Individual Disaster Assistance For Socially Vulnerable People: Lessons Learned From the Pohang Earthquake in the Republic of Korea

The Republic of Korea has been considered to be relatively safe from earthquake hazards because of the geological location of the Korean Peninsula, which has a low level of intraplate seismic activity. However, an earthquake with a moment magnitude of 5.4 struck the city of Pohang on November 15, 2017, causing 90 casualties and 52 million USD in property losses. During the recovery process after the earthquake, the Korean government provided individual disaster assistance to victims who reported their damages. However, the government disaster assistance could have been unfairly distributed among the socially vulnerable victims who essentially relied on that assistance. This study identifies whether the government disaster assistance was fairly distributed to socially vulnerable victims using a statistical model based on the data from the Pohang earthquake that occurred in 2017 in Korea. A conceptual model was constructed using a structural equation model (SEM) of three factors—social vulnerability, physical vulnerability, and the amount paid out in individual disaster assistance. Furthermore, interviews with and a survey of the victims were conducted to verify the problems identified by the conceptual model. This study found that socially vulnerable victims were less likely to take advantage of the government disaster assistance program.     

Marginal information for structure learning

Structure learning for Bayesian networks has been made in a heuristic mode in search of an optimal model to avoid an explosive computational burden. In the learning process, a structural error which occurred at a point of learning may deteriorate its subsequent learning. We proposed a remedial approach to this error-for-error process by using marginal model structures. The remedy is made by fixing local errors in structure in reference to the marginal structures. In this sense, we call the remedy a marginally corrective procedure. We devised a new score function for the procedure which consists of two components, the likelihood function of a model and a discrepancy measure in marginal structures. The proposed method compares favourably with a couple of the most popular algorithms as shown in experiments with benchmark data sets.     

PARAMETRIC FRACTIONAL IMPUTATION FOR NON‐IGNORABLE CATEGORICAL MISSING DATA WITH FOLLOW‐UP

Incomplete data subject to non‐ignorable non‐response are often encountered in practice and have a non‐identifiability problem. A follow‐up sample is randomly selected from the set of non‐respondents to avoid the non‐identifiability problem and get complete responses. Glynn, Laird, & Rubin analyzed non‐ignorable missing data with a follow‐up sample under a pattern mixture model. In this article, maximum likelihood estimation of parameters of the categorical missing data is considered with a follow‐up sample under a selection model. To estimate the parameters with non‐ignorable missing data, the EM algorithm with weighting, proposed by Ibrahim, is used. That is, in the E‐step, the weighted mean is calculated using the fractional weights for imputed data. Variances are estimated using the approximated jacknife method. Simulation results are presented to compare the proposed method with previously presented methods.     

Diagnostic checks for integer-valued autoregressive models using expected residuals

Integer-valued time series models make use of thinning operators for coherency in the nature of count data. However, the thinning operators make residuals unobservable and are the main difficulty in developing diagnostic tools for autocorrelated count data. In this regard, we introduce a new residual, which takes the form of predictive distribution functions, to assess probabilistic forecasts, and this new residual is supplemented by a modified usual residuals. Under integer-valued autoregressive (INAR) models, the properties of these two residuals are investigated and used to evaluate the predictive performance and model adequacy of the INAR models. We compare our residuals with the existing residuals through simulation studies and apply our method to select an appropriate INAR model for an over-dispersed real data.     

Stages of sexual readiness and six-month stage progression among African American pre-teens

We examined the range of sexual intentions and behaviors preceding sexual initiation among 211 African-American pre-teens assigned to the control arm of a longitudinal community-based intervention trial. Stage of sexual readiness was assessed using the stage of change construct from the Transtheoretical Model, and patterns of stage movement during a 6-month period were examined. Overall, 90% of participants were in precontemplation at baseline, with the proportion of participants in this stage declining with each year of age. There was substantial stability in stage of sexual readiness during the 6-month period (87% stable). While definitive conclusions regarding exact patterns of movement are not yet possible, stage movement does not appear to be linear for all pre-teens, and there is evidence of both stage progression and regression. We present emerging patterns of stage movement, which suggest potential variation by age, gender, and baseline stage, and discuss potential implications.     

Hardness of <Emphasis Type="Italic">k</Emphasis>-Vertex-Connected Subgraph Augmentation Problem

Given a k-connected graph G=(V,E) and V ^′⊂V, k-Vertex-Connected Subgraph Augmentation Problem (k-VCSAP) is to find S⊂V∖V ^′ with minimum cardinality such that the subgraph induced by V ^′∪S is k-connected. In this paper, we study the hardness of k-VCSAP in undirect graphs. We first prove k-VCSAP is APX-hard. Then, we improve the lower bound in two ways by relying on different assumptions. That is, we prove no algorithm for k-VCSAP has a PR better than O(log (log n)) unless P=NP and O(log n) unless NP⊆DTIME(n ^{O(log log n)}), where n is the size of an input graph.     

Financial Capability,Asset Ownership,and Later-Age Immigration: Evidence From a Sample of Low-Income Older Asian Immigrants

We examined financial capability and asset ownership among low-income older Asian immigrants with special attention given to later-age immigrants who came to the United States when they were 55 years old or older. Survey data collected from supported employment program participants (N = 150) were used. The analyses demonstrated a low level of financial knowledge and asset ownership in the sample. The findings also indicated that later-age immigrants’ financial-management skills, knowledge of social programs, and asset ownership were significantly lower than those of young-age immigrants. These findings call for active interventions to enhance economic security among low-income older Asian immigrants.     

Parametric estimation of association in bivariate failure-time data subject to competing risks: sensitivity to underlying assumptions

Lifetime Data Analysis - There has arisen a considerable body of research addressing the estimation of association between paired failure times in the presence of competing risks. In a 2002 paper,...     

Nonparametric estimation of varying-coefficient single-index models

The varying-coefficient single-index model has two distinguishing features: partially linear varying-coefficient functions and a single-index structure. This paper proposes a nonparametric method based on smoothing splines for estimating varying-coefficient functions and an unknown link function. Moreover, the average derivative estimation method is applied to obtain the single-index parameter estimates. For interval inference, Bayesian confidence intervals were obtained based on Bayes models for varying-coefficient functions and the link function. The performance of the proposed method is examined both through simulations and by applying it to Boston housing data.