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Modeling Infectious Disease Parameters Based on Serological and Social Contact Data
Mathematical epidemiology of infectious diseases usually involves describing the flow of individuals between mutually exclusive infection states. One of the key parameters describing the transition from the susceptible to the infected class is the hazard of infection, often referred to as the force of infection. The force of infection reflects the degree of contact with potential for transmission between infected and susceptible individuals. The mathematical relation between the force of infection and effective contact patterns is generally assumed to be subjected to the mass action principle, which yields the necessary information to estimate the basic reproduction number, another key param...
Statistical Inference Based on the likelihood
The Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood. Focusing on those methods, which have both a solid theoretical background and practical relevance, the author gives formal justification of the methods used and provides numerical examples with real data.
Mean Field Simulation for Monte Carlo Integration
This book presents the first comprehensive and modern mathematical treatment of these mean field particle models, including refined convergence analysis on nonlinear Markov chain models. It also covers applications related to parameter estimation in hidden Markov chain models, stochastic optimization, nonlinear filtering and multiple target tracking, stochastic optimization, calibration and uncertainty propagations in numerical codes, rare event simulation, financial mathematics, and free energy and quasi-invariant measures arising in computational physics and population biology.
Longitudinal Data with Serial Correlation
This monograph is written for students at the graduate level in biostatistics, statistics or other disciplines that collect longitudinal data. It concentrates on the state space approach that provides a convenient way to compute likelihoods using the Kalman filter.
The Evidence Base of Clinical Diagnosis
This unique book presents a framework for the strategy and methodology of diagnostic research, in relation to its relevance for practice. Now in its second edition The Evidence Base of Clinical Diagnosis has been fully revised and extended with new chapters covering the STARD guidelines (STAndards for the Reporting of Diagnostic accuracy studies) and the multivariable analysis of diagnostic data. With contributions from leading international experts in evidence-based medicine, this book is an indispensable guide on how to conduct and interpret studies in clinical diagnosis. It will serve as a valuable resource for all investigators who want to embark on diagnostic research and for clinicians, practitioners and students who want to learn more about its principles and the relevant methodological options available.
Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical Analysis of Spatial and Spatio-Temporal Point Patter
Sequential Analysis
Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book pri...
Quasi-Least Squares Regression
Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a d
Hidden Markov Models for Time Series
Reveals How HMMs Can Be Used as General-Purpose Time Series Models Implements all methods in R Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting. Illustrates the methodology in action After presenting the simple Poisson HMM, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference. Through examples and applications, the authors describe how to extend and generalize the basic model so it can be applied in a rich variety of situations. They also provide R code for some of the examples, enabling the use of the codes in similar applications. Effectively interpret data using HMMs This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It provides a broad understanding of the models and their uses.
COMPSTAT
This Volume contains the Keynote, Invited and Full Contributed papers presented at COMPSTAT'98. A companion volume (Payne & Lane, 1998) contains papers describing the Short Communications and Posters. COMPSTAT is a one-week conference held every two years under the auspices of the International Association of Statistical Computing, a section of the International Statistical Institute. COMPSTAT'98 is organised by IACR-Rothamsted, IACR-Long Ashton, the University of Bristol Department of Mathematics and the University of Bath Department of Mathematical Sciences. It is taking place from 24-28 August 1998 at University of Bristol. Previous COMPSTATs (from 1974-1996) were in Vienna, Berlin, Leide...
