LEADER 05317nam 2200649Ia 450 001 9910139469703321 005 20200520144314.0 010 $a1-282-25388-3 010 $a9786613814531 010 $a0-470-61098-0 010 $a0-470-39356-4 035 $a(CKB)2550000000005837 035 $a(EBL)477625 035 $a(SSID)ssj0000340204 035 $a(PQKBManifestationID)11266897 035 $a(PQKBTitleCode)TC0000340204 035 $a(PQKBWorkID)10387451 035 $a(PQKB)10857074 035 $a(MiAaPQ)EBC477625 035 $a(PPN)190666242 035 $a(OCoLC)521032117 035 $a(EXLCZ)992550000000005837 100 $a20071107d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aMathematical methods in survival analysis, reliability and quality of life /$fedited by Catherine Huber ... [et al.] 210 $aLondon $cISTE ;$aHoboken, N.J. $cJohn Wiley$d2008 215 $a1 online resource (371 p.) 225 1 $aISTE ;$vv.13 300 $aDescription based upon print version of record. 311 $a1-84821-010-8 320 $aIncludes bibliographical references and index. 327 $aMathematical Methods in Survival Analysis, Reliability and Quality of Life; Contents; Preface; PART I; Chapter 1. Model Selection for Additive Regression in the Presence of Right-Censoring; 1.1. Introduction; 1.2. Assumptions on the model and the collection of approximation spaces; 1.2.1. Non-parametric regression model with censored data; 1.2.2. Description of the approximation spaces in the univariate case; 1.2.3. The particular multivariate setting of additive models; 1.3. The estimation method; 1.3.1. Transformation of the data; 1.3.2. The mean-square contrast 327 $a1.4. Main result for the adaptive mean-square estimator1.5. Practical implementation; 1.5.1. The algorithm; 1.5.2. Univariate examples; 1.5.3. Bivariate examples; 1.5.4. A trivariate example; 1.6. Bibliography; Chapter 2. Non-parametric Estimation of Conditional Probabilities, Means and Quantiles under Bias Sampling; 2.1. Introduction; 2.2. Non-parametric estimation of p; 2.3. Bias depending on the value of Y; 2.4. Bias due to truncation on X; 2.5. Truncation of a response variable in a non-parametric regression model; 2.6. Double censoring of a response variable in a non-parametric model 327 $a2.7. Other truncation and censoring of Y in a non-parametric model2.8. Observation by interval; 2.9. Bibliography; Chapter 3. Inference in Transformation Models for Arbitrarily Censored and Truncated Data; 3.1. Introduction; 3.2. Non-parametric estimation of the survival function S; 3.3. Semi-parametric estimation of the survival function S; 3.4. Simulations; 3.5. Bibliography; Chapter 4. Introduction of Within-area Risk Factor Distribution in Ecological Poisson Models; 4.1. Introduction; 4.2. Modeling framework; 4.2.1. Aggregated model; 4.2.2. Prior distributions; 4.3. Simulation framework 327 $a4.4. Results4.4.1. Strong association between relative risk and risk factor, correlated within-area means and variances (mean-dependent case); 4.4.2. Sensitivity to within-area distribution of the risk factor; 4.4.3. Application: leukemia and indoor radon exposure; 4.5. Discussion; 4.6. Bibliography; Chapter 5. Semi-Markov Processes and Usefulness in Medicine; 5.1. Introduction; 5.2. Methods; 5.2.1. Model description and notation; 5.2.2. Construction of health indicators; 5.3. An application to HIV control; 5.3.1. Context; 5.3.2. Estimation method 327 $a5.3.3. Results: new indicators of health state5.4. An application to breast cancer; 5.4.1. Context; 5.4.2. Age and stage-specific prevalence; 5.4.3. Estimation method; 5.4.4. Results: indicators of public health; 5.5. Discussion; 5.6. Bibliography; Chapter 6. Bivariate Cox Models; 6.1. Introduction; 6.2. A dependence model for duration data; 6.3. Some useful facts in bivariate dependence; 6.4. Coherence; 6.5. Covariates and estimation; 6.6. Application: regression of Spearman's rho on covariates; 6.7. Bibliography; Chapter 7. Non-parametric Estimation of a Class of Survival Functionals 327 $a7.1. Introduction 330 $aReliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006. 410 0$aISTE 606 $aFailure time data analysis 606 $aSurvival analysis (Biometry) 615 0$aFailure time data analysis. 615 0$aSurvival analysis (Biometry) 676 $a519.5/46 686 $aQH 252$2rvk 701 $aHuber$b Catherine$0478908 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139469703321 996 $aMathematical methods in survival analysis, reliability and quality of life$92111201 997 $aUNINA