Nonlinear regression analysis for complex surveys 1

In a clustered finite population, it is assumed that a given function depending on an unknown parameter may be adopted to reveal the relationship among the variables of interest. The finite population parameter corresponding to this unknown parameter is defined as a solution of an estimating equation defined by a properly chosen population loss function. An estimation procedure that takes sample weights into account is considered. Use of this function in estimating the population mean per cluster is discussed. Large sample properties of estimators are investigated.     

A simulation study of dispatch rules for reducing flow times in semiconductor wafer fabrication

Semiconductor wafer fabrication involves very complex process routing, and reducing flow times is very important. This study reports a search for better dispatch rules for achieving the goal of reducing flow times, while maintaining high machine utilization. We explored a new simulation-based dispatch rule and a queue prediction dispatch rule. Using simulation experiments and an industrial data set, we also compared several other dispatch rules commonly used in semiconductor manufacturing with our proposed dispatch rules. Among these rules, in addition to the simulation-based dispatching rule, the shortest-remaining-processing-time, earliest-due-date and leastslack rules also performed well in terms of reducing flow times. The reasons behind these good rules are discussed in this paper. Based on the previous works and this study, accurately predicting and effectively utilizing future flow times can improve the quality of production management decisions.     

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback‐Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.     

An intuitive clustering algorithm for spherical data with application to extrasolar planets

This paper proposes an intuitive clustering algorithm capable of automatically self-organizing data groups based on the original data structure. Comparisons between the propopsed algorithm and EM [1 A. Banerjee, I.S. Dhillon, J. Ghosh, and S. Sra, Clustering on the unit hypersphere using von Mises–Fisher distribution, J. Mach. Learn. Res. 6 (2005), pp. 1–39. [Google Scholar]] and spherical k-means [7 I.S. Dhillon and D.S. Modha, Concept decompositions for large sparse text data using clustering, Mach. Learn. 42 (2001), pp. 143–175. doi: 10.1023/A:1007612920971[Crossref], [Web of Science ®] , [Google Scholar]] algorithms are given. These numerical results show the effectiveness of the proposed algorithm, using the correct classification rate and the adjusted Rand index as evaluation criteria [5 J.-M. Chiou and P.-L. Li, Functional clustering and identifying substructures of longitudinal data, J. R. Statist. Soc. Ser. B. 69 (2007), pp. 679–699. doi: 10.1111/j.1467-9868.2007.00605.x[Crossref] , [Google Scholar],6 J.-M. Chiou and P.-L. Li, Correlation-based functional clustering via subspace projection, J. Am. Statist. Assoc. 103 (2008), pp. 1684–1692. doi: 10.1198/016214508000000814[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. In 1995, Mayor and Queloz announced the detection of the first extrasolar planet (exoplanet) around a Sun-like star. Since then, observational efforts of astronomers have led to the detection of more than 1000 exoplanets. These discoveries may provide important information for understanding the formation and evolution of planetary systems. The proposed clustering algorithm is therefore used to study the data gathered on exoplanets. Two main implications are also suggested: (1) there are three major clusters, which correspond to the exoplanets in the regimes of disc, ongoing tidal and tidal interactions, respectively, and (2) the stellar metallicity does not play a key role in exoplanet migration.     

The Retirement Provisions in China,Hong Kong,Singapore and Taiwan: Perspectives on Policy Inputs and Social Outputs

This paper aims to develop an integrated policy index system using a Surface Measure of Overall Performance (SMOP) approach to comprehensively evaluate and compare the policy input and social output of the retirement payment system in urban and rural China, Hong Kong, Singapore and Taiwan. With this, a tool may be developed to help insightfully examine the old‐age income policies and appropriately, and perhaps theoretically in the future, categorize the types of retirement provisions or social security policies as a whole in the East and West. The results indicate that, particularly in urban China, Hong Kong, Singapore and Taiwan, a low level of de‐familization and medium and high levels of gender equality are the common features of these five pensions in China, Hong Kong, Singapore and Taiwan. Furthermore, it is found that the retirement provisions in China, Hong Kong, Singapore and Taiwan can well be divided into two groups based on the level of pension right protection and illustrate different characteristics between them accordingly. In addition, policy implications and suggestions for further reforms of these retirement payment schemes are elaborated in the light of the findings of this policy index system.     

