  
Juhyun Kim ◽  
Yiwen Zhang ◽  
Joshua Day ◽  
Hua Zhou
Keyword(s):  
Data Analysis ◽  
Categorical Data ◽  
R Package ◽  
Categorical Data Analysis ◽  
Multivariate Categorical

scholarly journals Simultaneous Edit and Imputation For Household Data with Structural Zeros

2018 ◽  
Vol 7 (4) ◽  
pp. 498-519 ◽  
Author(s):  
Olanrewaju Akande ◽  
Andrés Barrientos ◽  
Jerome P Reiter
Keyword(s):  
Dirichlet Process ◽  
Missing Values ◽  
Secondary Data ◽  
Dirichlet Process Mixture ◽  
Bayesian Hierarchical ◽  
Household Data ◽  
Multivariate Relationships ◽  
Using Data ◽  
Structural Zeros ◽  
Multivariate Categorical

Abstract Multivariate categorical data nested within households often include reported values that fail edit constraints—for example, a participating household reports a child’s age as older than his biological parent’s age—and have missing values. Generally, agencies prefer datasets to be free from erroneous or missing values before analyzing them or disseminating them to secondary data users. We present a model-based engine for editing and imputation of household data based on a Bayesian hierarchical model that includes (i) a nested data Dirichlet process mixture of products of multinomial distributions as the model for the true latent values of the data, truncated to allow only households that satisfy all edit constraints, (ii) a model for the location of errors, and (iii) a reporting model for the observed responses in error. The approach propagates uncertainty due to unknown locations of errors and missing values, generates plausible datasets that satisfy all edit constraints, and can preserve multivariate relationships within and across individuals in the same household. We illustrate the approach using data from the 2012 American Community Survey.

Quantification of Multivariate Categorical Data Considering Typicality of Item

2007 ◽  
Vol 11 (1) ◽  
pp. 35-39 ◽  
Author(s):  
Chi-Hyon Oh ◽  
◽  
Katsuhiro Honda ◽  
Hidetomo Ichihashi ◽  
Keyword(s):  
Numerical Experiment ◽  
Objective Function ◽  
Fuzzy Clustering ◽  
Categorical Data ◽  
The Other ◽  
Additional Parameter ◽  
Homogeneity Analysis ◽  
Degree Of Membership ◽  
Multivariate Categorical

We propose simultaneously applying homogeneity analysis and fuzzy clustering that simultaneously partitions individuals and items in categorical multivariate datasets. This objective function includes two types of memberships. One is conventional membership representing the degree of membership of each individual in each cluster. The other is an additional parameter that represents typicality of item. A numerical experiment demonstrates that our proposal is useful in quantifying categorical data, taking the typicality of each item into account.

Sign in / Sign up

Export Citation Format

Share Document