Credit and Ethnic Consumption Inequality in the Central Highlands of Vietnam

Social Indicators Research - Credit is commonly considered an important instrument to relieve financial capital constraints of poor households and subsequently to improve their welfare. However,...     

A new diagnostic tool for VARMA(p,q) models

A new diagnostic method for VARMA(p,q) time series models is introduced. The procedure is based on a statistic that generalizes to a multivariate setting the properties of the usual univariate ARMA(p,q) residual correlations. A multiple version of the cumulative periodogram statistic is also suggested. Simulation studies and one real data application are presented.     

Progress on a conjecture regarding the triangular distribution

Triangular distributions are a well-known class of distributions that are often used as an elementary example of a probability model. Maximum likelihood estimation of the mode parameter of the triangular distribution over the unit interval can be performed via an order statistic based method. It had been conjectured that such a method can be conducted using only a constant number of likelihood function evaluations, on average, as the sample size becomes large. We prove two theorems that validate this conjecture. Graphical and numerical results are presented to supplement our proofs.     

Inferring an Augmented Bayesian Network to Confront a Complex Quantitative Microbial Risk Assessment Model with Durability Studies: Application to Bacillus Cereus on a Courgette Purée Production Chain

The Monte Carlo (MC) simulation approach is traditionally used in food safety risk assessment to study quantitative microbial risk assessment (QMRA) models. When experimental data are available, performing Bayesian inference is a good alternative approach that allows backward calculation in a stochastic QMRA model to update the experts’ knowledge about the microbial dynamics of a given food‐borne pathogen. In this article, we propose a complex example where Bayesian inference is applied to a high‐dimensional second‐order QMRA model. The case study is a farm‐to‐fork QMRA model considering genetic diversity of Bacillus cereus in a cooked, pasteurized, and chilled courgette purée. Experimental data are Bacillus cereus concentrations measured in packages of courgette purées stored at different time‐temperature profiles after pasteurization. To perform a Bayesian inference, we first built an augmented Bayesian network by linking a second‐order QMRA model to the available contamination data. We then ran a Markov chain Monte Carlo (MCMC) algorithm to update all the unknown concentrations and unknown quantities of the augmented model. About 25% of the prior beliefs are strongly updated, leading to a reduction in uncertainty. Some updates interestingly question the QMRA model.     

Maximizing k-submodular functions under budget constraint: applications and streaming algorithms

Journal of Combinatorial Optimization - Motivated by the practical applications in solving plenty of important combinatorial optimization problems, this paper investigates the Budgeted k-Submodular...     

Harnessing the reliability of outcome measures

A method is presented for engineering the necessary levels of measurement reliability for evaluating ongoing programs. Two studies of levels of client functioning at a community mental health center (CMHC), in which an outcome measure, the Global Assessment Scale (GAS), did not perform as expected, drew attention to the need for better control of outcome measure reliabilities. Drawing from generalizability theory, a study was conducted of three sources of GAS score variance — clients, raters, and training in the use of the scale. Several estimates of reliability (ERs) were developed, depending on the manner in which the GAS ratings were, or would be, obtained in the CMHC. The differences among these ERs clarified why the GAS had lower reliability when used in our setting. Finally, two hypothetical examples are described to illustrate the utility of applying generalizability theory to achieve higher reliabilities for outcome measures.     

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.     

Consumer Behaviour and Order Fulfilment in Online Retailing: A Systematic Review

This paper provides a systematic review of consumer behaviour and order fulfilment in online retailing. The objective of this review is threefold: first, to identify elements of order‐fulfilment operations that are relevant to online consumer behaviour (purchase, repurchase, product return); second, to understand the relationship between order‐fulfilment performance and consumer behaviour; and third, to inspire future research on developing consumer service strategies that takes account of these behavioural responses to order‐fulfilment performance outcomes. The paper is based on a systematic review of literature on online consumer behaviour and order‐fulfilment operations, mainly in the fields of marketing and operations, published in international peer‐reviewed journals between 2000 and September 2015. This study indicates that the current literature on online consumer behaviour focuses mainly on the use of marketing tools to improve consumer service levels. Very little research has been conducted on the use of consumer service instruments to steer consumer behaviour or, consequently, to manage related order‐fulfilment activities better. The study culminates in a framework that encompasses elements of order‐fulfilment operations and their relationship to online consumer behaviour. This paper is the first comprehensive review of online consumer behaviour that takes aspects of order‐fulfilment operations into account from both marketing and operations perspectives.