Mike K. P. So

The Hong Kong University of Science and Technology | UST · Department of Information Systems, Business Statistics and Operations Management

This study presents a dynamic Bayesian network framework that facilitates intuitive gradual edge changes. We use two conditional dynamics to model the edge addition and deletion, and edge selection separately. Unlike previous research that uses a mixture network approach, which restricts the number of possible edge changes, or structural priors to...

There is growing interest in accommodating network structure in panel data models. We consider dynamic network Poisson autoregressive (DN-PAR) models for panel count data, enabling their use in regard to a time-varying network structure. We develop a Bayesian Markov chain Monte Carlo technique for estimating the DN-PAR model, and conduct Monte Carl...

Family caregiving often leads to increased stress and anxiety due to intensive support in daily living and medical tasks. Traditionally, determining appropriate interventions has been a challenging task, given the need to conduct extended cohort studies with a restricted range of interventions, which proves to be time-consuming and costly. Moreover...

Background The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries’ pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era. Objective We aim to syste...

Background: The COVID-19 pandemic has posed various difficulties for policymakers, such as the identification of health issues, establishment of policy priorities, formulation of regulations, and promotion of economic competitiveness. Evidence-based practices and data-driven decision-making have been recognized as valuable tools for improving the p...

To accommodate linkage effects of individuals, we develop a new linkage vector autoregressive (LAR) model for dynamic panel data. A main feature of the LAR model is incorporating dynamic network information in autoregressive time series modeling. The dynamic network can be given, or we can formulate the network links as a function of historical dat...

The study of assortativity allows us to understand the heterogeneity of networks and the implication of network resilience. While a global measure has been predominantly used to characterize this network feature, there has been little research to suggest a local coefficient to account for the presence of local (dis)assortative patterns in diversely...

The stress burden generated from family caregiving makes caregivers particularly prone to developing psychosocial health issues; however, with early diagnosis and intervention, disease progression and long-term disability can be prevented. We developed an automatic speech analytics program (ASAP) for the detection of psychosocial health issues base...

Background The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to...

The internationalization of higher education has become a key policy within the global higher education sector. Yet a large body of literature suggests that simply having a diverse group of students does not guarantee meaningful intercultural engagement. This paper presents a qualitative study intended to gain a richer understanding of Hong Kong’s...

Online platforms are experimenting with interventions such as content screening to moderate the effects of fake, biased, and incensing content. Yet, online platforms face an operational challenge in implementing machine learning algorithms for managing online content due to the labeling problem, where labeled data used for model training are limite...

Systemic risk refers to the uncertainty that arises due to the breakdown of a financial system. The concept of “too connected to fail” suggests that network connectedness plays an important role in measuring systemic risk. In this paper, we first recover a time series of Bayesian networks for stock returns, which allow the direction of links among...

Background: In this globalization era, institutions are developing strategies including international service-learning pedagogies to integrate global perspectives and dimensions into the learning and teaching processes to develop students' capacity in intercultural competence. Objective: This study aimed to assess the students' intercultural lea...

This paper, “Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue,” proposes a novel Bayesian approach based on dynamic linear models to share information from different sectors, LSG (Local Store Group), and item category, through the use of auxiliary information (the discount information). The authors demonstrate the feasibil...

Background Interviewer effects may cause unfairness in assessments in multiple mini-interviews (MMIs). Due to cultural differences, the bias factors of interviewers may vary between the East and the West. MMIs are a relatively new type of assessment setting in China and few studies have been conducted to examine the interviewer effects of MMIs in t...

Estimating time-varying conditional covariance matrices of financial returns play important role in portfolio analysis, risk management, and financial econometrics research. The availability of high-frequency financial data can provide an additional data source for dynamic covariance modeling. In this paper, we propose to use the information of ass...

BACKGROUND The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to...

This study assesses governments' long-term non-pharmaceutical interventions upon the coronavirus disease 2019 (COVID-19) pandemic in East Asia. It advances the literature towards a better understanding of when and which control measures are effective. We (1) provide time-varying case fatality ratios and focus on the elderly's mortality and case fat...

In this paper, we develop a new statistical disclosure control (SDC) method for mixed-type data based on vine copulas. The use of Gaussian and skew-t copulas has been demonstrated to be capable of incorporating information from the marginal distributions of mixed-type variables, whether they are discrete or continuous. In particular, our proposed S...

The prevalence of big data has raised significant epistemological concerns in information systems research. This study addresses two of them—the deflated p -value problem and the role of explanation and prediction. To address the deflated p -value problem, we propose a multivariate effect size method that uses the log-likelihood ratio test. This me...

