Preservation properties for the Laplace transform ordering of residual lives

Several preservation results for the Laplace transform ordering of residual lives (Lt−rl) are given. In particular, we show that theTt−rl-order is preserved under convolutions, mixtures and weak convergence. Some examples of interest in reliability theory are also presented.     

Preservation properties of the moment generating function ordering of residual lives

The purpose of this note is to study some preservation properties of the moment generating function ordering of residual lives (mg-rl) under the reliability operations of mixture and convolution. Some examples of interest in reliability theory are also presented.     

Mean residual life ordering

A new partial ordering among life distributions in terms of their mean residual life is introduced. This ordering is weaker than the hazard rate ordering but it is stronger than the variability ordering. Characterizations of the DMRL and NBUE distribution are given.     

Tests for bivariate mean residual life

In this paper we have developed tests for bivariate exponentiaIity against the ‘bivariate decreasing mean residual life (BDMRL)’ and ‘bivariate new better than used in expectation (BNBUE)’ classes of non-exponentia1 probability distributions. We have also obtained a large-sample approximation to make the test readily applicable.     

On inference for mean residual life

In this paper, we propose a smooth nonparametric estimator of mean residual life based on a randomly censored sample. Large sample properties of the proposed estimator are examined. Also we study the asymptotic relative efficiency for different members in the family of test statistics, proposed by Lim and Park(1998), for testing whether or not the mean residual life changes its trend, and we discuss the efficiency values of loss due to censoring. Monte Carlo simulations are conducted to illustrate the performance of our estimation and investigate the performance of test statistics by the power of tests.     

Likelihood ratio inference for mean residual life

One of the basic parameters in survival analysis is the mean residual life M ₀. For right censored observation, the usual empirical likelihood based log-likelihood ratio leads to a scaled c₁²{\chi_1^2} limit distribution and estimating the scaled parameter leads to lower coverage of the corresponding confidence interval. To solve the problem, we present a log-likelihood ratio l(M ₀) by methods of Murphy and van der Vaart (Ann Stat 1471–1509, 1997). The limit distribution of l(M ₀) is the standard c₁²{\chi_1^2} distribution. Based on the limit distribution of l(M ₀), the corresponding confidence interval of M ₀ is constructed. Since the proof of the limit distribution does not offer a computational method for the maximization of the log-likelihood ratio, an EM algorithm is proposed. Simulation studies support the theoretical result.     

Conditional mean residual life functions

In this paper a conditional mean residual life in the context of reliability theory is introduced. The properties of the conditional mean residual life are studied. Various characterizations by the conditional mean residual life are proposed.     

On the mean residual life of records

Suppose that a technical system is subject to shocks, e.g. peaks of voltages from a sequence of identically independent voltages having a lower limit value v>0$v>0$. We propose a new definition for the mean residual life of the records of the sequence and study its various properties.     

Linearity of convex mean residual life

It is shown that linearity of convex mean residual life identifies the distribution up to a scale in the class of distributions with zero mean. On this basis new characterizations of the uniform and, unexpectedly, the Student distribution with two degrees of freedom are obtained. It is observed that, strictly speaking, the conjecture of Nagaraja and Nevzerov about uniqueness property of the convex mean residual life time is incorrect. The condition under study is in obvious relations with linearity of regression of observations with respect to order statistics.     

Aspects of the mean residual life order for weighted distributions

In this paper, we first provide conditions for preservation of the mean residual life (mrl) order under weighting. Then we apply the obtained results to establish our results about preservation of the decreasing mrl class by weighted distributions. In addition, we present some results for comparing the original random variable to its weighted version in terms of the mrl order. Also, some examples are given to illustrate the results.     

Jackknife empirical likelihood test for mean residual life functions

Mean residual life (MRL) function is an important function in survival analysis which describes the expected remaining life given survival to a certain age. In this article, we propose a non parametric method based on jackknife empirical likelihood through a U-statistic to test the equality of two mean residual functions. The asymptotic distribution of the test statistic has been derived. Simulations are conducted to illustrate the performance of the proposed test under different distributional assumptions and compare with some existing method. The proposed method is then applied to two real datasets.     

