Aims and Scope: Lifetime Data Analysis
The objective of Lifetime Data Analysis is to advance and promote statistical science in the various applied fields that deal with lifetime data, including: Actuarial Science – Economics – Engineering Sciences – Environmental Sciences – Management Science – Medicine – Operations Research – Public Health – Social and Behavioral Sciences.
The following listing of some current topics of interest to the journal is not intended to be exclusive but to indicate the editorial policy of attracting papers generated by a broad range of interests:
accelerated failure time models – Bayesian lifetime models – censoring and truncation – classes of lifetime distributions – competing risk models – counting processes for lifetime data – degradation processes – goodness-of-fit – maintenance policies and replacement models – measurement errors – meta-analysis of lifetime data – models for multiple events – models for noncompliance – multivariate failure models – multi-state models – nonparametric estimation of survival functions – parametric estimation and predictive inference – parametric regression models – proportional hazard models and extensions – quality-of-life models – rank tests for comparing lifetime distributions – reliability methods – residual analysis and model diagnostics – surrogate marker processes and joint modeling of these processes with lifetime data.
Officially cited as: Lifetime Data Anal