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Acceptance sampling plans are a decision-making process on the basis of a randomly selected sampling from a party, where it is not possible to completely scan the products for reasons such as time and cost being limited or the formation of damaged products during the inspection. For some products, the life span (time from beginning to failure) may be an important quality characteristic. In this case, the quality control adequacy of the products can be checked with an acceptance sampling plan based on the truncated life test with a censored scheme for the lifetime of the products. In this study, group acceptance sampling plans (GASPs) based on life tests are studied under the Type-I censored scheme for the compound Weibull-exponential (CWE) distribution.

GASPs based on life tests under the Type-I censored scheme for the CWE distribution are developed by using both the producer's risk and the consumer's risk.

In this study, optimum sample size, optimum number of groups and acceptance number are obtained under the Type-I censored scheme for the CWE distribution. Real data set illustration is given to show GASPs how to be used for the industry applications.

Different from acceptance sampling plans with just considering the producer's risk, GASPs are constructed by using two-point approach included both the producer's risk and the consumer's risk for CWE distribution.

The purpose of this paper is to examine the effect of funding structure on technical efficiency of banks in Ghana, between 2011 and 2016.

Employing the random-effect and the truncated panel data of 25 banks, the results present new evidence.

The findings reveal that Ghanaian banks are less technically efficient, as the average efficiency scores generated is below the threshold of 1. Furthermore, the results show that banks in Ghana finance their operations mainly with deposit source of funding. The results reveal a significantly positive relationship between funding structure and technical efficiency. However, internally generated source of funds was negatively linked with technical efficiency. This is not surprising because banks that rely on external funds attract higher costs than internally generated funds, and this puts pressure on managers to perform. The results are relevant to emerging economies when the authors use additional macroeconomic factors.

Thus, a proportionally larger deposit base funding would typically lead to an overall increase in technical efficiency of banks in Ghana. Shareholders should put pressure on managers to plough back earnings in order to increase the use of internally generated funds, thus, increasing technical efficiency. Banks that are inefficient should make some adjustments to their weights of inputs and/or outputs combinations by following their benchmark banks (efficient banks) to improve their efficiency.

The results of this study have important implications for regulators, investors and policy makers, particularly an emerging economy. The implication of the study to investors is that investors should be able to identify an appropriate source of funds that can be used efficiently to maximize their wealth in emerging markets. It is important for regulators and managers of banks to improve technical efficiency by considering the role that macroeconomic and monetary environment play when identifying and using various sources of funds as a strategy to improve bank efficiency.

Consequently, future research should investigate the impact of funding structure on technical efficiency for other regions and considering their interactions with institutional quality, macroeconomic factors and financial stability.

To the best of the authors’ knowledge, the study is the first to fulfill an urgent need to explore a robust approach of measuring technical efficiency and funding structure within the context of banks over six-year period, prompting insightful avenues to the survival, growth and performance of financiers in emerging economy.

The purpose of this paper is to investigate the stochastic comparisons of the parallel system with independent heterogeneous Gumbel components and series and parallel systems with independent heterogeneous truncated Gumbel components in terms of various stochastic orderings.

The obtained results in this paper are obtained by using the vector majorization methods and results. First, the components of series and parallel systems are heterogeneous and having Gumbel or truncated Gumbel distributions. Second, multiple-outlier truncated Gumbel models are discussed for these systems. Then, the relationship between the systems having Gumbel components and Weibull components are considered. Finally, Monte Carlo simulations are performed to illustrate some obtained results.

The reversed hazard rate and likelihood ratio orderings are obtained for the parallel system of Gumbel components. Using these results, similar new results are derived for the series system of Weibull components. Stochastic comparisons for the series and parallel systems having truncated Gumbel components are established in terms of hazard rate, likelihood ratio and reversed hazard rate orderings. Some new results are also derived for the series and parallel systems of upper-truncated Weibull components.

To the best of our knowledge thus far, stochastic comparisons of series and parallel systems with Gumbel or truncated Gumble components have not been considered in the literature. Moreover, new results for Weibull and upper-truncated Weibull components are presented based on Gumbel case results.

The purpose of this paper is to propose a semiparametric estimator for the tail index of Pareto-type random truncated data that improves the existing ones in terms of mean square error. Moreover, we establish its consistency and asymptotic normality.

To construct a root mean squared error (RMSE)-reduced estimator of the tail index, the authors used the semiparametric estimator of the underlying distribution function given by Wang (1989). This allows us to define the corresponding tail process and provide a weak approximation to this one. By means of a functional representation of the given estimator of the tail index and by using this weak approximation, the authors establish the asymptotic normality of the aforementioned RMSE-reduced estimator.

In basis on a semiparametric estimator of the underlying distribution function, the authors proposed a new estimation method to the tail index of Pareto-type distributions for randomly right-truncated data. Compared with the existing ones, this estimator behaves well both in terms of bias and RMSE. A useful weak approximation of the corresponding tail empirical process allowed us to establish both the consistency and asymptotic normality of the proposed estimator.

A new tail semiparametric (empirical) process for truncated data is introduced, a new estimator for the tail index of Pareto-type truncated data is introduced and asymptotic normality of the proposed estimator is established.

