Bayesian Estimation Using Warner's Randomized Response Model through Simple and Mixture Prior Distributions

The linear discriminant function (LDF) is known to be optimal in the sense of achieving an optimal error rate when sampling from multivariate normal populations with equal covariance matrices. Use of the LDF in nonnormal situations is known to lead to some strange results. This paper will focus on an evaluation of misclassification probabilities when the power transformation could have been used to achieve at least approximate normality and equal covariance matrices in the sampled populations for the distribution of the observed random variables. Attention is restricted to the two-population case with bivariate distributions.     

A characteristic function-based approach to approximate maximum likelihood estimation

The choice of the summary statistics in approximate maximum likelihood is often a crucial issue. We develop a criterion for choosing the most effective summary statistic and then focus on the empirical characteristic function. In the iid setting, the approximating posterior distribution converges to the approximate distribution of the parameters conditional upon the empirical characteristic function. Simulation experiments suggest that the method is often preferable to numerical maximum likelihood. In a time-series framework, no optimality result can be proved, but the simulations indicate that the method is effective in small samples.     

On some new randomized item sum techniques

A recent quantified version of item count technique (ICT), called the item sum technique (IST), was developed by Trappmann et al. (2014 Trappmann, M., I. Krumpal, A. Kirchner, and B. Jann. 2014. Item sum—A new technique for asking quantitative sensitive questions. Journal of Survey Statistics and Methodology 2 (1):58–77. doi:10.1093/jssam/smt019.[Crossref] , [Google Scholar]). In this method, two subsamples are required to obtain reliable data on the sensitive issues. In this article, we propose three alternative item sum techniques by utilizing an additional randomization device. The main advantage associated with the current study is that in order to estimate population sensitive parameters, it requires only one sample to obtain reliable data on quantitative sensitive issue without jeopardizing the privacy of participants. Furthermore, it reduces the cost, effort, and time as compared to usual IST. It is also free from the requirement of finding optimum subsample sizes as in the usual IST. The mean and variance of these proposed estimators are also derived and compared with those of the usual IST. Through algebraic and nnumerical comparsions, it is observed that the proposed techniques perform better than the usual IST. Moreover, the proposed randomized IST 3 is observed to be unconditionally more effeicient than the proposed IST 1 and IST 2.     

Multi-objective multi-verse optimiser for integrated two-sided assembly sequence planning and line balancing

Research in assembly optimisation is presently inclined towards integrative measures. Several benefits of simultaneously optimised Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) have been highlighted by researchers to have better solution quality, shorter time-to-market, and minimalised error during planning. Recently, several efforts have been made to realise integrated assembly optimisation. However, none of the published research considered the two-sided assembly line problem. This paper presents an integrated ASP and ALB optimisation in a two-sided assembly environment (2S-ASPLB), which is mainly adopted in automotive assembly process. In this study, the 2S-ASPLB problem was formulated and optimised using Multi-Objective Multi-Verse Optimiser (MOMVO) by considering line efficiency, reorientation penalty, and tool change as the optimisation objectives. The computational experiments were conducted in a few stages, beginning with the identification of the best decoding approach for 2S-ASPLB. Next, the best MOMVO coefficient was studied, followed by comparing MOMVO performance with well-established multi-objective optimisation algorithms. Finally, a case study problem was presented to demonstrate applicability of the proposed model and algorithm in real-life problem. The results indicated that the priority factor (PF) decoding approach had better performance compared with others. Meanwhile, in comparison with well-established algorithms, MOMVO performed better in convergence and solution distribution. The case study results indicated the applicability of proposed 2S-ASPLB model and algorithm to improve line efficiency in assembly line. The main contribution of the research is a new 2S-ASPLB model and optimisation scheme, which can assist manufacturer in designing better assembly layout.

