Identification in censored regression analysis and hazard models of duration outcomes relies on the condition that censoring points are independent of latent outcomes, an assumption which may be questionable in many settings. This note proposes a test for this assumption based on a Kolmogorov-Smirnov-like test statistic comparing two different nonparametric estimators for the latent outcome cdf...

In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring su...

Recently, survival ensembles have found more and more applications in biological and medical research when censored time-to-event data are often confronted. In this research, we investigate the plausibility of extending a rotation forest, originally proposed for classification purpose, to survival analysis. Supported by the proper statistical analysis, we show that rotation survival forests are...

Censored data are quite common in statistics and have been studied in depth in the last years (for some early references, see Powell (1984), Muphy et al. (1999), Chay and Powell (2001)). In this paper we consider censored high-dimensional data. High-dimensional models are in some way more complex than their lowdimensional versions, therefore some different techniques are required. For the linea...

The problem of target localization with ideal binary detectors is considered in one dimensional space. The problem is investigated in both a censored and non-censored scheme. In the censored setting, the problem is equivalent to estimating the center of a uniform distribution by knowing samples of data. It does not admit an MVU estimator according to the previous results of Lehmann-Sheffe. Howe...

For right-censored data perhaps the most commonly used tests are weighted logrank tests, such as the logrank and Wilcoxon-type tests. In this paper we review several generalizations of those weighted logrank tests to interval-censored data and present an R package, interval, to implement many of them. The interval package depends on the perm package, also presented here, which performs exact an...

How to include censored data in a statistical analysis is a recurrent issue in statistics. In multivariate extremes, the dependence structure of large observations can be characterized in terms of a non parametric angular measure, while marginal excesses above asymptotically large thresholds have a parametric distribution. In this work, a flexible semi-parametric Dirichlet mixture model for ang...

In many applications the observed data can be viewed as a censored high dimensional full data random variable X. By the curse of dimensionality it is typically not possible to construct estimators which are asymptotically eecient at every probability distribution in a semiparametric censored data model of such a high dimensional censored data structure. We provide a general method for construct...

In many applications the observed data can be viewed as a censored high dimensional full data random variable X. By the curse of dimensionality it is typically not possible to construct estimators which are asymptotically eecient at every probability distribution in a semiparametric censored data model of such a high dimensional censored data structure. We provide a general method for construct...

Interval-censored data are generated by a random survival time X and a random censoring interval. We either observe the exact survival time or only know the survival time lies within the censoring interval. Turnbull (1976) proposes a self-consistent algorithm for obtaining the generalized maximum likelihood estimator (GMLE) of a survival function with interval-censored data. Yu, Li and Wong (19...