Crude oil draws attention in recent research as its demand may indicate world economic growth trend in the post-COVID-19 era. In this paper, we study the dynamic lead–lag relationship between the COVID-19 pandemic and crude oil future prices. We perform rolling-sample tests to evidence whether two pandemic risk scores derived from network analysis,...

Background Health information technologies (HITs) are increasingly being used to support the self-management of chronic diseases. However, patients’ initial or continued acceptance of such technologies is not always achieved. Objective The aim of this study was to develop a theory-driven HIT acceptance model to examine factors affecting acceptance...

Systemic risk in financial markets refers to the breakdown of a financial system due to global events, catastrophes, or extreme incidents, leading to huge financial instability and losses. This study proposes a dynamic topic network (DTN) approach that combines topic modelling and network analysis to assess systemic risk in financial markets. We ma...

A new statistical method is proposed to combine the randomized response technique, probit modeling, and Bayesian analysis to analyze large-scale online surveys of multiple binary randomized responses. The proposed method is illustrated by analyzing sensitive dichotomous randomized responses on different types of drug administration error from nurse...

During the 2019 novel coronavirus disease (COVID-19) pandemic, many employees have switched to working from home. Despite the findings of previous research that working from home can improve productivity, the scale, nature, and purpose of those studies are not the same as in the current situation with the COVID-19 pandemic. We studied the effects t...

Understanding how textual information impacts financial market volatility has been one of the growing topics in financial econometric research. In this paper, we aim to examine the relationship between the volatility measure that is extracted from GARCH modelling and textual news information both publicly available and from subscription, and the pe...

The COVID-19 pandemic causes a huge number of infections. The outbreak of COVID-19 has not only caused substantial healthcare impacts, but also affected the world economy and financial markets. In this paper, we study the effect of the COVID-19 pandemic on financial market connectedness and systemic risk. Specifically, we test dynamically whether t...

This study examined the association between caregivers’ burdens and their individual characteristics and identified characteristics that are useful for predicting the level of caregiver burden. We successfully surveyed 387 family caregivers, having them complete the caregiver burden inventory scale (CBI) and an individual characteristic questionnai...

In this article, we extend the skew‐t data perturbation (STDP) to develop a new statistical disclosure control (SDC) method for data with continuous variables. In this new SDC method, we construct an extended skew‐t (EST) copula to release confidential data for third‐party usage. Using the EST copula for producing perturbed data, we can incorporate...

Objective To validate the Family Resilience Assessment Scale (FRAS) in Chinese family caregivers. Background Caregiver burden among family caregivers is a growing social issue. Family resilience is a crucial protective factor for easing caregiver burden. The FRAS is specifically designed to evaluate family resilience. However, the factor structure...

This article proposes a novel multivariate generalized autoregressive conditionally heteroscedastic (GARCH) model that incorporates the modified Cholesky decomposition for a covariance matrix in order to reduce the number of covariance parameters and increase the interpretation power of the model. The modified Cholesky decomposition for covariance...

An understanding of the financial instability during financial crises is an important topic in risk management. Market participants actively use risk indicators, such as the VIX in the US, the VHSI in Hong Kong and the V2TX in Europe, which are derived from derivative products, to measure market anxiety and fear and thus to estimate systemic risk i...

The global coronavirus disease (COVID-19) outbreak forced a shift from face-to-face education to online learning in higher education settings around the world. From the outset, COVID-19 online learning (CoOL) has differed from conventional online learning due to the limited time that students, instructors, and institutions had to adapt to the onlin...

The coronavirus disease 2019 (COVID‐19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The spread of the disease has also caused dramatic uncertainty in financial markets, especially in the early stages of the pandemic. In this paper, we adopt the stochastic actor‐oriented model (SAOM) to mod...

Since the introduction of ARCH models close to 40 years ago, a wide range of models for volatility estimation and prediction have been developed and integrated into asset allocation, financial derivative pricing, and financial risk management. Research has also been very active in extending volatility modeling to dependence modeling and in developi...

The coronavirus disease 2019 (COVID-19) pandemic has affected educational institutions and instructors in an unprecedented way. The majority of educational establishments were forced to take their courses online within a very short period of time, and both instructors and students had to learn to navigate the digital array of courses without much t...

In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization...

