Springer

Lifetime Data Analysis

(ISSN 1572-9249)

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