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Record Nr. |
UNINA9910438030603321 |
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Autore |
Sun Jianguo |
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Titolo |
Statistical Analysis of Panel Count Data / / by Jianguo Sun, Xingqiu Zhao |
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Pubbl/distr/stampa |
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New York, NY : , : Springer New York : , : Imprint : Springer, , 2013 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (283 p.) |
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Collana |
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Statistics for Biology and Health, , 1431-8776 ; ; 80 |
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Disciplina |
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Soggetti |
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Statistics |
Statistics for Life Sciences, Medicine, Health Sciences |
Statistical Theory and Methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Nota di contenuto |
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Introduction -- Poisson Models and Parameter Inference -- Nonparametric Estimation -- Nonparametric Comparison of Point Processes -- Regression Analysis of Panel Count Data I and II -- Analysis of Multivariate Panel Count Data -- Other Topics -- Some Sets of Data -- References -- Index. |
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Sommario/riassunto |
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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 |
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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. . |
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