This paper discusses random effects in censored ordinal regression and presents a Gibbs sampling approach to fit the regression model. A latent structure and its corresponding Bayesian formulation are introduced to effectively deal with heterogeneous and censored ordinal observations. This work is motivated by the need to analyze interval-censored ordinal data from multiple studies in toxicolog...

Medical researchers frequently make statements that one model predicts survival better than another, and are frequently challenged to provide rigorous statistical justification for these statements. In general, it is important to quantify how well the model is able to distinguish between high risk and low risk subjects (discrimination), and how well the model predicts the probability of having ...

Traditional inferential procedures often fail with censored and truncated data, especially when sample sizes are small. In this paper we evaluate the performances of the double and single bootstrap interval estimates by comparing the double percentile (DB-p), double percentile-t (DB-t), single percentile (B-p), and percentile-t (B-t) bootstrap interval estimation methods via a coverage probabil...

A comprehensive, unified approach to modeling arbitrarily censored spatial survival data is presented for the three most commonly-used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. Unlike many other approaches, all manner of censored survival times are simultaneously accommodated including uncensored, interval censored, current-status, left and ri...

One commonly encountered problem in statistical analysis of extreme events is that very few data are available for inference. This issue is all the more important in multivariate problems that the dependence structure among extremes has to be inferred. In some cases, e.g. in environmental applications, it is sometimes possible to increase the sample size by taking into account historical or inc...

Breast cancer is one of the top cancer-death causes and specifically accounts for 10.4% of all cancer incidences among women. The prediction of breast cancer recurrence has been a challenging research problem for many researchers. Data mining techniques have recently received considerable attention, especially when used for the construction of prognosis models from survival data. However, exist...

We propose an estimation procedure for (semiparametric) panel data censored regression models in which the error terms may be subject to general forms of non-stationarity, thus permitting heteroscedasticity over time. The proposed estimator exploits a weak structural form imposed on the individual speci ̄c e®ect. This is in contrast to the estimators introduced in Honor¶e(1992) where the individ...

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...

This study proposes an approach based on neural networks to perform the analysis of the censored data. Two neural networks are constructed: the first neural network is designed to estimate the censored data by means of constructing the model derived from the uncensored data and, the second neural network is designed to obtain the optimum parameter settings of control factors by using the uncens...