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The purpose of this paper is to develop a statistical control chart based model for earlier defect identification.

The paper used statistical process control methods and an auto‐regression model to model the identification of the initiation point of a random defect. Conventional statistical process control (SPC) methods have been widely used in process industries for process abnormality detections. However, their practicability and achievable performance are limited due to the assumptions that a continuous process is operated in a particular steady state and that all variables are normally distributed. Because the case considered here does not meet the requirement of conventional SPC methods, we proposed adaptive statistical process control charts based on an autoregressive model to distinguish defects from normal changes in operating conditions. The method proposed has been tested on a set of vibration data of rolling element ball bearings

Several control charts have been used and compared in this paper to identify the initial point of a defect. Overall, the adaptive Shewhart average level chart is a good choice since it overcomes the drawback of adaptive moving charts by working out the limits using all the bearings' data, with no such a need for a subjective threshold level. They are also not very sensitive to the small casual changes in the data.

The model developed can be served as part of a prognosis tool for maintenance decision making since once the earlier warning point has been identified, corrective maintenance actions may be taken. It has practical application areas in vibration based monitoring or any monitoring scheme where a trend in the monitored measurements may exist. The method proposed is easy to use and can be implemented in any condition based maintenance software packages.

The approach proposed in this paper is a new application of existing methods and of original contribution from a point of view of applicability. It adds value to the existing literature and is of value to practitioners.

The purpose of this article is to estimate value at risk (VaR) using quantile regression and provide a risk analysis for defaultable bond portfolios.

The method proposed is based on quantile regression pioneered by Koenker and Bassett. The quantile regression approach allows for a general treatment on the error distribution and is robust to distributions with heavy tails.

This article provides a risk analysis for defaultable bond portfolios using quantile regression method. In the proposed model we use information variables such as short‐term interest rates and term spreads as covariates to improve the estimation accuracy. The study also finds that confidence intervals constructed around the estimated VaRs can be very wide under volatile market conditions, making the estimated VaRs less reliable when their accurate measurement is most needed.

Provides a risk analysis for defaultable bond using quantile regression approach.

In the first part of this paper a new method of applying the Maximum Entropy Principle (MEP) is presented, which makes use of a “frequency related” entropy, and which is valid for all stationary processes. The method is believed valid only in the case of discrete spectra. In the second part of the paper, a method of estimating continuous spectra in the presence of noise is presented, which makes use of the Mutual Information Principle (MIP). Although the method proceeds smoothly in mathematical terms, there appear to be some difficulties in interpreting the physical meaning of some of the expressions. Examples in the use of both methods are presented, for the usual practical problem of estimating a power spectrum for a process whose autocorrelation function is partially known a priori.

The purpose of this paper is to empirically examine the market efficiency, asymmetric effect and time varying risk–return relationship for daily stock return of Amman Stock Exchange (ASE).

The Box–Jenkins selection model is used to determine the stochastic process of equity returns; the exponential generalized autogressive conditional heteroscedesticity (EGARCH) and threshhold autoregressive conditional heteroscedasticity in mean are utilized to measure the persistent of volatility, risk–return relationship and volatility magnitude to bad and good news.

The univariate statistics show negative skewness, excess kurtosis and deviation from normality for the ASE index. The results show that stock return follows an ARMA (1, 1) stochastic process with significant serial correlation, implying stock market inefficiency. The results also show significant positive relationship between equity return and risk in the ASE, which is consistent with the portfolio theory. The EGARCH model suggests the existence of the asymmetric effect.

The paper offers insights into market efficiency, time‐varying volatility and asymmetric effect in the ASE.

The purpose of this paper is to ascertain whether weather variables can explain the stock return reaction on the Dow Jones Sustainability Europe Index by employing a number of macroeconomic indicators as control variables.

The authors incorporate the generalized autogressive conditional heteroskeasticity model in methodology for the period August 26, 2009 to May 30, 2014 using daily data.

The empirical results indicate that not only do changes in humidity and wind levels seem to affect positively the European stock market but changes in returns oil and gold prices as well. However, the results show that the volatility of the US dollar/Yen exchange rate and ten-year bond value exerts significant negative impact on companies’ stock returns.

This study adds to the international literature by documenting the impact of weather variables on socially responsible companies.

