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We present a method to assess the reliability of a material operating in a dynamic environment. The dynamic environment is represented as a sequence of shocks governed by a self-exciting point process. The time-to-failure of the material is assumed to have a piecewise exponential distribution. A Cox model is integrated to take into account the effect of the stress. Maximum likelihood estimates of the model parameters are obtained and their properties are studied through simulated data. An application on field data is displayed. Hypothesis testing procedures for environment effect are suggested.

The paper presents a Bayesian approach of the Brown–Proschan imperfect maintenance model. The initial failure rate is assumed to follow a Weibull distribution. A discussion of the choice of informative and non-informative prior distributions is provided. The implementation of the posterior distributions requires the Metropolis-within-Gibbs algorithm. A study on the quality of the estimators of the model obtained from Bayesian and frequentist inference is proposed. An application to real data is finally developed.

Forecasting is a difficult area of management. In this article, we deal with macroforecasting. The development and assessment of econometric methods for use in empirical finance and macroeconomics, with special emphasis on problems of prediction, is very important. Stock market analysis, also known as technical analysis, is the process of deriving patterns from price movement. In the literature, different methods have been applied in order to predict stock market returns. These methods can be grouped in four major categories: technical analysis methods, fundamental analysis methods, traditional time series forecasting, and machine learning methods. Technical analysts, known as chartists, attempt to predict the market by tracing patterns that come from the study of charts that describe historic data of the market. This study examines the effectiveness of technical analysis on US stocks for long range and shorter term.

We perform a survival analysis on the records of the burials at the Westbury Cemetery, Barbados, between 1877 and 1976. The goal of the paper is to observe the stratified life expectancies of persons of particular time appropriate occupations. Comparing different occupations through time, amongst each other and to the general population, enables us to get some insights into the public health situations and living conditions of the persons working in the respective occupations.

With the development of technology, a large amount of spatial data are usually observed in many applications. These massive spatial data impose a challenge to the traditional spatial data analysis primarily because of the large covariance matrix. One way to overcome the computation burden is to utilize a low rank model. The optimal low rank model is provided by the Karhunen–Loève (KL) expansion of the spatial process. However, the inference and prediction of the spatial data require an efficient algorithm for the KL expansion. In this paper, we compare four algorithms that have been proposed to numerically obtain the KL expansion. It is found that the Gaussian quadrature method outperforms the others for spatial processes.