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In this chapter we demonstrate the construction of inverse test confidence intervals for the turning-points in estimated nonlinear relationships by the use of the marginal or first derivative function. First, we outline the inverse test confidence interval approach. Then we examine the relationship between the traditional confidence intervals based on the Wald test for the turning-points for a cubic, a quartic, and fractional polynomials estimated via regression analysis and the inverse test intervals. We show that the confidence interval plots of the marginal function can be used to estimate confidence intervals for the turning-points that are equivalent to the inverse test. We also provide a method for the interpretation of the confidence intervals for the second derivative function to draw inferences for the characteristics of the turning-point.

This method is applied to the examination of the turning-points found when estimating a quartic and a fractional polynomial from data used for the estimation of an Environmental Kuznets Curve. The Stata do files used to generate these examples are listed in Appendix A along with the data.

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.

This paper aims to present sample-based estimation methodologies to compute the confidence interval for the mean size of the content of material communicated on the digital social media platform in presence of volume, velocity and variety. Confidence interval acts as a tool of machine learning and managerial decision-making for coping up big data.

Random sample-based sampling design methodology is adapted and mean square error is computed on the data set. Confidence intervals are calculated using the simulation over multiple data sets. The smallest length confidence interval is the selection approach for the most efficient in the scenario of big data.

Resultants of computations herein help to forecast the future need of web-space at data-centers for anticipation, efficient management, developing a machine learning algorithm for predicting better quality of service to users. Finding supports to develop control limits as an alert system for better use of resources (memory space) at data centers. Suggested methodologies are efficient enough for future prediction in big data setup.

In IT sector, the startup with the establishment of data centers is the current trend of business. Findings herein may help to develop a forecasting system and alert system for optimal decision-making in the enhancement and share of the business.

The contribution is an original piece of thought, idea and analysis, deriving motivation from references appended.

While crop insurance ratemaking has been studied for many decades, it is still faced with many challenges. Crop insurance premium rates (PRs) are traditionally determined only by point estimation, and this approach may lead to uncertainty because it is sensitive to the underwriter’s assumptions regarding the trend, yield distribution, and other issues such as data scarcity and credibility. Thus, the purpose of this paper is to obtain the interval estimate for the PR, which can provide additional information about the accuracy of the point estimate.

A bootstrap method based on the loss cost ratio ratemaking approach is proposed. Using Monte Carlo experiments, the performance of this method is tested against several popular methods. To measure the efficiency of the confidence interval (CI) estimators, the actual coverage probabilities and the average widths of these intervals are calculated.

The proposed method is shown to be as efficient as the non-parametric kernel method, and has the features of flexibility and robustness, and can provide insight for underwriters regarding uncertainty based on the width of the CI.

Comprehensive comparisons are conducted to show the advantage and the efficiency of the proposed method. In addition, a significant empirical example is given to show how to use the CIs to support ratemaking.

Each ethnic group has its own peculiar cultural practices that may widen inequalities in child health and survival among ethnic groups. This study estimated ethnic disparities in mortality of under‐five‐year‐olds, controlling for individual and community level characteristics. Using multilevel multivariable regression analysis on a nationally representative sample drawn from 7,864 households in the 2003 Nigeria Demographic and Health Survey, we estimated the risks of deaths under‐five‐year‐olds for 6,029 children nested within 2,735 mothers aged 15‐49 years old, who were in turn nested within 365 communities. Results were expressed as odds ratios with 95% confidence intervals. The observed risk of under‐five death was highest among children of Hausa/Fulani/Kanuri mothers and lowest among children of Yoruba mothers. The mother's affiliation to the Yoruba ethnic group, compared to Hausa/Fulani/Kanuri, was still significantly associated with decreased under‐five mortality (OR = 0.66, 95% CI = 0.45 ‐ 0.96) after adjustment for individual and community level factors. Under‐five mortality was significantly related to socio‐economic and demographic factors (birth order/birth interval, mother's age, and mother's education), which explained much but not all of the ethnic disparities. Findings underscore the need for measures aimed at improving female education and the socio‐economic standard of women, changing short birth spacing norms and reducing inequitable distribution of maternal and child health services.

Quality function deployment (QFD) is a planning methodology to improve products, services and their associated processes by ensuring that the voice of the customer has been effectively deployed through specified and prioritised technical attributes (TAs). The purpose of this paper is two ways: to enhance the prioritisation of TAs: computer simulation significance test; and computer simulation confidence interval. Both are based on permutation sampling, bootstrap sampling and parametric bootstrap sampling of given empirical data.

