Correction to: The impact of parental illness on children’s schooling and labour force participation: evidence from Vietnam

Review of Economics of the Household - The original version of this article unfortunately contained some errors in reference citations and in significance indicators in some of the tables. These...     

Depressive Symptoms in Mothers of Recently Adopted Post-Institutionalized Children

To determine whether depressive symptoms are elevated within months of adoption, we compared women who had recently adopted a toddler from an overseas orphanage to women with children of the same age who adopted more than a year before or were raising a child they birthed. The groups did not differ significantly in means or in percentage with elevated symptoms (Session 1, χ²(2) = 2.5, ns; Session 2, χ²(2) = 1.38, ns). The results provided no evidence of elevated symptoms in the first months following adoption for women who adopted internationally relative to other women with toddler-aged children.     

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.