Statistical Modeling and Analysis for Complex Data Problems - Duchesne, Pierre; Rémillard, Bruno (Springer US : Imprint: Springer, 2005)
STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of today's more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors-largely from Montreal's GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes-present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida, data mining, estimation of uncertainty for machine learning algorithms, interacting stochastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statistical testing in genetics and for dependent data, statistical analysis of time series analysis, theoretical and applied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area.