The authors present a theoretical case for the use permutation sampling, bootstrap sampling and parametric bootstrap sampling. Using a published case study the authors demonstrate how these can be applied on given empirical data to generate a theoretical population. From this the authors describe a procedure to decide upon which TAs have significantly different priority, and also estimate confidence intervals from the theoretical simulated populations.

First, the authors demonstrate not only parametric bootstrap is useful to simulate theoretical populations. The authors can also employ permutation sampling and bootstrap sampling to generate theoretical populations. Then the authors obtain the results from these three approaches. qThe authors describe why there is a difference in results of permutation sampling, bootstrap and parametric bootstrap sampling. Practitioners can employ any approach, it depends how much variation in FWs is required by quality assurance division.

Using these methods provides QFD practitioners with a robust and reliable method for determining which TAs should be selected for attention in product and service design. The explicit selection of TAs will help to achieve maximum customer satisfaction, and save time and money, which are the ultimate objectives of QFD.

This paper provides a quantitative review of the literature on the repercussions of idiosyncratic information on firms’ cost of equity (CoE) capital. In total, I review the results of 113 unique studies examining the CoE effects of information Quantity, Precision and Asymmetry. My results suggest that the association between firm-specific information and CoE is subject to moderate effects. First, the link between Quantity and CoE is moderated by disclosure types and country-level factors in that firms in comparatively weakly regulated countries tend to enjoy up to four times greater CoE benefits from more expansive disclosure—depending on the type of disclosure—than firms in strongly regulated markets. Second, a negative relationship between Precision and CoE is only significant in studies using non-accrual quality proxies for Precision and risk factor-based (RFB)/valuation model-based (VMB) proxies for CoE. Third, almost all VMB studies confirm the positive association between Asymmetry and CoE, but there is notable variation in the conclusions reached when ex post CoE measurers are used.

The purpose of this paper is to analyze the technical efficiency (TE) of the rainfed rice farming system in Nigeria.

Special attention is directed to the construction of confidence interval (CI) for individual efficiency estimates using stochastic frontier production model.

The results show that the coefficient of the regression monotonically and significantly increased output level, while returns‐to‐scale (RTS) of 0.943 suggest decreasing RTS in the study. An average point estimate of TE of 0.669 is obtained from the analysis. CI show that the TE intervals are quite wide, as the assumption that farms originally identified to be on the frontier, or very close to the frontier, by the point estimate may be below the frontier. Age and years of schooling of the farmers are efficiency‐increasing policy variables found in the study.

Contrary to the previous efficiency studies, the present study demonstrates that economic inferences drawn on the basis of CI seem plausible to provide much more fruitful and insightful information about the TE of farms in the country, rather than the point estimation alone.

To improve the accuracy of parameter prediction for small-sample data, considering the existence of error in samples, the error circle is introduced to analyze original samples.

The influence of surface roughness on fatigue life is discussed. The error circle can treat the original samples and extend the single sample, which reduces the influence of the sample error.

The S-N curve obtained by the error circle method is more reliable; the S-N curve of the Bootstrap method is more reliable than that of the Maximum Likelihood Estimation (MLE) method.

The parameter distribution and characteristics are statistically obtained based on the surface roughness, surface roughness factor and intercept constant. The original sample is studied by an error circle and discussed using the Bootstrap and MLE methods to obtain corresponding S-N curves. It provides a more trustworthy basis for predicting the useful life of products.

The coronavirus disease (COVID-19) pandemic has affected Latinx immigrant neighborhoods in New York City (NYC) disproportionately. Poverty, hypertension and diabetes have been associated with adverse COIVD-19 outcomes. This study aims to assess the prevalence of these COVID-19 vulnerabilities among Latinx immigrants in NYC.

Data were obtained from the 2018 New York City Community Health Survey. The relation between Latinx immigrant status and study outcomes was assessed in univariate and multivariable regression models.

Latinx immigrants were 1.3 times (95% confidence interval: 1.2–1.5) more likely to have hypertension and 2.5 times (95% confidence interval: 1.9–3.2) to have diabetes, compared to the US-born Whites after adjusting for age. They were 46.5 times (95% confidence interval: 24.3–88.8) more likely to live in a neighborhood with high poverty, eight times more likely (95% confidence interval: 5.5–11.6) not to have had enough food in the previous six months and 1.4 times more likely (95% confidence interval: 1.2–1.6) to lack health insurance coverage, compared to the US-born Whites.

These findings highlight the greater vulnerabilities of Latinx immigrants in NYC to COVID-19 in the year prior to the pandemic. Poverty, food insecurity, hostile immigration policies and lack of access to health care exacerbate health disparities among Latinx immigrants in NYC.

This study provides a public health perspective for understanding the association of health disparities and socioeconomic conditions of Latinx immigrants in NYC.