Comparison of Six Dose-Response Models for Use with Food-Borne Pathogens

Food-related illness in the United States is estimated to affect over six million people per year and cost the economy several billion dollars. These illnesses and costs could be reduced if minimum infectious doses were established and used as the basis of regulations and monitoring. However, standard methodologies for dose-response assessment are not yet formulated for microbial risk assessment. The objective of this study was to compare dose-response models for food-borne pathogens and determine which models were most appropriate for a range of pathogens. The statistical models proposed in the literature and chosen for comparison purposes were log-normal, log-logistic, exponential, -Poisson and Weibull-Gamma. These were fit to four data sets also taken from published literature, Shigella flexneri, Shigella dysenteriae,Campylobacter jejuni, and Salmonella typhosa, using the method of maximum likelihood. The Weibull-gamma, the only model with three parameters, was also the only model capable of fitting all the data sets examined using the maximum likelihood estimation for comparisons. Infectious doses were also calculated using each model. Within any given data set, the infectious dose estimated to affect one percent of the population ranged from one order of magnitude to as much as nine orders of magnitude, illustrating the differences in extrapolation of the dose response models. More data are needed to compare models and examine extrapolation from high to low doses for food-borne pathogens.     

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.     

Strengthening urban community governance through geographical information systems and participation: An evaluation of my Google Map and service coordination

It is increasingly common for urban planners and local policymakers to adopt interactive technologies, such as geographical information systems (GISs), to inform their decision making and to enhance performance. This paper aimed to assess the role that interactive technology can play in enhancing urban governance to meet social needs by improving local organisations’ capacities, client access, and service effectiveness and responsiveness, as well as community building. Using the previous research framework on mapping community capacity developed by Brudney et al. (Community Development Journal, 2016, 52, 354) and Çöltekin et al. (Cartography and Geographic Information Science, 2009, 36, 5–17), we examined a simulated online interactive map for service provision in Hong Kong and interviewed around 20 local community organisations and 100 local residents about their user experience, their willingness to support its long-term adoption and their challenges. Our findings reveal that local residents find the interactive map helpful, particularly in improving service access, whereas local organisations are more reluctant to adopt such technology due to high maintenance costs. The implications of our study include the need to develop incentives to encourage technology adoption, to improve the capacity of local organisations and to establish better links between community organisations and local residents to identify sustainable financing (through crowdsourcing) for GIS applications to improve urban community governance.     

Poverty affects access to regular source of primary care among the general population in Hong Kong

A territory‐wide two‐stage stratified random sample of 2,282 community‐dwelling Hong Kong adults were surveyed between 2014 and 2015 to investigate the association between poverty and regular source of primary care utilization. Poverty was operationalized by income‐poverty and deprivation. About 94% of our sample reported having regular source of primary care (Western and/or Chinese medical practitioner) and about 69% among them were in private sector. Multivariable logistic regression showed that people who were income‐poor and deprived were less likely to have regular source of primary care (income‐poor: OR = 0.523, p = .027; deprived: OR = 0.488, p = .007) and visit private primary care doctors (income‐poor: OR = 0.445, deprived: OR = 0.222, both p < .0001). Those who had chronic diseases were more likely to have regular source of primary care (multimorbid: OR = 10.709, p < .0001), but less likely to access care in the private sector (one chronic disease: OR = 0.690, p = .019; multimorbid: OR = 0.374, p < .0001) than those without. Further, being older and less skilled were significantly associated with less likelihood of visiting a private doctor. Path analysis showed that the number of chronic diseases had significant indirect effect on having regular source of primary care with being income‐poor and deprived as the mediators (β = ?.0183, p = .0016). Therefore, despite a public health‐care system that aims to deny no one from adequate health care for lack of means, regular source of primary care in Hong Kong is found to be pro‐rich. Future policies should tackle the problem of health‐care inequalities to meet the needs of the underprivileged.