     

Women on boards and bank efficiency in ASEAN-5: the moderating role of the independent directors

Corporate governance codes around the world promote women’s presence in corporate board. The extant literature suggests women directors contribute to improve monitoring and advisory role of the board. This study aims to examine the role of independent women directors in improving banks’ efficiency. Using 102 commercial banks from ASEAN-5 countries for a period of 1999–2012, we run generalized method of moments models to test the relationship between the variables. Our results show that banks with independent women directors on board contribute significantly to the bank’s efficiency level. Contrary to the literature, we found that the appointment of women directors or independent directors alone does not significantly enhance banks’ efficiency. The result suggests that women directors are more effective on boards if they are also appointed as independent directors. This finding implies that a combination of corporate governance mechanism is more powerful than relying on a single element of corporate governance.     

Design of acceptance sampling plan using a modified EWMA statistic

In this paper, a modified exponentially weighted moving average (EWMA) statistic is proposed. The approximate distribution of the proposed modified EWMA statistic is derived. A variable acceptance sampling plan is designed using the proposed EWMA statistic. The plan parameters of the proposed sampling plan are determined such that the given producer's risk and consumer's risk are satisfied. The efficiency of the proposed plan based on the new EWMA statistic is compared with the existing EWMA plan in terms of the sample size required. The application of the proposed plan is given with the help of an example.     

Progressive Variance Control Charts for Monitoring Process Dispersion

In a process, the deviation from location or scale parameters affects the quality of the process and waste resources. So it is essential to monitor such processes for possible changes due to any assignable causes. Control charts are the most famous tool used to meet this intention. It is useless to monitor process location until the assurance that process dispersion is in-control. This study proposes some new two-sided memory control charts named as progressive variance (PV) control charts which are based on sample variance to monitor changes in process dispersion assuming normality of quality characteristic to be monitored. Simulation studies are made, and an example is discussed to evaluate the performance of the proposed charts. The comparison of the proposed chart is made with exponentially weighted moving average- and cumulative sum-type charts for process dispersion. The study shows that performance of the proposed charts are uniformly better than its competitors for detecting positive shifts while for detecting negative shift in the variance their performance is better for small shifts and reasonably good for moderated shifts.     

On Optimum Regression Estimator for Population Mean Using Two Auxiliary Variables in Simple Random Sampling

In this paper, a difference-in-regression estimator is proposed by using two auxiliary variables in simple random sampling. Variance of proposed estimator up to the first order of approximation is compared with other competing estimators. Additionally, by taking the known value of one of the population regression coefficients, another version of the proposed estimator is also obtained. The proposed estimator is found optimum in the class of estimators based on two auxiliary variables. A simulation study is carried out in support with theoretical results. If only the means of auxiliary variables are available, another estimator can be obtained for large trivariate normal population.     

On Using Negative Binomial Distribution as a Randomization Device in Sensitive Surveys

In this article, an interesting improvement of some recent randomized response techniques has been proposed. The proposed randomized response technique applies Negative Binomial distribution to obtain data from respondents. An unbiased estimator of proportion of a sensitive attribute has been suggested and it is shown, numerically, that the new estimator performs better than the recent estimators while doing a sensitive survey. It is also established that the proposed estimator is unconditionally better than that of the estimator based on using the geometric distribution.     

Resolving a Multi-Million Dollar Contract Dispute With a Latin Square

The City of New York negotiated a dispute over the performance of new garbage trucks purchased from a vehicle manufacturer. The dispute concerned the fulfillment of a specification in the purchase contract that the trucks load a minimum full-load of 12.5 tons of household refuse. On behalf of the City, but in cooperation with the manufacturer, the City's Department of Sanitation and consulting statisticians tested fulfillment of the contract specification, employing a Latin Square design for routing trucks. We present the classical analysis using a linear model and analysis of variance. We also show how fixed, mixed, and random effect models are useful in analyzing the results of the test. Finally, we take a Bayesian perspective to demonstrate how the information from the data overcomes the difference between the prior densities of the city and the manufacturer for the load capacities of the trucks to result in much closer posterior densities. This procedure might prove useful in similar negotiations. Supplementary material including the data and R code for computations in the article are available online.