基于计数过程的统计模型（影印版）

　　One of the most remarkable examples of fast technology transfer from new developments in mathematical probability theory to applied statistical methodology is the use of counting processes， martingales in continuous time， and stochastic integration in event history analysis. By this （or generalized survival analysis）， we understand the study of a collection of individuals， each moving among a finite （usually small） number of states. A basic example is moving from alive to dead， which forms the basis of survival analysis. Compared to other branches of statistics， this area is characterized by the dynamic temporal aspect， making modelling via the intensities useful， and by the special patterns of incompleteness of observation， of which right-censoring in survival analysis is the most important and best known example.

暂缺《基于计数过程的统计模型（影印版）》作者简介

Preface
I.Introduction
I.1GeneralIntroductiontotheBook
1.2BriefSurveyoftheDevelopmentoftheSubject
1.3PresentationofPracticalExamples
II.TheMathematicalBackground
II.1AnInformalIntroductiontotheBasicConcepts
II.2Preliminaries:Processes,Filtrations,andStoppingTimes
II.3MartingaleTheory
II.4CountingProcesses
II.5LimitTheory
II.6Product-IntegrationandMarkovProcesses
II.7LikelihoodsandPartialLikelihoodsforCountingProcesses
II.8TheFunctionalDelta-Method
II.9BibliographicRemarks
III.ModelSpecificationandCensoring
III.1ExamplesofCountingProcessmodelsforCompleteLife
HistoryData.TheMultiplicativeIntensityModel
III.2Right-Censoring
III.3Left-Truncation
III.4GeneralCensorship,Filtering,andTruncation
III.5PartialModelSpecification.Time-DependentCovariates
III.6BibliographicRemarks
IV.NonparametricEstimation
IV.1TheNelson-Aalenestimator
IV.2SmoothingtheNelson-AalenEstimator
IV.3TheKaplan-MeierEstimator
IV.4TheProduct-LimitEstimatorfortheTransitionMatrixofa
NonhomogeneousMarkovProcess
IV.5BibliographicRemarks
V.NonparametricHypothesisTesting
V.1One-SampleTests
V.2k-SampleTests
V.3OtherLinearNonparametricTests
V.4UsingtheCompleteTestStatisticProcess
V.5BibliographicRemarks
VI.ParametricModels
VI.1MaximumLikelihoodEstimation
VI.2M-Estimators
VI.3ModelChecking
VI.4BibliographicRemarks
VII.RegressionModels
VII.1Introduction.RegressionModelFormulation
VII.2SemiparametricMultiplicativeHazardModels
VII.3Goodness-of-FitMethodsfortheSemiparametric
MultiplicativeHazardModel
VII.4NonparametricAdditiveHazardModels
VII.5OtherNon-andSemi-parametricRegressionModels
VII.6ParametricRegressionModels
VII.7BibliographicRemarks
VIII.AsymptoticEfficiency
VIII.1Contiguityand'LocalAsymptoticNormality
VIII.2LocalAsymptoticNormalityinCountingProcessModels
VIII.3Infinite-dimensionalParameterSpaces:theGeneralTheory
VIll.4SemiparametricCountingProcessModels
VIII.5BibliographicRemarks
IX.FrailtyModels
IX.1Introduction
IX.2ModelConstruction
IX.3LikelihoodsandIntensities
IX.4ParametricandNonparametricMaximumLikelihood
EstimationwiththeEM-Algorithm
IX.5BibliographicRemarks
X.MultivariateTimeScales
X.1ExamplesofSeveralTimeScales
X.2SequentialAnalysisofCensoredSurvivalDatawith
StaggeredEntry
X.3NonparametricEstimationoftheMultivariateSurvival
Function
X.4BibliographicRemarks
AppendixTheMelanomaSurvivalDataandStandardMortality
TablesfortheDanishPopulation1971-75
References
AuthorIndex
SubjectIndex