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This important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics (medical statistics). This new edition contains an additional two chapters. The first of these discusses fully parametric models for discrete repeated measures data. The second explores statistical models for time-dependent predictors where there may be feedback between the predictor and response variables.
Through this text, the author aims to make recent developments in the title subject (a modern strategy for the creation of statistical models to solve 'real world' problems) accessible to graduate students and researchers in the field of statistics.
Time series analysis is one of several branches of statistics whose practical importance has increased with the availability of powerful computational tools. Methodology that was originally developed for specialized applications, for example in finance or geophysics, is now widely available within general statistical packages. The second edition of Time Series: A Biostatistical Introduction is an introductory account of time series analysis, written from the perspective of applied statisticians whose interests lie primarily in the biomedical and health sciences. This edition has a stronger focus on substantive applications, in which each statistical analysis is directed at a specific researc...
"This text examines the theory of statistical modelling with generalised linear models. It also looks at applications of the theory to practical problems, using the GLIM4 package"--Provided by publisher.
This text brings together important ideas on the model-based approach to sample survey, which has been developed over the last twenty years. Suitable for graduate students and professional statisticians, it moves from basic ideas fundamental to sampling to more rigorous mathematical modelling and data analysis and includes exercises and solutions.
Non linearity arises in statistical inference in various ways, with varying degrees of severity, as an obstacle to statistical analysis. More entrenched forms of nonlinearity often require intensive numerical methods to construct estimators, and the use of root search algorithms, or one-step estimators, is a standard method of solution. This book provides a comprehensive study of nonlinear estimating equations and artificial likelihood's for statistical inference. It provides extensive coverage and comparison of hill climbing algorithms, which when started at points of nonconcavity often have very poor convergence properties, and for additional flexibility proposes a number of modification to the standard methods for solving these algorithms. The book also extends beyond simple root search algorithms to include a discussion of the testing of roots for consistency, and the modification of available estimating functions to provide greater stability in inference. A variety of examples from practical applications are included to illustrate the problems and possibilities thus making this text ideal for the research statistician and graduate student.
This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.
Parliamentary obstruction, popularly known as the "filibuster," has been a defining feature of the U.S. Senate throughout its history. In this book, Gregory J. Wawro and Eric Schickler explain how the Senate managed to satisfy its lawmaking role during the nineteenth and early twentieth century, when it lacked seemingly essential formal rules for governing debate. What prevented the Senate from self-destructing during this time? The authors argue that in a system where filibusters played out as wars of attrition, the threat of rule changes prevented the institution from devolving into parliamentary chaos. They show that institutional patterns of behavior induced by inherited rules did not re...
A history illustrating the complexity of medical decision making and risk. Still the leading cause of death worldwide, heart disease challenges researchers, clinicians, and patients alike. Each day, thousands of patients and their doctors make decisions about coronary angioplasty and bypass surgery. In Broken Hearts David S. Jones sheds light on the nature and quality of those decisions. He describes the debates over what causes heart attacks and the efforts to understand such unforeseen complications of cardiac surgery as depression, mental fog, and stroke. Why do doctors and patients overestimate the effectiveness and underestimate the dangers of medical interventions, especially when doing so may lead to the overuse of medical therapies? To answer this question, Jones explores the history of cardiology and cardiac surgery in the United States and probes the ambiguities and inconsistencies in medical decision making. Based on extensive reviews of medical literature and archives, this historical perspective on medical decision making and risk highlights personal, professional, and community outcomes.
There is a fundamental contradiction between economics and ecology. Activities that increase well-being by economic criteria often erode ecosystem vitality, and what preserves and enhances environmental well-being is often deemed 'inefficient' to economic demands. Regrettably, in our culture, we usually accord much greater importance to economic concerns than to ecology. However, given many indicators of continued environmental degradation - escalating rates of species extinctions, global warming, the profusion of toxins in our air, water, and soil - it is increasingly urgent that economics be infused with ecological principles. In Culture of Ecology, Robert Babe proposes a move towards more...