Springer Dynamic Mixed Models for Familial Longitudinal Data (Springer Series in Statistics)

This book provides a theoretical foundation for analysis of discrete data such as count and binary data in the longitudinal setup. It presents differences between the familial and longitudinal correlation models, and illustrations of real life data analysis. Review “This is a great book on the combination of longitudinal and familial data in the framework of the generalized linear model. … the book provides an extremely valuable description of complex models in a very logical and understandable way. I strongly recommend it to all researchers dealing with longitudinal familial data with the primary aim of estimating the mean structure by parametric regression models.” (Andreas Ziegler, Biometrical Journal, Vol. 56 (1), 2014)“This book describes models that deal with discrete familial data. … The content is detailed and supported by up-to-date references, many of which are from the author himself, who is an expert in the field. The style is technical, making this book a useful reference for researchers in the area of quasi-likelihood estimation. … In addition, this book can also be used as a textbook for graduate courses or for statisticians interested in familial data.” (Vassilis G. S. Vasdekis, Mathematical Reviews, Issue 2012 c) From the Back Cover This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing ‘working’ correlations based GEE (generalizedestimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up.The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents theillustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University Research Professor at Memorial University in St. John’s, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3