Abstract
Summary The importance of developing useful and appropriate statistical methods for analyzing discrete multivariate data is apparent from the enormous amount of attention this subject has commanded in the literature over the last thirty years. Central to these discussions has been Pearson's X2 statistic and the loglikelihood ratio statistic G2. Our review seeks to consolidate this fragmented literature and develop a unifying theme for much of this research. The traditional X2 and G2 statistics are viewed as members of the power-divergence family of statistics, and are linked through a single real-valued parameter. The principal areas covered in this comparative survey are small-sample comparisons of X2 and G2 under both classical (fixed-cells) assumptions and sparseness assumptions, efficiency comparisons, and various modifications to the test statistics (including parameter estimation for ungrouped data, data-dependent and overlapping cell boundaries, serially dependent data, and smoothing). Finally some future areas for research are discussed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Statistical Review / Revue Internationale de Statistique
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
