The Discrepancy Between Two Approaches to Global Poverty: What Does it Reveal?

Social Indicators Research - For decades, international communities have developed poverty measures to inform needs assessment and aid allocation. Building on these efforts, this paper examines the...     

Form Perception of Partly Occluded Shapes in 4‐Month‐Old Infants

Two habituation experiments were conducted to investigate how 4‐month‐old infants perceive partly occluded shapes. In the first experiment, we presented a simple, partly occluded shape to the infants until habituation was reached. Then we showed either a probable completion (one that would be predicted on the basis of both local and global cues) or an improbable completion. Longer looking times were found for the improbably completed shape (compared to probable and control conditions), suggesting that the probable shape was perceived during partial occlusion. In the second experiment, infants were habituated to more ambiguous partly occluded shapes, where local and global cues would result in different completions. For adults, the percept of these shapes is usually dominated by global influences. However, after habituation the infants looked longer at the globally completed shapes. These results suggest that by the age of 4 months, infants are able to infer the perceptual completion of partly occluded shapes, but for more ambiguous shapes, this completion seems to be dominated by local influences.     

Single-peaked Compatible Preference Profiles: Some Combinatorial Results

This paper examines combinatorial problems in connection with single-peaked preference orderings on a unidimensional scale. A binary relation, dominance, is defined on the set of connected orderings. Relevant properties of the corresponding poset and cover graph are discussed. A formula for the number of pairs of connected orderings consistent with spatial single-peakedness is derived. The total number of such pairs is shown to be expressible in a simple form involving binomial probabilities. A possible application is a coalition formation process of the kind examined by Brams et al. (J Theor Polit 14:359–383, 2002), where actors have single-peaked preferences on a common scale.     

Joint impact of multiple observations on a subset of variables in multiple linear regression

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE_ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F_j (Partial F-test for the jth variable) and F_j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE_IJ, F_J and F_J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.     

Religious Attendance,Stress, and Happiness in South Korea: Do Gender and Religious Affiliation Matter?

Is religious attendance positively associated with happiness in South Korea? If yes, can religious attendance buffer against the harmful effect of stress on happiness? Moreover, do gender and religious affiliation modify these associations? This study addresses these questions with data from 2009 Korean General Social Survey which is a nationally representative survey (N = 1,599). Ordinal least square regression analyses reveal that although the effect size is relatively small, religious attendance is associated with a higher level of happiness in South Korea. However, this positive effect holds only for women and only for Protestants. In addition, an interaction effect between religious attendance and stress is observed for women only; the negative association between stress and happiness is weakened among those women who report more frequent church attendance. In this regard, a high level of church attendance buffers against the deleterious effects of stress on happiness for women. I discuss the implications of the findings with regard to theories about religion, mental health, and gender in South Korean context.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

Mixtures of regression models with incomplete and noisy data

The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.