The objective of this paper is to investigate the efficiency of hybrid solution methods when incorporated into large‐scale optimization problems solved by evolution strategies (ESs) and to demonstrate their influence on the overall performance of these optimization algorithms. ESs imitate biological evolution and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism. In this paper modified multi‐membered evolution strategies with discrete variables are adopted. Two solution methods are implemented based on the preconditioned conjugate gradient (PCG) algorithm. The first method is a PCG algorithm with a preconditioner resulted from a complete Cholesky factorization, and the second is a PCG algorithm in which a truncated Neumann series expansion is used as a preconditioner. The numerical tests presented demonstrate the computational advantages of the proposed methods, which become more pronounced in large‐scale optimization problems and in a parallel computing environment.

Data envelopment analysis (DEA) is based on the production possibility set that involves the process of converting resources or inputs to outputs. Accordingly, most DEA models include endogenous variables and need an additional step to find the influence of exogenous variables on the process. The purpose of this paper is to examine the relationship between the efficiency scores of DEA and the exogenous variables using truncated regression analysis with double bootstrapping along with two additional methods.

First, the authors employ DEA for benchmarking the comparative efficiency of the health care institutes. Next, the authors run and compare truncated, ordinary least square (OLS) and Tobit regression analysis using the double bootstrapping algorithm for finding the influence of exogenous variables on the efficiency of the health care institutes.

The authors confirmed the amount of bias for the Tobit and OLS regression models, which was caused by serially correlated errors. Accordingly, the authors chose results from the truncated regression model with double bootstrapping for examining the influence of exogenous or environment variables on the efficiency scores.

The study includes cross-sectional data on health care institutes in the state of Washington, USA. Collecting data in various states or regions over time is left for future studies.

In this study, three exogenous variables such as Medicaid revenues, locations of health care institutes and ownership types are significant for explaining the relationship between the efficiency scores and a group of the exogenous variables. Managers and policy makers need to pay attention to these variables along with endogenous variables for promoting the sustainability of the health care institutes.

The study demonstrates the usefulness of the truncated regression analysis with double bootstrapping for confirming the relationship between the efficiency scores of DEA and a group of exogenous variables, which is rare in the DEA literature.

This article will select 25 years of subdivided data to perform Kaplan–Meier survival analysis on the export trade relations of Chinese wooden flooring, use discrete-time cloglog models to analyze influencing factors, use logit and probit models to test the robustness, and try to systematically reveal the duration of China's wood flooring export trade and its influencing factors.

This study used Kaplan–Meier survival function estimation method. In the survival analysis, survival function and hazard rate function are often used to characterize the distribution of survival time.

The continuous average export time of China's wooden flooring is relatively long, about 14 years. China's wooden flooring has a negative time dependency. After the export trade exceeds the threshold value of 15 years, the failure rate of trade greatly decreases, which has a “threshold effect.” Gravity model variables have a significant impact on the duration of China's wooden floor export.

Studying the duration of forest products trade is of great significance for clearing deep-level trade relations and promoting sustainable development of forest products trade.

The processes of liberalisation, globalisation and integration have brought new dynamics into banking markets. In an increasingly competitive environment, banks have been forced to refocus their strategies and examine their performance, because their survival in the 21st century will depend on efficiency (Denizer & Tarimcilar, 2001). In recent years, therefore, bank efficiency has received wide attention, and researchers have developed an extensive array of sophisticated methods and tools to estimate efficiency.

Although handloom is a significant source of livelihood for millions of people in India, it performs poorly compared to other sectors of the economy, which may be the root of technical inefficiency. Until now, to measure technical efficiency, no studies have been carried out; therefore, the purpose of this study is to estimate the technical efficiency in the handloom micro-enterprises in India.

This study includes 427 handloom micro-entrepreneurs from the Indian state of Assam. Using bootstrap truncated regression, the data envelopment analysis (DEA) was used to calculate the technical efficiency and identify the factors responsible for inefficiency.

The findings of this study reveal that handloom enterprises are 75% pure technically efficient, suggesting room for input reduction. The bootstrap truncated regression results show that education, prior experience, modern technology, ICT, bank loan, training, gender and location significantly influence the technical efficiency of handloom enterprises.

Despite recent advances in the DEA method, this study used a traditional form of DEA. This study used only one output and a limited set of inputs. Better results could have been obtained by expanding the number of inputs and output. Finally, the data for this study has been obtained from a very narrow geographic area. The production practices of the handloom enterprises in other parts of the region and other states might vary considerably.

Technical efficiency measurement has management implications for businesses because it allows entrepreneurs to determine how much less input is required to produce the same output. A meticulous analysis can pinpoint the causes of inefficiency.

This paper aims to make two significant contributions to the extant literature. First, to the best of the authors’ knowledge, no published document has analyzed the technical efficiency of handloom micro-enterprises anywhere in the world. The authors fill this void by systematically analyzing the technical efficiency of the handloom industry in Assam.

This study investigates the association of various corporate governance attributes and financial characteristics with the survival likelihood of distressed firms. To address the manner in which firms evolve over time, we employ survival analysis techniques by incorporating Cox Proportional Hazards regressions. We longitudinally track an ex ante sample of 176 financially distressed firms. The results suggest that firms that replaced their CEO with an outsider, were more than twice as likely to experience bankruptcy. Furthermore, larger levels of blockholder and insider ownership over the sample period are positively associated with the likelihood of firm survival.