Modelling and forecasting interval-valued time series (ITS) have received increasing attention in statistics and econometrics. An interval-valued observation contains more information than a point-valued observation in the same time period. The previous literature has mainly considered modelling and forecasting a univariate ITS. However, few works attempt to model a vector process of ITS. In this paper, we propose an interval-valued vector autoregressive moving average (IVARMA) model to capture the cross-dependence dynamics within an ITS vector system. A minimum-distance estimation method is developed to estimate the parameters of an IVARMA model, and consistency, asymptotic normality and asymptotic efficiency of the proposed estimator are established. A two-stage minimum-distance estimator is shown to be asymptotically most efficient among the class of minimum-distance estimators. Simulation studies show that the two-stage estimator indeed outperforms other minimum-distance estimators for various data-generating processes considered.

Jeliazkov and Poirier (2008) analyze the daily incidence of violence during the Second Intifada in a statistical way using an analytical Bayesian implementation of a second-order discrete Markov process. We tackle the same data and modeling problem from our perspective as cognitive scientists. First, we propose a psychological model of violence, based on a latent psychological construct we call “build up” that controls the retaliatory and repetitive violent behavior by both sides in the conflict. Build up is based on a social memory of recent violence and generates the probability and intensity of current violence. Our psychological model is implemented as a generative probabilistic graphical model, which allows for fully Bayesian inference using computational methods. We show that our model is both descriptively adequate, based on posterior predictive checks, and has good predictive performance. We then present a series of results that show how inferences based on the model can provide insight into the nature of the conflict. These inferences consider the base rates of violence in different periods of the Second Intifada, the nature of the social memory for recent violence, and the way repetitive versus retaliatory violent behavior affects each side in the conflict. Finally, we discuss possible extensions of our model and draw conclusions about the potential theoretical and methodological advantages of treating societal conflict as a cognitive modeling problem.

The emergence of advanced manufacturing technologies such as Flexible Manufacturing Systems (FMS) is forcing organisations to re‐examine their manufacturing strategies. CNC machines are an integral part of FMS. The literature dealing with the downtime behaviour of these machines is sparse. The purpose of this article is to analyse the behaviour and forecast downtimes of these machines using Box‐Jenkins time series analysis. It is concluded that the models fitted to the data are appropriate, and the results of this study can be used in production planning.

The purpose of this study is to investigate the relationship between renewable energy and economic growth of Bulgaria.

This study analyzes the relationship between renewable energy and economic growth of Bulgaria for the period 1990-2016, based on annual data, by using the Toda–Yamamoto analysis and Autogressive Distrubuted Lag (ARDL) bound test. This period is characterized by the democratization of the Balkans and several crisis cycles in Bulgaria. Renewable energy consumption (REC, percentage of total final energy consumption), renewable electricity output (REO, percentage of total electricity output) and economic growth (GDP constant 2010 US$) were used. The levels or differences of the variables that are stationary were investigated using the augmented Dickey–Fuller (ADF), Philips–Perron (PP) and Kwiatkowski-Philips-Schmidt-Shin (KPSS) unit root tests.

Three different results were obtained from this study. One showed that renewable energy consumption and renewable electricity output are the causes of economic growth. Another result of this study is that economic growth and renewable electricity output are the causes of renewable energy consumption. The last result is that economic growth and renewable energy consumption are not causes of renewable electricity output. There was no long-term relationship between variables.

The ARDL and Toda–Yamamoto tests were used because of lack of data sets. Thus, it is estimated that there is no long-term relationship.

This study is an original work for Bulgaria, showing the results of the relationship between renewable energy and economic growth. In line with the results of this study, renewable energy projects related to Bulgaria can be predicted.

Analyzing and forecasting reliability is increasingly important for enterprises. An accurate product reliability forecasting model cannot only learn and track a product's reliability and operational performance, but can also offer useful information that allows managers to take follow‐up actions to improve the product's quality and cost. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) model is already extensively used to analyze and forecast time series data. However, the GARCH model has not been used to analyze and forecast the failure data of repairable systems. Based on these concerns, this study proposes the GARCH model to analyze and forecast the field failure data of repairable systems.

This paper proposes the GARCH model to analyze and forecast the field failure data of repairable systems. Empirical results from electronic systems designed and manufactured by suppliers of the Chrysler Corporation are presented and discussed.

The proposed method can analyze and forecast failure data for repairable systems. Not only can this method analyze failure data volatility, it can also forecast the future failure data of repairable systems.

Advanced progress in the field of reliability prediction estimation can benefit engineers or management authorities by providing important decision support tools in which the prediction accuracy suggests financial and business outcomes as well as other outcome application results.