A class of nonparametric bivariate survival function estimators for randomly censored and truncated data

This paper proposes a class of nonparametric estimators for the bivariate survival function estimation under both random truncation and random censoring. In practice, the pair of random variables under consideration may have certain parametric relationship. The proposed class of nonparametric estimators uses such parametric information via a data transformation approach and thus provides more accurate estimates than existing methods without using such information. The large sample properties of the new class of estimators and a general guidance of how to find a good data transformation are given. The proposed method is also justified via a simulation study and an application on an economic data set

On waiting time distributions for patterns in a sequence of multistate trials

In this paper, we consider the waiting time distributions for patterns in a sequence of multistate trials. A simple and general framework, using the Markov chain imbedding method, is developed to study the waiting time distributions of both simple and compound patterns. Algorithms for the computation of these are given. The general theory is employed for the investigation of some examples in order to illustrate the theoretical results.Waiting time distribution Markov chain imbedding Simple and compound patterns

Characterizations of multivariate life distributions

AbstractCharacterizations of multivariate distributions has been a topic of great interest in applied statistics literature for the last three decades. In this paper, we develop characterizations of multivariate lifetime distributions by relationship between multivariate failure rates (reversed failure rates) and the left (right) truncated expectations of functions of random variables. We, then, discuss the application of the results to derive a multivariate Stein type identity

Characterizations of multivariate life distributions

Characterizations of multivariate distributions has been a topic of great interest in applied statistics literature for the last three decades. In this paper, we develop characterizations of multivariate lifetime distributions by relationship between multivariate failure rates (reversed failure rates) and the left (right) truncated expectations of functions of random variables. We, then, discuss the application of the results to derive a multivariate Stein type identity.62H05 62N05 Multivariate distributions Multivariate failure rates Multivariate reversed failure rates Conditional expectation Stein's identity Pearson family

Quantile-Based Reliability Analysis

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