Design optimization using ANOVA

This paper describes the design optimization of a robot sensor used for locating 3-D objects employing the Taguchi method in a computer simulation scenario. The location information from the sensor is to be utilized to control the movements of an industrial robot in a 'pick-and-place' or assembly operation. The Taguchi method, which is based on the Analysis-of-Variance (ANOVA) approach, is utilized to improve the performance of the sensor over a wider operating range. A review of the Taguchi method is presented along with step-by-step implementation details to identify and optimize the design parameters of the sensor. The method allows us to gauge the impact of various interactions present in the sensor system exclusively and permits us to single out those factors that have a dominant influence on the overall performance of the sensor. The investigation suggests that the Taguchi method is a more structured and efficient approach for achieving a robust design compared with the classical full factorial design approach.     

Women's employment decisions in Malaysia: Does religion matter?

Religion may influence female employment decisions because different religions specify different life styles. This study investigates whether religion is a significant determinant of married and single women's paid-work and full-time employment in Malaysia. Using the Second Malaysian Family Life Survey and a sequential logit approach, this paper finds that religion is less influential in urban areas than in rural areas.     

Early stopping in seamless phase I/II clinical trials

In recent years, seamless phase I/II clinical trials have drawn much attention, as they consider both toxicity and efficacy endpoints in finding an optimal dose (OD). Engaging an appropriate number of patients in a trial is a challenging task. This paper attempts a dynamic stopping rule to save resources in phase I/II trials. That is, the stopping rule aims to save patients from unnecessary toxic or subtherapeutic doses. We allow a trial to stop early when widths of the confidence intervals for the dose-response parameters become narrower or when the sample size is equal to a predefined size, whichever comes first. The simulation study of dose-response scenarios in various settings demonstrates that the proposed stopping rule can engage an appropriate number of patients. Therefore, we suggest its use in clinical trials.     

Effectiveness of Performance Appraisal: An Integrated Framework

Based on a robust analysis of the existing literature on performance appraisal (PA), this paper makes a case for an integrated framework of effectiveness of performance appraisal (EPA). To achieve this, it draws on the expanded view of measurement criteria of EPA, i.e. purposefulness, fairness and accuracy, and identifies their relationships with ratee reactions. The analysis reveals that the expanded view of purposefulness includes more theoretical anchors for the purposes of PA and relates to various aspects of human resource functions, e.g. feedback and goal orientation. The expansion in the PA fairness criterion suggests certain newly established nomological networks, which were ignored in the past, e.g. the relationship between distributive fairness and organization‐referenced outcomes. Further, refinements in PA accuracy reveal a more comprehensive categorization of rating biases. Coherence among measurement criteria has resulted in a ratee reactions‐based integrated framework, which should be useful for both researchers and practitioners.     

Minimax property of stein's estimator

Stein's estimator and some other estimators of the mean of a K-variate normal distribution are known to dominate the maximum likelihood estimator under quadratic loss for K > 3, and are therefore minimax. In this paper it is shown that the minimax property of Stein's rule is preserved with respect to a generalized loss function.     

On selecting the best population

This paper deals with the problem of selecting the “best” population from a given number of populations in a decision theoretic framework. The class of selection rules considered is based on a suitable partition of the sample space. A selection rule is given which is shown to have certain optimum properties among the selection rules in the given class for a mal rules are known.     

Data falsification and question on academic integrity

In this commentary, we argue that plagiarism is not a new problem in academic publishing and data falsification in recent times has received a great attention globally. Due to lack of literature, the objective of this study is to evaluate data falsification and academic integrity. Accordingly, the study presents the academic misconduct (Falsification/Fabrication of data and Concerns/Issues About Data) case of Professor James E. Hunton, a former top ranked accounting professor from Bentley University. The study shows how research fraud/data falsification activity in the academic world lacks honesty and morality. The study offers some recommendations for the detection of plagiarism and academic misconduct. In the age of the Internet and digital era, Crossref, iThenticate, the Committee on Publication Ethics (COPE) would help to detect plagiarism. However, the question remains on detecting data falsification in the academic world.     

The Current Rohingya Crisis in Myanmar in Historical Perspective

This paper critically examines the longstanding Rohingya crisis in Myanmar. It argues that the crisis is rooted in the British colonial era that subsequently gained momentum through their (i) gradual marginalization as an ethnic minority, (ii) exclusion from the governmental institutions, and (iii) deprivation of citizenship, effectively rendering them stateless. Starting with a demographic profile of the Rohingya it explores the major elements of the crisis from the colonial period to the present day through a historiographical methodology. Based on these historical factors shaping and aggravating the crisis, the paper concludes with a mitigating recommendatory pursuit towards a durable resolution.     

Measurement error in regression analysis

Consider the linear regression model Y = Xθ+ ε where Y denotes a vector of n observations on the dependent variable, X is a known matrix, θ is a vector of parameters to be estimated and e is a random vector of uncorrelated errors. If X'X is nearly singular, that is if the smallest characteristic root of X'X s small then a small perurbation in the elements of X, such as due to measurement errors, induces considerable variation in the least squares estimate of θ. In this paper we examine for the asymptotic case when n is large the effect of perturbation with regard to the bias and mean squared error of the estimate.