New approximate solutions per unit of time for periodically checked systems with different lifetime distributions.
New generalization of compound Rayleigh distribution: Different estimation methods based on progressive type-II censoring schemes and applications
Fitting a suitable distribution to the data from a real experiment is a crucial topic in statistics. However, many of the existing distributions cannot account for the effect of environmental conditions on the components under test. Moreover, the components are usually heterogeneous, meaning that they do not share the same distribution. In this article, we aim to obtain a new generalization of the Compound Rayleigh distribution by using mixture models and incorporating the environmental conditions...
New models in durability tool-testing: pseudo-Weibull distribution
New results on the NBUFR and NBUE classes of life distributions
Some properties of the "new better than used in failure rate" (NBUFR) and the "new better than used in expectation" (NBUE) classes of life distributions are given. These properties include moment inequalities and moment generating functions behaviors. In addition, nonparametric estimation and testing of the survival functions of these classes are discussed.
Nichtparametrisches Schätzen in Lebensdauer-Untersuchungen bei reduzierter Information.
Nonlinear filtering for LIDAR signal processing.
Nonparametric bivariate estimation for successive survival times.
Several aspects of the analysis of two successive survival times are considered. All the analyses take into account the dependent censoring on the second time induced by the first. Three nonparametric methods are described, implemented and applied to the data coming from a multicentre clinical trial for HIV-infected patients. Visser's and Wang and Wells methods propose an estimator for the bivariate survival function while Gómez and Serrat's method presents a conditional approach for the second...
Nonparametric estimation: the survival function.
The unknown survival function S(t) of a random variable T ≥ 0 is considered. First we study the properties of S(t) and then, we estimate it from a Bayesian point of view. We compare the estimator with the posterior mean and we finish giving Bayes rules for linear functions of S(t).
Note on the truncated Rayleigh variate