Journal of Biometrics & Biostatistics

Harriet Kitzman

Publications

• Research Article
  Systemize the Probabilistic Discrete Event Systems with Moorepenrose Generalized-inverse Matrix Theory for Cross-sectional Behavioral Data
  Author(s): Ding-Geng (Din) Chen, Xinguang Chen, Feng Lin, Y.L. Lio and Harriet KitzmanDing-Geng (Din) Chen, Xinguang Chen, Feng Lin, Y.L. Lio and Harriet Kitzman
  
  Moore-Penrose (M-P) generalized inverse matrix theory provides a powerful approach to solve an admissible linear-equation system when the inverse of the coefficient matrix does not exist. M-P matrix theory has been used in different areas to solve challenging research questions, including operations research, signal process, and system controls. In this study, we report our work to systemize a probability discrete event systems (PDES) modeling in characterizing the progression of health risk behaviors. A novel PDES model was devised by Lin and Chen to extract and investigate longitudinal properties of smoking multi-stage behavioral progression with cross-sectional survey data. Despite its success, this PDES model requires extra exogenous equations for the model to be solvable and practically implementable. However, exogenous equations are often difficult if not impossible to obtain. E.. Read More»
  DOI: 10.4172/2155-6180.1000219

  Abstract PDF