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In this paper, the authors propose two construction methods to estimate confidence intervals of the time-based optimal software rejuvenation policy and its associated maximum system availability via a parametric bootstrap method. Through simulation experiments the authors investigate their asymptotic behaviors and statistical properties.

The present paper is the first challenge to derive the confidence intervals of the optimal software rejuvenation schedule, which maximizes the system availability in the sense of long run. In other words, the authors concern the statistical software fault management by employing an idea of process control in quality engineering and a parametric bootstrap.

As a remarkably different point from the existing work, the authors carefully take account of a special case where the two-sided confidence interval of the optimal software rejuvenation time does not exist due to that fact that the estimator distribution of the optimal software rejuvenation time is defective. Here the authors propose two useful construction methods of the two-sided confidence interval: conditional confidence interval and heuristic confidence interval.

Although the authors applied a simulation-based bootstrap confidence method in this paper, another re-sampling-based approach can be also applied to the same problem. In addition, the authors just focused on a parametric bootstrap, but a non-parametric bootstrap method can be also applied to the confidence interval estimation of the optimal software rejuvenation time interval, when the complete knowledge on the distribution form is not available.

The statistical software fault management techniques proposed in this paper are useful to control the system availability of operational software systems, by means of the control chart.

Through the online monitoring in operational software systems, it would be possible to estimate the optimal software rejuvenation time and its associated system availability, without applying any approximation. By implementing this function on application programming interface (API), it is possible to realize the low-cost fault-tolerance for software systems with aging.

In the past literature, almost all authors employed parametric and non-parametric inference techniques to estimate the optimal software rejuvenation time but just focused on the point estimation. This may often lead to the miss-judgment based on over-estimation or under-estimation under uncertainty. The authors overcome the problem by introducing the two-sided confidence interval approach.

The purpose of the study is to examine how shared perceptions of collective behaviors, such as transformational leadership climate (TLC), collective trust and collective engagement, affect unit performance.

The sample consisted of 450 employees in 74 agencies of a financial organization, using measurements at the collective team level. Multiple structural equation modeling analyses were used to test the hypotheses.

The relationship between TLC and unit performance was sequentially mediated by collective trust and collective engagement.

This study highlights how employees' shared perceptions of different team characteristics can affect the overall functioning and performance of an organization.

The purpose of this paper is to propose the parametric bootstrap method for valuation of over-the-counter derivative (OTCD) initial margin (IM) in the financial market with low outstanding notional amounts. That is, an aggregate outstanding gross notional amount of OTC derivative instruments not exceeding R20bn.

The OTCD market is assumed to have a Gaussian probability distribution with the mean and standard deviation parameters. The bootstrap value at risk model is applied as a risk measure that generates bootstrap initial margins (BIM).

The proposed parametric bootstrap method is in favour of the BIM amounts for the simulated and real data sets. These BIM amounts are reasonably exceeding the IM amounts whenever the significance level increases.

This paper only assumed that the OTCD returns only come from a normal probability distribution.

The OTCD IM requirement in respect to transactions done by counterparties may affect the entire financial market participants under uncleared OTCD, while reducing systemic risk. Thus, reducing spillover effects by ensuring that collateral (IM) is available to offset losses caused by the default of a OTCDs counterparty.

This paper contributes to the literature by presenting a valuation of IM for the financial market with low outstanding notional amounts by using the parametric bootstrap method.

The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry.

This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models.

The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills.

The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)].

The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.

This paper aims to focus on the intention to use of the Backstage game by teachers delivering entrepreneurship course at Tunisian public universities. A research model was developed based on a literature review of the variables that would affect the intention to use this game.

Data were collected by survey questionnaires, and a partial least square-structural equation model approach was adopted to analyze these data. Overall, the whole model was able to account for 61.3% of variance in the construct intention to use.

Results showed the relevance of both perceived behavioral control and teaching experience as the main determinants of the intention to use Backstage game. Positive and significant linkages were also found among perceived behavioral control, social influence and perceived ease of use. Moreover, it was found that perceived ease of use and social influence are significant determinants of perceived usefulness. Perceived usefulness and perceived enjoyment had significant effects on attitude towards intention to use this game.

