title: Concurrent generation of multivariate mixed data with variables of dissimilar types.
abstract: Data sets originating from wide range of research studies are composed of multiple variables that are correlated and of dissimilar types, primarily of count, binary/ordinal and continuous attributes. The present paper builds on the previous works on multivariate data generation and develops a framework for generating multivariate mixed data with a pre-specified correlation matrix. The generated data consist of components that are marginally count, binary, ordinal and continuous, where the count and continuous variables follow the generalized Poisson and normal distributions, respectively. The use of the generalized Poisson distribution provides a flexible mechanism which allows under- and over-dispersed count variables generally encountered in practice. A step-by-step algorithm is provided and its performance is evaluated using simulated and real-data scenarios.
authors.affiliation: Department of Public Health Sciences, New Mexico State University, 1335 International mall, RM 102, Las Cruces, NM 88011.
authors.forename: Anup
authors.initials: A
authors.lastname: Amatya
authors.affiliation: Division of Epidemiology and Biostatistics (MC923), University of Illinois at Chicago, 1603 W. Taylor St., Chicago, IL, 60612.
authors.forename: Hakan
authors.initials: H
authors.lastname: Demirtas
keywords: Generalized Poisson; Mutivariate ordinal; discretization