Nonparametric predictive inference for combined competing risks data

Nonparametric predictive inference for combined competing risks data

The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where uni...

Nonparametric Predictive Multiple Comparisons with Censored Data and Competing Risks

This paper provides an overview of nonparametric predictive inference for comparison of multiple groups of data including right-censored observations. Different right-censoring schemes discussed are early termination of an experiment, progressive censoring and competing risks. Theoretical results are briefly stated, detailed justifications are presented elsewhere. The methods are illustrated an...

Nonparametric predictive pairwise comparison with competing risks

In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserv...

On Nonparametric Predictive Inference for Ordinal Data

Nonparametric predictive inference (NPI) is a powerful frequentist statistical framework based only on an exchangeability assumption for future and past observations, made possible by the use of lower and upper probabilities. In this paper, NPI is presented for ordinal data, which are categorical data with an ordering of the categories. The method uses a latent variable representation of the ob...

Nonparametric Estimation for Current Status Data with Competing Risks