Lifetime Data: Models in Reliability and Survival Analysis
Published on 3. December 2010
Book
Paperback/Softback
XII, 410 pages
978-1-4419-4753-6 (ISBN)
Description
Statistical models and methods for lifetime and other time-to-event data are widely used in many fields, including medicine, the environmental sciences, actuarial science, engineering, economics, management, and the social sciences. For example, closely related statistical methods have been applied to the study of the incubation period of diseases such as AIDS, the remission time of cancers, life tables, the time-to-failure of engineering systems, employment duration, and the length of marriages.
This volume contains a selection of papers based on the 1994 International Research Conference on Lifetime Data Models in Reliability and Survival Analysis , held at Harvard University. The conference brought together a varied group of researchers and practitioners to advance and promote statistical science in the many fields that deal with lifetime and other time-to-event-data. The volume illustrates the depth and diversity of the field. A few of the authors have published their conference presentations in the new journal Lifetime Data Analysis (Kluwer Academic Publishers).
This volume contains a selection of papers based on the 1994 International Research Conference on Lifetime Data Models in Reliability and Survival Analysis , held at Harvard University. The conference brought together a varied group of researchers and practitioners to advance and promote statistical science in the many fields that deal with lifetime and other time-to-event-data. The volume illustrates the depth and diversity of the field. A few of the authors have published their conference presentations in the new journal Lifetime Data Analysis (Kluwer Academic Publishers).
More details
Edition
Softcover reprint of the original 1st ed. 1996
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XII, 410 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 23 mm
Weight
639 gr
ISBN-13
978-1-4419-4753-6 (9781441947536)
DOI
10.1007/978-1-4757-5654-8
Schweitzer Classification
Other editions
Additional editions
Nicholas P. Jewell | Alan C. Kimber | Mei-Ling Ting Lee
Lifetime Data: Models in Reliability and Survival Analysis
E-Book
04/2013
Springer
€149.79
Available for download
Nicholas P. Jewell | Alan C. Kimber | Mei-Ling Ting Lee
Lifetime Data: Models in Reliability and Survival Analysis
Book
11/1995
Kluwer Academic Publishers
€160.49
Shipment within 15-20 days
Content
On the Entropies and Mutual Information of Some Bivariate Continuous Distributions Used in Reliability.- Initial Conditions Problem in Event History Analysis: An Indirect Inference Procedure.- Analysis of Survival Data Under Competing Risks with Missing Cause of Death Information: Application and Implications for Study Design.- Conditional Proportional Hazards Models.- A Comparison of Conditional and Unconditional Inference Relating to Log-Gamma Distribution.- Universal Formulas for Treatment Effects from Noncompliance Data.- Foundational Issues Concerning the Analysis of Censored Data.- Burn-in at the Component and System Level.- Identification of Dependent Competing Risks Models.- Reliability Modelling for Optical Amplified Communication Systems.- A Parametric Approach to Measurement Errors in Receiver Operating Characteristic Studies.- A Modification of Goel-Okumoto Model.- Models for Degradation Processes and Event Times Based on Gaussian Processes.- A Non-Parametric Two-Sample Survival Test Based on a Single Occupancy Fermi-Dirac Model for the Discrete Range Distribution.- Probability Approximations and Inequalities for Sequential Tests.- Cure Mixture Models in Breast Cancer Survival Studies.- A General Approach to Derive Chi-Square Type of Goodness-of-Fit Tests for Lifetime Data.- Prediction in Survival Analysis: Model or Medic.- Dynamic Reliability Models.- Generalizations of Current Status Data with Applications.- Trend Analysis of Multiple Counting Processes.- Historical Controls and Model Survival Analysis.- A Random Effects Model for Multivariate Life Data.- Statistical Challenges in Comparing Chemotherapy and Bone Marrow Transplantation as a Treatment for Leukimia.- Maximum Likelihood Estimation and the Multivariate Bernoulli Distribution: An Application toReliability.- Intermediate Clinical Events, Surrogate Markers and Survival.- A Semiparametric Bootstrap for Proportional Hazards Models.- Application of Cox Regression with a Change Point in Clinical Studies.- Burn-in with Age Replacement.- Fitting Cox's Prportional Hazards Model Using Grouped Survival Data.- Statistical Methods for Dependent Competing Risks.- Orthogonal Functions of Inverse Gaussian Distributions.- Statistical Models for Quality of Life Measures.- Nonparametric Estimation of Regression Parameters from Censored Data with Two Discrete Covariates.- Locally Efficient Median Regression with Random Censoring and Surrogate Markers.- Survival Models for Heterogeneity Using the Non-Central Chi-Squared Distribution with Zero Degrees of Freedom.- Efficiently Weighted Estimating Equations with Application to Proportional Excess Hazards.- Sequential Multi-Hypothesis Testing in Software Reliability.- Assessing Gamma Frailty Models for Clustered Failure Time Data.- The Shapes of a Probability Density Function and its Hazard Function.- Therapeutic Equivalence Using a Rich Family of Prior Distributions.- Dependent Competing Risks with Time-Dependent Covariates.- Life Estimation from Pooled Discrete Renewal Counts.- Efficient and Ad Hoc Estimation in the Bivariate Censonring Model.- Efficient Estimation in a Nonproportional Hazards Model.- Modeling Frailty in Manufacturing Processes.- Estimation of Wiener Diffusion Parameters Using Process Measurements Subject to Error.- Adaptive Replacement Policies for a System of Parallel Machines.- Discrete First Passage Time Distribution for Describing Inequality among Individuals.- A Graphical Classification of Survival Distributions.- Survival Analysis in S-Plus.- A Note on Strong Uniform Consistency of Kernel Estimators ofHazard Functions Under Random Censorship.- Identifiability and Estimation of Marginal Survival Functions for Dependent Competing Risks Assuming the Copula is Known.- Author Index.