The spread of coronavirus disease 2019 (COVID-19) has caused more than 80 million confirmed infected cases and more than 1.8 million people died as of 31 December 2020. While it is essential to quantify risk and characterize transmission dynamics in closed populations using Susceptible-Infection-Recovered modeling, the investigation of the effect f...

Nowadays, we mainly depend on financial consultants or advisors to conduct risk assessments for individual investors before providing them with any investment advice or recommendations. Individual investors should understand the risk level of their investment choices and their investment decisions should match their risk profile. This process is us...

Background: Communicable diseases, such as coronavirus disease 2019, pose a major threat to public health across the globe. To effectively curb the spread of communicable diseases, timely surveillance and prediction of pandemic risk are essential. Objective: The aim of this study is to analyze free and publicly available data to construct useful...

UNSTRUCTURED Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for net...

Interfirm relationships are crucial to our understanding of firms’ collective and interactive behavior. Many information systems-related phenomena, including the diffusion of innovations, standard alliances, technology collaboration, and outsourcing, involve a multitude of relationships between firms. This study proposes a latent space approach to...

Objectives United States has become the country with the largest number of COVID-19 reported cases and deaths. This study aims to analyze the pandemic risk of COVID-19 outbreak in the US. Methods Time series plots of the network density, together with the daily reported confirmed COVID-19 cases and flight frequency in the five states in the US wit...

In this paper, we study the impacts of the COVID-19 pandemic on the connectedness of the Hong Kong financial market. We construct dynamic financial networks based on correlations and partial correlations of stock returns to assess the impacts of COVID-19 and to compare the impacts in the previous financial crises in the past 15 years. Compared to o...

In this paper, we consider a quasi‐maximum likelihood (QML) estimation of conditional autoregressive Wishart models, which generalize the conditional autoregressive Wishart models by not restricting the conditional distribution of covariances to follow the Wishart distribution. Strong consistency is established under the existence of the expectatio...

In this paper, we incorporate a GARCH model into an artificial neural network (ANN) for financial volatility modeling and estimate the parameters in Tensorflow. Our goal was to better predict stock volatility. We evaluate the performance of the models using the mean absolute errors of powers of the out-of-sample returns between 2 March 2018 and 28...

The spread of coronavirus disease 2019 (COVID-19) has caused more than 24 million confirmed infected cases and more than 800,000 people died as of 28 August 2020. While it is essential to quantify risk and characterize transmission dynamics in closed populations using Susceptible-Infection-Recovered modeling, the investigation of the effect from wo...

This paper aims to explore a modified method of high-dimensional dynamic variance–covariance matrix estimation via risk factor mapping, which can yield a dependence estimation of asset returns within a large portfolio with high computational efficiency. The essence of our methodology is to express the time-varying dependence of high-dimensional ret...

A novel use of network analysis in public health Developing a quantitative assessment method for the COVID-19 pandemic risk Exploring the time series of network density for early warning signals of pandemic risk Tracking the evolution of pandemic risk through the degree of connectedness.

With the domestic and international spread of the COVID-19, much attention has been given to estimating pandemic risk. We propose the use of a novel application of a well-established scientific approach, network analysis, to provide a direct visualisation (the infographics in Figures 1 and 2) of the COVID-19 pandemic risk. By showing visually the d...

This article examines the occurrences of four types of unethical employee information security behavior—misbehavior in networks/applications, dangerous Web use, omissive security behavior, and poor access control—and their relationships with employees’ information security management efforts to maintain sustainable information systems in the workpl...

Bayesian quasi-likelihoods constructed from estimating functions extend the scope of Bayesian inference to a wide range of semi-parametric problems. Nonetheless, when the estimating functions possess complex structure, like containing highly irregular weighting matrices, the quasi-likelihoods constructed from those estimating functions may deform w...

Background Technological surrogate nursing (TSN) derives from the idea that nurse-caregiver substitutes can be created by technology to support chronic disease self-care. Objective This paper begins by arguing that TSN is a useful and viable approach to chronic disease self-care. The analysis then focuses on the empirical research question of test...

Objective: To test whether self-administered acupressure reduces stress and stress-related symptoms in caregivers of older family members. Design: In this randomized, assessor-blind, controlled trial, 207 participants were randomized (1:1) to an acupressure intervention or a wait-list control group. Setting: Community centers in Hong Kong, Chi...

Patient data or information collected from public health and health care surveys are of great research value. Usually, the data contain sensitive personal information. Doctors, nurses, or researchers in the public health and health care sector do not analyze the available datasets or survey data on their own, and may outsource the tasks to third pa...

