A Review of Bootstrap Methods in Ranked Set Sampling

This chapter provides a brief review of the existing resampling methods for RSS and its implementation to construct a bootstrap confidence interval for the mean parameter. The authors present a brief comparison of these existing methods in terms of their flexibility and consistency. To construct the bootstrap confidence interval, three methods are adopted, namely, bootstrap percentile method, bias-corrected and accelerated method, and method based on monotone transformation along with normal approximation. Usually, for the second method, the accelerated constant is computed by employing the jackknife method. The authors discuss an analytical expression for the accelerated constant, which results in reducing the computational burden of this bias-corrected and accelerated bootstrap method. The usefulness of the proposed methods is further illustrated by analyzing real-life data on shrubs.

One Parameter A (α) Distribution: Different Methods of Estimation

This paper addresses the different methods of estimation of the unknown parameter of one parameter A(α) distribution from the frequentist point of view. We briefly describe different approaches, namely, maximum likelihood estimator, least square and weighted least square estimators, maximum product spacing estimators, Cram´er-von Mises estimator and compare those using extensive numerical simulations. Next, we obtain parametric bootstrap confidence interval of the parameter using frequentist approaches. Finally, one real data set has been analysed for illustrative purposes.

When to Use Different Tests for Power Analysis and Data Analysis for Mediation

Several options exist for conducting inference on indirect effects in mediation analysis. While methods which use bootstrapping are the preferred inferential approach for testing mediation, they are time consuming when the test must be performed many times for a power analysis. Alternatives which are more computationally efficient are not as robust, meaning accuracy of the inferences from these methods are more affected by nonnormal and heteroskedastic data (Biesanz et al., 2010). While previous research focused on how different sample sizes would be needed to achieve the same amount of power for different inferential approaches (Fritz & MacKinnon, 2007), we explore how similar power estimates are at the same sample size. We compare the power estimates from six tests using a Monte Carlo simulation study, varying the path coefficients and tests of the indirect effect. If tests produce similar power estimates, the more computationally efficient test could be used for power analysis and the more intensive test involving resampling can be used for data analysis. We found that when the assumptions of linear regression are met, three tests consistently perform similarly: the joint significance test, the Monte Carlo confidence interval, and the percentile bootstrap confidence interval. Based on these results, we recommend using the more computationally efficient joint significance test for power analysis then using the percentile bootstrap confidence interval for the data analysis.

Correcting the Bias Correction for the Bootstrap Confidence Interval in Mediation Analysis

The bias-corrected bootstrap confidence interval (BCBCI) was once the method of choice for conducting inference on the indirect effect in mediation analysis due to its high power in small samples, but now it is criticized by methodologists for its inflated type I error rates. In its place, the percentile bootstrap confidence interval (PBCI), which does not adjust for bias, is currently the recommended inferential method for indirect effects. This study proposes two alternative bias-corrected bootstrap methods for creating confidence intervals around the indirect effect. Using a Monte Carlo simulation, these methods were compared to the BCBCI, PBCI, and a bias-corrected method introduced by Chen and Fritz (2021). The results showed that the methods perform on a continuum, where the BCBCI has the best balance (i.e., having closest to an equal proportion of CIs falling above and below the true effect), highest power, and highest type I error rate; the PBCI has the worst balance, lowest power, and lowest type I error rate; and the alternative bias-corrected methods fall between these two methods on all three performance criteria. An extension of the original simulation that compared the bias-corrected methods to the PBCI after controlling for type I error rate inflation suggests that the increased power of these methods might only be due to their higher type I error rates. Thus, if control over the type I error rate is desired, the PBCI is still the recommended method for use with the indirect effect. Future research should examine the performance of these methods in the presence of missing data, confounding variables, and other real-world complications to enhance the generalizability of these results.

Ranked set sampling on estimation of P[Y

PurposeDistribution. The purpose of this study is to obtain the modified maximum likelihood estimator of stress–strength model using the ranked set sampling, to obtain the asymptotic and bootstrap confidence interval of P[Y < X], to compare the performance of author’s estimates with the estimates under simple random sampling and to apply author’s estimates on head and neck cancer.Design/methodology/approachThe maximum likelihood estimator of R = P[Y < X], where X and Y are two independent inverse Weibull random variables common shape parameter that affect the shape of the distribution, and different scale parameters that have an effect on the distribution dispersion are given under ranked set sampling. Together with the asymptotic and bootstrap confidence interval, Monte Carlo simulation shows that this estimator performs better than the estimator under simple random sampling. Also, the asymptotic and bootstrap confidence interval under ranked set sampling is better than these interval estimators under simple random sampling. The application to head and neck cancer disease data shows that the estimator of R = P[Y < X] that shows the treatment with radiotherapy is more efficient than the treatment with a combined radiotherapy and chemotherapy under ranked set sampling that is better than these estimators under simple random sampling.FindingsThe ranked set sampling is more effective than the simple random sampling for the inference of stress-strength model based on inverse Weibull distribution.Originality/valueThis study sheds light on the author’s estimates on head and neck cancer.

Trust, ethical climate and nurses’ turnover intention

Background: Nursing turnover is a very serious problem, and nursing managers need to be aware of how ethical climates are associated with turnover intention. Objectives: The article explored the effects of ethical climates on nurses’ turnover intention, mediated through trust in their organization. Methods: A cross-sectional survey of 285 nurses from three Indian hospitals was conducted to test the research model. Various established Likert-type scales were used to measure ethical climates, turnover intention and trust in organization. Hierarchical regression analysis and mediation analysis were used to test the model. Results: Hierarchical regression analysis and mediation analysis were used to test the model. The indirect effect of benevolent ethical climate on turnover intention through trust in organization was –0.20 with a 95% bootstrap confidence interval of lower level = –0.31 and upper level = –0.01. The indirect effect of principled ethical climate on turnover intention through trust in organization was –0.39 with a 95% bootstrap confidence interval of lower level = –0.58 and upper level = –0.17. Ethical considerations: The study adheres to the ethical standards recommended by the American Psychological Association for conducting research with informed consent, confidentiality and privacy. Conclusion: Both benevolent and principled ethical climates decreased turnover intention indirectly through trust in organization. Only principled ethical climates were directly associated with turnover intention. Our results suggest that nurse managers and leaders should try and establish principled and benevolent climates in order to engender trust in organization and to reduce turnover intention.

The use of the bootstrap method for the assessment of investment effectiveness and risk – the case of confidence intervals estimation for the Sharpe ratio and TailVaR

This paper is aimed at presenting application of bootstrap interval estimation methods to the assessment of financial investment’s effectiveness and risk. At first, we give an overview of various methods of bootstrap confidence interval estimation, i.e. bootstrap-t interval, percentile interval and BCa interval. Then, bootstrap confidence interval estimation methods are used to estimate confidence intervals for the Sharpe ratio and TailVaR of the Warsaw Stock Exchange sectoral indices. The results show that the bootstrap confidence intervals of different types are quite similarly positioned for each of the analysed index and measure. Taking into the account the locations of confidence intervals for both the Sharpe ratio and TailVaR, the real estate sector tends to be the most advantageous from the investor’s viewpoint.