Nonparametric estimator for mean residual life and vitality function

In this paper, we study the estimation of the vitality function(v(x)=E(X|X>x) and mean residual life function(e(x)=E(X-x|X>x) from a sample ofX using the empirical estimator and kernel estimator. Under suitable conditions of regularity, the asymptotic normality of the kernel estimator is obtained. Partially supported by Consejeria de Cultura y Ed. (C.A.R.M.), under Grant PIB 95/90.     

Tests for properties of residual life

The lOOα -percentile (0 < α < 1) residual life function at time x is defined to be the lOOα -percentile of the remaining life given survival up to time x. Joe and Proschan (1982b) develop tests for testing the alternatives representing decreasing 100α-percentile residual life (DPRL-α ) and the property ‘new better than used with respect to the lOOα -percentile’ (NBUP-α ). In this paper, tests are developed for DPRL[α, l) and NBUP[α, l) alternatives, where DPRL[α, l) is the class of life distributions which are DPRL-β distributions for all a ≤ β < 1 if 0 ≤ α < 1 and for all 0 < β < 1 if α = 0, and NBUP[α, l) is similarly defined. When α = 0, the DPRL[α, l) class of life distributions corresponds to the increasing failure rate class and the NBUP[α, l) class of life distributions corresponds to the new better than used class, and the test statistics are respectively asymptotically equivalent to the Hollander and Proschan (1975) test statistics for decreasing mean residual life and new better than used in expectation alternatives.     

Semiparametric regression for restricted mean residual life under right censoring

A mean residual life function (MRLF) is the remaining life expectancy of a subject who has survived to a certain time point. In the presence of covariates, regression models are needed to study the association between the MRLFs and covariates. If the survival time tends to be too long or the tail is not observed, the restricted mean residual life must be considered. In this paper, we propose the proportional restricted mean residual life model for fitting survival data under right censoring. For inference on the model parameters, martingale estimating equations are developed, and the asymptotic properties of the proposed estimators are established. In addition, a class of goodness-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and the approach is applied to a set of real life data collected from a randomized clinical trial.     

On the mean residual life of a generalized k-out-of-n system

A generalized k-out-of-n system consists of N modules in which the i th module is composed of n_i components in parallel. The system failswhen at least f components in the whole system or at least k consecutive modules have failed. In this article, we obtain the mean residual life function of such a generalized k-out-of-n system under different conditions, namely, when the number of components in each module is equal or unequal and when the components of the system are independent or exchangeable.     

Goodness-of-fit tests for additive mean residual life model under right censoring

The mean residual life (MRL) measures the remaining life expectancy and is useful in actuarial studies, biological experiments and clinical trials. To assess the covariate effect, an additive MRL regression model has been proposed in the literature. In this paper, we focus on the topic of model checking. Specifically, we develop two goodness-of-fit tests to test the additive MRL model assumption. We explore the large sample properties of the test statistics and show that both of them are based on asymptotic Gaussian processes so that resampling approaches can be applied to find the rejection regions. Simulation studies indicate that our methods work reasonably well for sample sizes ranging from 50 to 200. Two empirical data sets are analyzed to illustrate the approaches.     

Asymptotic equivalence between two empirical mean residual life processes

There are two mean residual life estimates for right censored data. One is based on the Kaplan-Meier estimate, the other, based on the Susarla-Van Ryzin estimate for survival function. In this paper, we define the empirical mean residual life process for right censored data and show that the two empirical mean residual life processes based on the Kaplan-Meier and Susarla-Van Ryzin estimates are asymptotically equivalent uniformly on an interval under some conditions. Also we discuss the case which the asymptotic equivalence might fail.     

A class of tests for exponentiality against decreasing mean residual life alternatives

A class of tests is proposed for testing Exponentiality against the Decreasing Mean Residual Life (DMRL) class of non-exponential probability distributions. These tests are consistent and asymptotically unbiased against all continuous DMRL alternatives. They are U - statistics and hence asymptotically normally distributed. The asymptotic relative efficiency (ARE) with respect to other tests for DMRL are quite high. Small sample powers are also comparable with small sample powers of the competitors.