To understand and predict chronological dependence in the second‐order moments of asset returns, this paper considers a multivariate hysteretic autoregressive (HAR) model with generalized autoregressive conditional heteroskedasticity (GARCH) specification and time‐varying correlations, by providing a new method to describe a nonlinear dynamic struc...

eHealth has become popular worldwide, and it is transforming health care. However, studies examining the use of eHealth applications in the Chinese population are scarce. The study reports on the characteristics of eHealth applications in Hong Kong information and communication technology (ICT) users, their attitudes towards eHealth, and their reas...

Sensitive questions are often involved in healthcare or medical survey research. Much empirical evidence has shown that the randomized response technique is useful for the collection of truthful responses. However, few studies have discussed methods to estimate the dependence of sensitive responses of multiple types. This study aims to fill that ga...

A survey study is a research method commonly used to quantify population characteristics in biostatistics and public health research, two fields that often involve sensitive questions. However, if answering sensitive questions could cause social undesirability, respondents may not provide honest responses to questions that are asked directly. To mi...

The purpose of this paper is to extend the work of So (1999 So, M. K. P. 1999. Time series with additive noise. Biometrika 86 (2):474–82. doi: 10.1093/biomet/86.2.474.[Crossref], [Web of Science ®] , [Google Scholar]) by accommodating heteroskedasticity in long memory processes and correlation in disturbances. We propose an alternative representati...

Objective To compare the angle of progression (AoP) measured by transperineal ultrasonography before indicating an instrumental delivery or cesarean delivery. Methods A prospective observational study was conducted among women with singleton term pregnancies with prolonged second stage of labor at Kwong Wah Hospital, Hong Kong, China, between May...

This paper proposes the use of threshold heteroskedastic models which integrate threshold nonlinearity [Tong, H (1978). On a Threshold Model, pp. 575–586. Netherlands: Sijthoff & Noordhoff; Tong, H and KS Lim (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological), 3, 245...

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility i...

A Qigong App was designed to promote a more flexible mode of delivering qigong training than face-to-face, with which individuals can access to this mind-body aerobic exercise more readily. The objective of the study was to examine the usability and acceptance of the App. Target participants were Cantonese- or Putonghua-speaking adults and owned a...

The randomized response technique (RRT) is a classical and effective method used to mitigate the distortion arising from dishonest answers. The traditional RRT usually focuses on the case of a single sensitive attribute, and discussion of the case of multiple sensitive attributes is limited. Here, we study a business case to identify some individua...

Mitigating response distortion in answers to sensitive questions is an important issue for business ethics researchers. Sensitive questions may be asked in surveys related to business ethics, and respondents may intend to avoid exposing sensitive aspects of their character by answering such questions dishonestly, resulting in response distortion. P...

To model extreme spatial events, a general approach is to use the generalized extreme value (GEV) distribution with spatially varying parameters such as spatial GEV models and latent variable models. In the literature, this approach is mostly used to capture spatial dependence for only one type of event. This limits the applications to air pollutan...

Spatial–temporal modeling is commonly used to explain the dependence of environmental and socio-economic variables over space and time. Early published works usually assumed constant second and fourth moments. In this paper, we propose a new spatial time series model with dynamic variance and kurtosis. A distinctive feature of our proposed model is...

The max-stable process is a natural approach for modelling extrenal dependence in spatial data. However, the estimation is difficult due to the intractability of the full likelihoods. One approach that can be used to estimate the posterior distribution of the parameters of the max-stable process is to employ composite likelihoods in the Markov chai...

Background Caregiving can be stressful, potentially creating physical and psychological strain. Substantial evidence has shown that family caregivers suffer from significant health problems arising from the demands of caregiving. Although there are programs supporting caregivers, there is little evidence regarding their effectiveness. Acupressure i...

The threshold autoregressive model with generalized autoregressive conditionally heteroskedastic (GARCH) specification is a popular nonlinear model that captures the well-known asymmetric phenomena in financial market data. The switching mechanisms of hysteretic autoregressive GARCH models are different from threshold autoregressive model with GARC...

Stochastic covariance models have been explored in recent research to model the interdependence of assets in financial time series. The approach uses a single stochastic model to capture such interdependence. However, it may be inappropriate to assume a single coherence structure at all time t. In this paper, we propose the use of a mixture of stoc...

Integer-valued time series analysis offers various applications in biomedical, financial, and environmental research. However, existing works usually assume no or constant over-dispersion. In this paper, we propose a new model for time series of counts, the autoregressive conditional negative binomial model that has a time-varying conditional autor...