Correction to: Assessing recall bias and measurement error in high-frequency social data collection for human-environment research

Population and Environment - The original version of this article unfortunately contained a mistake. The name of “Md. Ehsanul Haque Tamal” is now corrected in the author group of this...     

The nexus between extreme weather events,sexual violence,and early marriage: a study of vulnerable populations in Bangladesh

Population and Environment - This study aims to explore whether a relationship exists between extreme weather events, sexual violence, and early marriage. We selected two districts in Bangladesh...     

Assessing recall bias and measurement error in high-frequency social data collection for human-environment research

Population and Environment - A major impediment to understanding human-environment interactions is that data on social systems are not collected in a way that is easily comparable to natural...     

Enrolling girls without learning: Evidence from public schools in Afghanistan

While more girls are now attending school in Afghanistan than prior to 2010, there is a lack of evidence on how school attendance is helping their cognitive development. We use data from a large sample of all‐girls state‐funded school students to estimate for the first time the “learning profile” for Afghanistan. Students enrolled in grades 4 to 9 were assessed using the Early Grade Reading Assessment (EGRA) and the Early Grade Mathematics Assessment (EGMA) test items. We find that higher grade progression (particularly in grades 6–9) leads to almost no gain in numeracy (addition and subtraction) skills that should have been achieved in early grades. Similar results follow from the analysis of student performance in the EGRA test, particularly in oral reading fluency (correct words read per minute) and oral reading comprehension (number of correct answers out of 12). These findings warn that simply enrolling girls in school without improving the relationship between grade completion and learning is unlikely to transform the lives of women in Afghanistan.     

A novel weighted likelihood estimation with empirical Bayes flavor

We propose a novel approach to estimation, where a set of estimators of a parameter is combined into a weighted average to produce the final estimator. The weights are chosen to be proportional to the likelihood evaluated at the estimators. We investigate the method for a set of estimators obtained by using the maximum likelihood principle applied to each individual observation. The method can be viewed as a Bayesian approach with a data-driven prior distribution. We provide several examples illustrating the new method and argue for its consistency, asymptotic normality, and efficiency. We also conduct simulation studies to assess the performance of the estimators. This straightforward methodology produces consistent estimators comparable with those obtained by the maximum likelihood method. The method also approximates the distribution of the estimator through the “posterior” distribution.     

A class of distributions connected to order statistics with nonintegral sample size

Newton's binomial series expansion is used to develop a (class of) distribution function(s) F_r:∝ which may be interpreted as the distribution of the r^thorder statistic with nonintegral sample size∝. It is shown that F_r:∝ has properties similar to F_r:n. Multivariate extension is considered and an elementary proof of the integral representation for the joint distribution of a subset of order statistics is given. An application is included.     

Estimation of Slope for Linear Regression Model with Uncertain Prior Information and Student-t Error

This article considers estimation of the slope parameter of the linear regression model with Student-t errors in the presence of uncertain prior information on the value of the unknown slope. Incorporating uncertain non sample prior information with the sample data the unrestricted, restricted, preliminary test, and shrinkage estimators are defined. The performances of the estimators are compared based on the criteria of unbiasedness and mean squared errors. Both analytical and graphical methods are explored. Although none of the estimators is uniformly superior to the others, if the non sample information is close to its true value, the shrinkage estimator over performs the rest of the estimators.     

Comments on ‘Application of an adaptive design to a randomized phase II selection trial in gastric cancer: a report of the study design’ by Satoshi Morita and Junichi Sakamoto. Pharmaceutical Statistics

In this commentary, we show that the treatment selection probabilities in Morita and Sakamoto [1] could be different if safety information is considered. Copyright © 2012 John Wiley & Sons, Ltd.     

Estimating quantiles using optimally selected order statistics

This expository paper deals with the linear estimation of quantiles of location-scale families of distributions using a few selected order statistics.The general theory for the problem i s reviewed for the exact as well as the asymptotic cases.     

On shrinkage r-estimation in a multiple regression model

For a multiple regression model, bearing the plausibility of a subset of the regression parameters being close to a pivot, for the complementary subset, based on the usual James-Stein rule, a general formulation of shrinkage R-estimation is considered. In the light of asymptotic distributional risks of estimators, performance characteristics ( under local alternatives) of the classical R-est-imator and its preliminary test and shrinkage versions (all based on the common score function ) are studied. These shed light on the relative dominance picture in a meaningful asymptotic setup.