Participatory child poverty assessment in rural Vietnam

There are increasing calls for more child specific measures of poverty in developing countries and the need for such measures to be multi‐dimensional (that is not just based on income) has been recognised. Participatory Poverty Assessments (PPAs) are now common in international development research. Most PPAs have been undertaken with adults and there are still relatively few PPAs with children. The objective of the current study was to understand adults' and children's perceptions of the causes and consequences of child poverty in rural Vietnam using a variety of participatory methods. Poor children are perceived by poor children as those who lack basic needs such as food, clothes, and safe shelter. Poor children feel they do not receive enough attention from their parents, have to work and have no safe place to play.     

Distributions of the ratios of independent beta variables and applications

We establish the exact expressions of X₁/X₂ and of X₁/(X₁+X₂), where X₁ and X₂ are independent beta random variables of the first type, and provide some of their applications, in reliability and availability.     

Penalized spline joint models for longitudinal and time-to-event data

The joint models for longitudinal data and time-to-event data have recently received numerous attention in clinical and epidemiologic studies. Our interest is in modeling the relationship between event time outcomes and internal time-dependent covariates. In practice, the longitudinal responses often show non linear and fluctuated curves. Therefore, the main aim of this paper is to use penalized splines with a truncated polynomial basis to parameterize the non linear longitudinal process. Then, the linear mixed-effects model is applied to subject-specific curves and to control the smoothing. The association between the dropout process and longitudinal outcomes is modeled through a proportional hazard model. Two types of baseline risk functions are considered, namely a Gompertz distribution and a piecewise constant model. The resulting models are referred to as penalized spline joint models; an extension of the standard joint models. The expectation conditional maximization (ECM) algorithm is applied to estimate the parameters in the proposed models. To validate the proposed algorithm, extensive simulation studies were implemented followed by a case study. In summary, the penalized spline joint models provide a new approach for joint models that have improved the existing standard joint models.     

Bayesian decision criteria in the presence of noises under quadratic and absolute value loss functions

Summary Misclassifications, or noises, in the sampling stage of a Bayesian scheme can seriously affect the values of decision criteria such as the Bayes Risk and the Expected Value of Sample Information. This problem does not seem to be much addressed in the existing literature. In this article, using an approach based on hypergeometric functions and numerical computation, we study the effects of these noises under the two most important loss functions: the quadratic and the absolute value. A numerical example illustrates these effects in a representative case, using both loss functions, and provides additional insights into the general problem. Research partially supported by NSERC grant A 9249 (Canada) and FICU Grant 2000/pas/13. The authors wish to thank colleagues at the University of Alberta in Edmonton, Canada, for very stimulating discussions, and an anonymous referee for drawing their attention to three relevant references that have enriched the content of this final version.     

Improved estimators for the location of double exponential distribution

In this paper, attention is focused on estimation of the location parameter in the double exponential case using a weighted linear combination of the sample median and pairs of order statistics, with symmetric distance to both sides from the sample median. Minimizing with respect to weights and distances we get smaller asymptotic variance in the second order. If the number of pairs is taken as infinite and the distances as null we attain the least asymptotic variance in this class of estimators. The Pitman estimator is also noted. Similarly improved estimators are scanned over their probability of concentration to investigate its bound. Numerical comparison of the estimators is shown.     

Distribution of the Stopping Time in Bayesian Sequential Sampling

The paper presents a method for computing the distribution of the stopping time for the Bayesian sequential one step look-ahead procedure with the proportion as parameter.     

Techniques of Death: Buddhist Practice,Femininity and Self-Cultivation at the Last Stage of Life in Vietnam

This article explores how death is conceptualised by elderly lay Buddhist women in H? Chí Minh City (Vietnam). It explores the traits of a ‘good death’ which elderly laywomen wish to experience, and their dedicated practice of Buddhism to prepare themselves for a peaceful end stage of life. This article contends that, in fact, women’s perceptions of death speak to their desires to live a life with dignity and retain their full personhood and nurturing femininity which they have embodied throughout their adult lives even until their last moments. They pursue devotional practices to train their body and mind in order to prepare themselves for the critical moment of dying, believing that these self-cultivating practices will enable them to transcend physical suffering and mental confusion, and immediately move on to the next, better life.     

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.     

The type I distribution of the ratio of independent “Weibullized” generalized beta-prime variables

In this paper we introduce the distribution of , with c >  0, where X _i , i =  1, 2, are independent generalized beta-prime-distributed random variables, and establish a closed form expression of its density. This distribution has as its limiting case the generalized beta type I distribution recently introduced by Nadarajah and Kotz (2004). Due to the presence of several parameters the density can take a wide variety of shapes.