Academic “Centres,” Epistemic Differences and Brain Circulation

This article investigates the factors that shape how migrant academics engage with fellow scholars within their countries of origin. We focus specifically on the mobility of Asian‐born faculty between Singapore, a fast‐developing education hub in Southeast Asia, and their “home” countries within the region. Based on qualitative interviews with 45 migrant academics, this article argues that while education hubs like Singapore increase the possibility of brain circulation within Asia, epistemic differences between migrant academics and home country counterparts make it difficult to establish long‐term collaboration for research. Singapore institutions also look to the West in determining how research work is assessed for tenure and promotion, encouraging Singapore‐based academics to focus on networking with colleagues and peers based in the US and Europe rather than those based in origin countries. Such conditions undermine the positive impact of academic mobility between Singapore and surrounding countries within the region.     

Tests for homogeneity of risk differences in stratified design with correlated bilateral data

ABSTRACT

Correlated bilateral data arise from stratified studies involving paired body organs in a subject. When it is desirable to conduct inference on the scale of risk difference, one needs first to assess the assumption of homogeneity in risk differences across strata. For testing homogeneity of risk differences, we herein propose eight methods derived respectively from weighted-least-squares (WLS), the Mantel-Haenszel (MH) estimator, the WLS method in combination with inverse hyperbolic tangent transformation, and the test statistics based on their log-transformation, the modified Score test statistic and Likelihood ratio test statistic. Simulation results showed that four of the tests perform well in general, with the tests based on the WLS method and inverse hyperbolic tangent transformation always performing satisfactorily even under small sample size designs. The methods are illustrated with a dataset.     

Exploring the Effects of Conditioning Factors on the Strain–Crime Association: A Test on Incarcerated Chinese Women

A key assumption of general strain theory (GST) is that various factors condition the effects of strains on crime. Past research examining this conditioning hypothesis tended to focus on youth samples and use gender as a control variable. Using survey data from Chinese female inmates, this study tests the strain–crime relationship posited in GST as well as the hypothesized effects of the conditioning factors. Regression results show that different types of strains have distinct effects on respondents’ odds to commit violent over property crimes. Conditioning factors are found to moderate the strengths of strains toward respondents’ likelihood to commit property crimes over violent crimes.     

A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties

This paper considers modelling of a non‐stationary bivariate integer‐valued autoregressive process of order 1 (BINAR(1)) where the cross‐dependence between the counting series is formed through the relationship of the current series with the previous‐lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi‐likelihood (GQL) estimating equation based on the exact specification of the mean score and the auto‐covariance structure. The proposed approach is also compared with other popular techniques such as conditional maximum likelihood (CML), generalised least squares (GLS) and generalised method of moment (GMM) based on simulated data from the proposed BINAR(1). Moreover, the model is applied to weekly series of day and night road accidents arising in some regions of Mauritius and is compared with other existing BINAR(1) models.     

Are Economic Development and Education Improvement Associated with Participation in Transnational Terrorism?

Using transnational terrorism data from 1980 to 2000, this study empirically examines the relationships between frequency of participation in transnational terrorism acts and economic development and education improvement. We find an inverse U‐shaped association between the frequency of various nationals acting as perpetrators in transnational terrorism acts and per capita income in their respective home countries. As per capita incomes increase from relatively low levels, frequencies of participation in transnational terrorism increase. However, at sufficiently higher levels of per capita income, further increase in per capita income is negatively associated with the rate of participation in transnational terrorism. Education improvement from elementary to secondary is positively correlated with frequency of participation in transnational terrorism events, whereas further improvement from secondary to tertiary level is negatively correlated with participation in transnational terrorism. We also find that citizens of countries with greater openness to international trade, lower degree of income inequality, greater economic freedom, larger proportion of population with tertiary education, and less religious prevalence participate in transnational terrorism events less frequently.     

A moment-based empirical likelihood ratio test for exponentiality using the probability integral transformation

ABSTRACT

A simple and efficient goodness-of-fit test for exponentiality is developed by exploiting the characterization of the exponential distribution using the probability integral transformation. We adopted the empirical likelihood methodology in constructing the test statistic. The proposed test statistic has a chi-square limiting distribution. For small to moderate sample sizes Monte-Carlo simulations revealed that our proposed tests are much more superior under increasing failure rate (IFR) and bathtub decreasing-increasing failure rate (BFR) alternatives. Real data examples were used to demonstrate the robustness and applicability of our proposed tests in practice.     

A Bayesian adaptive design for biosimilar trials with time-to-event endpoint

A biosimilar drug is a biological product that is highly similar to and at the same time has no clinically meaningful difference from licensed product in terms of safety, purity, and potency. Biosimilar study design is essential to demonstrate the equivalence between biosimilar drug and reference product. However, existing designs and assessment methods are primarily based on binary and continuous endpoints. We propose a Bayesian adaptive design for biosimilarity trials with time-to-event endpoint. The features of the proposed design are twofold. First, we employ the calibrated power prior to precisely borrow relevant information from historical data for the reference drug. Second, we propose a two-stage procedure using the Bayesian biosimilarity index (BBI) to allow early stop and improve the efficiency. Extensive simulations are conducted to demonstrate the operating characteristics of the proposed method in contrast with some naive method. Sensitivity analysis and extension with respect to the assumptions are presented.     

Unequal returns to social capital: the study of Malays in Singapore through a network lens

Using the concepts of ‘social capital deficit’ and ‘return deficit’, this study considers the social network aspects of social disadvantage among Malays in Singapore, as compared to Singaporean Chinese. Analysing a 2005 representative survey, we find Malays have less social capital than Chinese, a social capital deficit partly explained by their lower educational attainment. We find no return deficit in earnings: that is, every additional unit of social capital increases earnings equally for Chinese and Malays. However, we find return deficits in education: every additional unit of social capital (e.g. ties to educated parents) increases educational attainment more for Chinese than Malays. In all, this study offers a social capital explanation for Malay ‘plight’, complementing the more conventional explanations of human and economic capital.     

Estimation of discrete survival function for error‐prone diagnostic tests

The product limit or Kaplan‐Meier (KM) estimator is commonly used to estimate the survival function in the presence of incomplete time to event. Application of this method assumes inherently that the occurrence of an event is known with certainty. However, the clinical diagnosis of an event is often subject to misclassification due to assay error or adjudication error, by which the event is assessed with some uncertainty. In the presence of such errors, the true distribution of the time to first event would not be estimated accurately using the KM method. We develop a method to estimate the true survival distribution by incorporating negative predictive values and positive predictive values, into a KM‐like method of estimation. This allows us to quantify the bias in the KM survival estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival function and its variance. Asymptotic properties of the proposed estimators are provided, and these properties are examined through simulations. We demonstrate our methods using data from the Viral Resistance to Antiviral Therapy of Hepatitis C study.     

Bias-corrected confidence bands in nonparametric regression

Bias-corrected confidence bands for general nonparametric regression models are considered. We use local polynomial fitting to construct the confidence bands and combine the cross-validation method and the plug-in method to select the bandwidths. Related asymptotic results are obtained. Our simulations show that confidence bands constructed by local polynomial fitting have much better coverage than those constructed by using the Nadaraya–Watson estimator. The results are also applicable to nonparametric autoregressive time series models.