Statistical models and methods for lifetime data
A unified treatment of models and statistical methods used in the analysis of lifetime or response time data. Draws together the most important, up-to-date methods used in engineering, medical and the biological sciences, including parametric, distribution-free, nonparametric, and graphical methods. Numerical illustrations and examples involving real data demonstrate the application of each method to problems in areas such as reliability, product performance evaluation, clinical trials, and experimentation in the biomedical sciences.
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Basic concepts and models
Life tables graphs and related procedures
Inference procedures for exponential distributions
10 other sections not shown
95 confidence interval Alternately approach assumed asymptotic calculated censored data censored sample Chapter computation covariance matrix defined derivatives discussed in Section distribution with p.d.f. equal equivariant estimators exact examine Example exponential distribution exponential model extreme value distribution failure gamma distribution given gives hazard function hypothesis inference information matrix interval estimation involving iteration joint p.d.f. large-sample least squares lifetime data lifetime distribution likelihood function likelihood ratio methods likelihood ratio statistic linear estimators log lifetime log likelihood log-gamma log-normal distribution Mann maximized maximum likelihood estimation normal approximation normal distribution number of deaths observed lifetimes order statistics parametric models partial likelihood percentage points pivotal Prentice probability plot problem procedures proportional hazards model quantiles random sample random variables rank test regression model regressor variables residuals scale parameter situations Suppose survival survivor function testing H0 Theorem tion Type II censored uncensored variance voltage Weibull distribution Weibull model