Lifetime Data Analysis

Aims & Scope

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.

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Source: OpenAlex · Each year’s green bar = citations earned by that year’s papers, counted to date — so recent years look lower simply because their papers haven’t had time to be cited yet.

SJR Quartile by Discipline

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Subject Classification