Inferences of variance function - a parametric robust way

Tsou (2003a) proposed a parametric procedure for making robust inference for mean regression parameters in the context of generalized linear models. This robust procedure is extended to model variance heterogeneity. The normal working model is adjusted to become asymptotically robust for inference about regression parameters of the variance function for practically all continuous response variables. The connection between the novel robust variance regression model and the estimating equations approach is also provided.     

Moral emotions in early childhood: Validation of the Chinese moral emotion questionnaire

Moral emotions such as pride, guilt and shame play an important role in the social-emotional development of preschool children. However, there are not many instruments available for measuring moral emotions in the preschool age. Moreover, relatively few research had examined cross-cultural validity of measures for moral emotions. The present study tested the Chinese version of the Moral emotion questionnaire (MEQ) upon a group of (N = 182) Chinese preschool children aged from 2 to 6 years. The Chinese MEQ is a parent-report translated from Dutch, assessing behavioural responses of pride, guilt and shame in preschool children. Confirmatory factor analysis showed satisfactory goodness-of-fit indexes for a three-factor structure (Pride, Guilt, Shame) with 15 loading items. For concurrent relations, the results suggested an adaptive role of pride and guilt and a maladaptive role of shame in the social-emotional development of preschool children. We could conclude that the 15-item Chinese MEQ is a valid and reliable instrument for measuring pride, guilt, and shame in 2–6-year-old children in the Chinese context.     

An optimal design for process quality improvement: modelling and application

Existing research works on process quality improvement focus largely on the linkages between quality improvement cost and production economics such as set-up cost and defect rate reduction. This paper deals with the optimal design problem for process improvement by balancing the sunk investment cost and revenue increments due to the process improvement. We develop an optimal model based on Taguchi cost functions. The model is validated through a real case study in automotive industry where the 6-sigma DMAIC methodology has been applied. According to this research, the management can adjust the investment on prevention and appraisal costs on quality improvement that enhances process capability, reduces product defect rate and, as a result, generates remarkable financial return.     

Determining the mean-variance relationship in generalized linear models—A parametric robust way

This article introduces a parametric robust way of determining the mean-variance relationship in the setting of generalized linear models. More specifically, the normal likelihood is properly amended to become asymptotically valid even if normality fails. Consequently, legitimate inference for the parametric relationship between mean and variance could be derived under model misspecification. More details are given to the scenario when the variance is proportional to an unknown power of the mean function. The efficacy of the novel technique is demonstrated via simulations and the analysis of two real data sets.     

Testing the homogeneity of proportions for clustered binary data without knowing the correlation structure

A robust generalized score test for comparing groups of cluster binary data is proposed. This novel test is asymptotically valid for practically any underlying correlation configurations including the situation when correlation coefficients vary within or between clusters. This structure generally undermines the validity of the typical large sample properties of the method of maximum likelihood. Simulations and real data analysis are used to demonstrate the merit of this parametric robust method. Results show that our test is superior to two recently proposed test statistics advocated by other researchers.     

Likelihood inference for correlated binary data without any information about the joint distributions

We propose a universal robust likelihood that is able to accommodate correlated binary data without any information about the underlying joint distributions. This likelihood function is asymptotically valid for the regression parameter for any underlying correlation configurations, including varying under- or over-dispersion situations, which undermines one of the regularity conditions ensuring the validity of crucial large sample theories. This robust likelihood procedure can be easily implemented by using any statistical software that provides naïve and sandwich covariance matrices for regression parameter estimates. Simulations and real data analyses are used to demonstrate the efficacy of this parametric robust method.     

Performance effects of IT capability, service process innovation, and the mediating role of customer service

Few academic studies have investigated how information technology (IT) capability and service process innovation can create performance gains for firms through customer service. We propose that customer service is a significant mediator through which IT capability and service process innovation influence the performance of a firm, and that IT capability is also a critical factor that facilitates service process innovation. Empirical support for our argument was derived from data collected from 174 firms in the Taiwan IT industry. The results suggest that managerial initiatives should be directed at developing IT capability and service process innovation and leveraging them to facilitate customer service to attain superior firm performance. Furthermore, greater IT capability would lead to a higher degree of service process innovation.     

Interpreting statistical evidence by using imperfect models: robust adjusted likelihood functions

Summary. The strength of statistical evidence is measured by the likelihood ratio. Two key performance properties of this measure are the probability of observing strong misleading evidence and the probability of observing weak evidence. For the likelihood function associated with a parametric statistical model, these probabilities have a simple large sample structure when the model is correct. Here we examine how that structure changes when the model fails. This leads to criteria for determining whether a given likelihood function is robust (continuing to perform satisfactorily when the model fails), and to a simple technique for adjusting both likelihoods and profile likelihoods to make them robust. We prove that the expected information in the robust adjusted likelihood cannot exceed the expected information in the likelihood function from a true model. We note that the robust adjusted likelihood is asymptotically fully efficient when the working model is correct, and we show that in some important examples this efficiency is retained even when the working model fails. In such cases the Bayes posterior probability distribution based on the adjusted likelihood is robust, remaining correct asymptotically even when the model for the observable random variable does not include the true distribution. Finally we note a link to standard frequentist methodology—in large samples the adjusted likelihood functions provide robust likelihood-based confidence intervals.     

Establishing consistency across all regions in a multi-regional clinical trial

In recent years, global collaboration has become a conventional strategy for new drug development. To accelerate the development process and shorten approval time, the design of multi-regional clinical trials (MRCTs) incorporates subjects from many countries/regions around the world under the same protocol. After showing the overall efficacy of a drug in a global trial, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each region. However, most of the recent approaches developed for the design and evaluation of MRCTs focus on establishing criteria to examine whether the overall results from the MRCT can be applied to a specific region. In this paper, we use the consistency criterion of Method 1 from the Japanese Ministry of Health, Labour and Welfare (MHLW) guidance to assess whether the overall results from the MRCT can be applied to all regions. Sample size determination for the MRCT is also provided to take all the consistency criteria from each individual region into account. Numerical examples are given to illustrate applications of the proposed approach.     

Case study: quality improvement model in a car seat assembly line

In this paper, we develop a quality improvement model based on the structure of classical economic production quantity (EPQ) model. The asymmetrical truncated loss function is used to evaluate the cost of poor quality in a production system. A practical quality improvement case which follows the 6-sigma DMAIC method in a car seat assembly line is discussed to verify the proposed model. Our model provides a fundamental structure for studying the cost of quality improvement in a production system. Based on this model, the management can evaluate the affect of quality investment to generate significant financial return in their production line.