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Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.
The book provides an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed. Finally, applications are reviewed in various fields.
This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.
Even experts on semiparametric regression should find something new here.
This book deals with estimation in situations in which there is believed to be enough information to model parametrically some, but not all of the features of a data set. Such models have arisen in a wide context in recent years, and involve new nonlinear estimation procedures. Statistical models of this type are directly applicable to fields such as economics, epidemiology, and astronomy.
Panel count data often arise in long term studies that concern occurrence rates of certain recurrent events. In such studies, each subject is observed only at finite discrete time points instead of continuously, and only the number of events that occurred between observation times is known. By multivariate panel count data, we mean that more than one type of recurrent events are of interest. Fields that produce such data include epidemiological studies, medical follow-up studies, reliability studies, and tumorigenicity experiments. This dissertation studies three research problems related to regression analysis of univariate and multivariate panel count data. Semi-parametric regression models and estimation procedures are presented for the situations where observation times or both observation and follow-up times are related with the recurrent events of interest. Their performances are evaluated through simulation studies for practical situations. In addition, the proposed methods are illustrated by application to two data sets from bladder tumor and psoriatic arthritis studies.
The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.
This book provides guidelines and fully worked examples of how to select, construct, interpret and evaluate the full range of count models.
In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models.