To the best of the authors’ knowledge, this research represents the first empirical study to investigate among Tunisian university teachers their intention to use a serious game technology dedicated to making the entrepreneurship course more attractive.

The purpose of this paper is to illustrate the importance of modeling parameter risk in premium risk, especially when data are scarce and a multi‐year projection horizon is considered. Internal risk models often integrate both process and parameter risks in modeling reserve risk, whereas parameter risk is typically omitted in premium risk, the modeling of which considers only process risk.

The authors present a variety of methods for modeling parameter risk (asymptotic normality, bootstrap, Bayesian) with different statistical properties. They then integrate these different modeling approaches in an internal risk model and compare their results with those from modeling approaches that measure only process risk in premium risk.

The authors show that parameter risk is substantial, especially when a multi‐year projection horizon is considered and when there is only limited historical data available for parameterization (as is often the case in practice). The authors' results also demonstrate that parameter risk substantially influences risk‐based capital and strategic management decisions, such as reinsurance.

The authors' findings emphasize that it is necessary to integrate parameter risk in risk modeling. Their findings are thus not only of interest to academics, but of high relevance to practitioners and regulators working toward appropriate risk modeling in an enterprise risk management and solvency context.

To the authors' knowledge, there are no model approaches or studies on parameter uncertainty for projection periods of not just one, but several, accident years; however, consideration of multiple years is crucial when thinking strategically about enterprise risk management.

This is a survey paper of the recent literature on the application of semiparametric–econometric advances to testing for functional form of the environmental Kuznets curve (EKC). The EKC postulates that there is an inverted U-shaped relationship between economic growth (typically measured by income) and pollution; that is, as economic growth expands, pollution increases up to a maximum and then starts declining after a threshold level of income. This hypothesized relationship is simple to visualize but has eluded many empirical investigations. A typical application of the EKC uses panel data models, which allows for heterogeneity, serial correlation, heteroskedasticity, data pooling, and smooth coefficients. This vast literature is reviewed in the context of semiparametric model specification tests. Additionally, recent developments in semiparametric econometrics, such as Bayesian methods, generalized time-varying coefficient models, and nonstationary panels are discussed as fruitful areas of future research. The cited literature is fairly complete and should prove useful to applied researchers at large.

Heavy-tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with internet transaction datasets, and machine learners often analyze such data without considering the biases and risks associated with the misuse of standard tools. This chapter outlines a procedure for inference about the mean of a (possibly conditional) heavy-tailed distribution that combines nonparametric analysis for the bulk of the support with Bayesian parametric modeling – motivated from extreme value theory – for the heavy tail. The procedure is fast and massively scalable. The work should find application in settings wherever correct inference is important and reward tails are heavy; we illustrate the framework in causal inference for A/B experiments involving hundreds of millions of users of eBay.com.

The purpose of this paper is to estimate technical efficiency obtained from both data envelopment analysis (DEA) and stochastic frontier approaches using household survey data for rice farming households in Vietnam.

A bootstrap method is used to provide statistical precision of the DEA estimator. Technical efficiency is modeled as a function of household and production factors.

The results from the deterministic, semi‐parametric and parametric approaches indicate that among other things, technical efficiency is significantly influenced by primary education and regional factors. In addition, scale efficiency analysis shows that many farms in Vietnam are operating with less than optimal scale of operation.

The study is among the first that employ a bootstrap method and compare estimates from both Data Envelopment Analysis (DEA) and stochastic frontier approaches.

The capability indices are widely used by quality professionals as an estimate of process capability. Many process indices have been proposed and developed with C_p, C_pk and C_pm among the most widely used. More recently, techniques have been developed to construct lower 95 percent confidence limits for each index. These techniques are based on the assumption that the underlying process is normally distributed. The non‐parametric but computer intensive method called Bootstrap is utilized and the Bootstrap confidence limits are calculated for these indices. A simulation using three distributions (normal, log‐normal and chi‐squared) was conducted and a comparison was made of the performances of the Bootstrap and the parametric estimates.