000 04074nam a22003975i 4500
001 978-0-387-37119-1
003 DE-He213
005 20250710083957.0
007 cr nn 008mamaa
008 100301s2006 xxu| s |||| 0|eng d
020 _a9780387371191
_a99780387371191
024 7 _a10.1007/0-387-37119-2
_2doi
082 0 4 _a519.5
_223
100 1 _aSun, Jianguo.
_eauthor.
245 1 4 _aThe Statistical Analysis of Interval-censored Failure Time Data
_h[recurso electrónico] /
_cby Jianguo Sun.
264 1 _aNew York, NY :
_bSpringer New York,
_c2006.
300 _aXV, 302 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStatistics for Biology and Health,
_x1431-8776
505 0 _aInference for Parametric Models and Imputation Approaches -- Nonparametric Maximum Likelihood Estimation -- Comparison of Survival Functions -- Regression Analysis of Current Status Data -- Regression Analysis of Case II Interval-censored Data -- Analysis of Bivariate Interval-censored Data -- Analysis of Doubly Censored Data -- Analysis of Panel Count Data -- Other Topics.
520 _aSurvival analysis, the analysis of failure time data, is a rapid developing area and a number of books on the topic have been published in last twenty-five years. However, all of these books deal with right-censored failure time data, not the analysis of interval-censored failure time data. Interval-censored data include right-censored data as a special case and occur in many fields. The analysis of interval-censored data is much more difficult than that of right-censored data because the censoring mechanism that yields interval censoring is more complicated than that for right censoring. This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. A number of inference approaches are discussed in the book, including the maximum likelihood, estimating equations, sieve maximum likelihood, and conditional likelihood. One major difference between the analyses of right- and interval-censored data is that the theory of counting processes, which is responsible for substantial advances in the theory and development of modern statistical methods for right-censored data, is not applicable to interval-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. In addition, Bayesian methods and the analysis of interval-censored data with informative interval censoring are considered as well as the analysis of interval-censored recurrent event, or panel count, data. This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions. It can also be used as a text for a graduate course in statistics or biostatistics that assume a basic knowledge of probability and statistics. Jianguo (Tony) Sun is a professor at the Department of Statistics of the University of Missouri-Columbia. He has developed novel statistical methods for the analysis of interval-censored failure time data and panel count data over the last fifteen years.
650 0 _aSTATISTICS.
650 1 4 _aSTATISTICS.
650 2 4 _aSTATISTICS FOR LIFE SCIENCES, MEDICINE, HEALTH SCIENCES.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387329055
830 0 _aStatistics for Biology and Health,
_x1431-8776
856 4 0 _uhttp://dx.doi.org/10.1007/0-387-37119-2
_zVer el texto completo en las instalaciones del CICY
912 _aZDB-2-SMA
942 _2ddc
_cER
999 _c57547
_d57547