An effective approach to linear calibration estimation with its applications

Abstract

The availability of some extra information, along with the actual variable of interest, may be easily accessible in different practical situations. A sensible use of the additional source may help to improve the properties of statistical techniques. In this study, we focus on the estimators for calibration and intend to propose a setup where we reply only on first two moments instead of modeling the whole distributional shape. We have proposed an estimator for linear calibration problems and investigated it under normal and skewed environments. We have partitioned its mean squared error into intrinsic and estimation components. We have observed that the bias and mean squared error of the proposed estimator are function of four dimensionless quantities. It is to be noticed that both the classical and the inverse estimators become the special cases of the proposed estimator. Moreover, the mean squared error of the proposed estimator and the exact mean squared error of the inverse estimator coincide. We have also observed that the proposed estimator performs quite well for skewed errors as well. The real data applications are also included in the study for practical considerations.     

A Study of the Relationship Between Innovation and Performance Among NPOs in Pakistan

Abstract

Innovation has become a core driver for the success and survival of organizations regardless of their size and nature. A plethora of literature has discussed the importance of innovation in profit-based organizations, while nonprofit organizations (NPOs) have generally been ignored in research. This study examines the influence of the sub-dimensions of innovation on the performance of NPOs. Data were collected through structured questionnaires using a sample size of 309 NPOs operating in the emerging market Pakistan. The hypotheses were tested through structural equation modeling (SEM) in AMOS.21. The results indicate that process innovation and organization innovation have significant positive influence on the performance of NPOs, while product innovation and marketing innovation have insignificant influence on the performance of NPOs. NPOs are advised to give enough attention to process and organization innovation in order to boost their performance. Future researchers are encouraged to test the model in other environmental settings. Implications for practice were discussed.     

Construction of repeated measurements designs strongly balanced for residual effects

Abstract

Repeated Measurements Designs have been widely used in agriculture, animal husbandry, education, biology, botany and engineering. Balanced or strongly balanced repeated measurements designs are useful to balance out the residual effects. In this article, some new generators and construction procedures are proposed to obtain circular strongly balanced repeated measurements designs in periods of (a) equal sizes, (b) two different sizes, and (c) three different sizes.     

Some new construction of circular weakly balanced repeated measurements designs in periods of two different sizes

Abstract

Balanced repeated measurements designs (RMDs) balance out the residual effects. Williams Latin square designs work as minimal combinatorial balanced as well as variance balanced for RMDs for p (period sizes) = v (number of treatments). If minimal balanced RMDs cannot be constructed for the situations where p must be less than v then weakly balanced RMDs should be preferred. In this article, some generators are developed to generate circular weakly balanced RMDs in periods of two different sizes. To obtain the proposed designs, some construction procedures are also described for some of the cases where we could not develop generators.     

Simultaneous estimation of Cronbach’s alpha coefficients

The simultaneous estimation of Cronbachs alpha coefficients from q populations under the compound symmetry assumption is considered. In a multi-sample scenario, it is suspected that all the Cronbachs alpha coefficients are identical. Consequently, the inclusion of non-sample information (NSI) on the homogeneity of Cronbachs alpha coefficients in the estimation process may improve precision. We propose improved estimators based on the linear shrinkage, preliminary test, and the Steins type shrinkage strategies, to incorporate available NSI into the estimation. Their asymptotic properties are derived and discussed using the concepts of bias and risk. Extensive Monte-Carlo simulations were conducted to investigate the performance of the estimators.     

International Academic Mobility in Chinese Academia: Opportunities and Challenges

International academic mobility is an important dimension of the internationalization of higher education institutions, which aims to enact practical changes in economies and societies. Although many studies have investigated the mobility of international students, the mobility of international academics has been less investigated, particularly in the context of mainland China. This qualitative study explores the experience of international academics in Chinese academia. The study is based on semi-structured interviews with twenty-six international academics from different countries, working at different universities in Beijing. Through a four-stage systematic analysis, the study shows that international academics’ engagement with mobility in Chinese academia can be characterized by a combination of gains and losses. This article explores how international academics view Chinese academia as either resourceful or restrictive for their academic career. It then discusses the hidden narratives concerning the challenges that international academics face in relation to mobility.     

Performance of Kibria's Method for the Heteroscedastic Ridge Regression Model: Some Monte Carlo Evidence

It is common for a linear regression model that the error terms display some form of heteroscedasticity and at the same time, the regressors are also linearly correlated. Both of these problems have serious impact on the ordinary least squares (OLS) estimates. In the presence of heteroscedasticity, the OLS estimator becomes inefficient and the similar adverse impact can also be found on the ridge regression estimator that is alternatively used to cope with the problem of multicollinearity. In the available literature, the adaptive estimator has been established to be more efficient than the OLS estimator when there is heteroscedasticity of unknown form. The present article proposes the similar adaptation for the ridge regression setting with an attempt to have more efficient estimator. Our numerical results, based on the Monte Carlo simulations, provide very attractive performance of the proposed estimator in terms of efficiency. Three different existing methods have been used for the selection of biasing parameter. Moreover, three different distributions of the error term have been studied to evaluate the proposed estimator and these are normal, Student's t and F distribution.     

Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects

Inappropriate management of health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the availability of H&S incident data, utilizing them to mitigate accident occurrence effectively is challenging due to inherent limitations of existing data logging methods. In this study, we used a text-mining approach for retrieving meaningful terms from data and develop six deep learning (DL) models for H&S risks management in power infrastructure. The DL models include DNN_classify (risk or no risk), DNN_reg1 (loss time), DNN_reg2 (body injury), DNN_reg3 (plant and fleet), DNN_reg4 (equipment), and DNN_reg5 (environment). An H&S risk database obtained from a leading UK power infrastructure construction company was used in developing the models using the H2O framework of the R language. Performances of DL models were assessed and benchmarked with existing models using test data and appropriate performance metrics. The overall accuracy of the classification model was 0.93. The average R² value for the five regression models was 0.92, with mean absolute error between 0.91 and 0.94. The presented results, in addition to the developed user-interface module, will help practitioners obtain a better understanding of H&S challenges, minimize project costs (such as third-party insurance and equipment repairs), and offer effective strategies to mitigate H&S risk.     

A Bayesian way of monitoring the linear profiles using CUSUM control charts

In statistical process control applications, profiles functions are considered an efficient way of representing quality of products or processes. Classical and Bayesian thoughts are two chief sources of defining control charting structures for profiles monitoring. This Study introduces novel Bayesian CUSUM control structures for profiles monitoring. The comprehensive comparative study identifies that the proposed Bayesian CUSUM control charts under conjugate priors has better expected performance than competing methods. The implementation of Bayesian structures requires detailed information about process parameters which come up with considerable benefits. In addition, simulative example and case study further justified the superiority of proposed techniques.     

An Assessment of Vulnerability to Poverty in Punjab,Pakistan: Subjective Choices of Poverty Indicators

In general, the happiness literature has paid little attention to the relationship between physical appearance and well-being. In this paper, we examine the link between weight, height and well-being for three distinct samples in China given that attractiveness effects likely vary greatly across sociocultural contexts. As China has recently undergone rapid economic transformation in the urban areas, this empirical exercise is particularly interesting because it can highlight how changing social norms have affected the relationship between physical appearance and subjective well-being. For the rural and migrant samples, we find that for both men and women, big and tall individuals have higher levels of well-being. This is consistent with the notion that the strong are better off when more labor intensive work is the norm. For the urban sample and for urban males in particular, no well-being penalty is found for being obese, unlike previous results based on Western samples. It is very likely that the unique Chinese cultural practice of network building banquets and feasting is behind this finding.