Concurrent generation of multivariate mixed data with variables of dissimilar types. Issue 18 (11th December 2016)

• Record Type: Journal Article
• Authors: Amatya, Anup; Demirtas, Hakan
• Is Part Of: Journal of statistical computation and simulation. Volume 86:Issue 18(2016)
• Page Start: 3595
• Page End: 3607
• Publication Date: 2016-12-11
• Subjects: Generalized Poisson -- mutivariate ordinal -- discretization; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2016.1177530
• Languages: English
• ISSNs: 0094-9655

Abstract

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.