Robust Designs in Generalized Linear Models: A Quantile Dispersion Graphs Approach

This article studies design selection for generalized linear models (GLMs) using the quantile dispersion graphs (QDGs) approach in the presence of misspecification in the link and/or linear predictor. The uncertainty in the linear predictor is represented by a unknown function and estimated using kriging. For addressing misspecified link functions, a generalized family of link functions is used. Numerical examples are shown to illustrate the proposed methodology.     

A Risk-Based Method for Modeling Traffic Fatalities

We describe a risk-based analytical framework for estimating traffic fatalities that combines the probability of a crash and the probability of fatality in the event of a crash. As an illustrative application, we use the methodology to explore the role of vehicle mix and vehicle prevalence on long-run fatality trends for a range of transportation growth scenarios that may be relevant to developing societies. We assume crash rates between different road users are proportional to their roadway use and estimate case fatality ratios (CFRs) for the different vehicle-vehicle and vehicle-pedestrian combinations. We find that in the absence of road safety interventions, the historical trend of initially rising and then falling fatalities observed in industrialized nations occurred only if motorization was through car ownership. In all other cases studied (scenarios dominated by scooter use, bus use, and mixed use), traffic fatalities rose monotonically. Fatalities per vehicle had a falling trend similar to that observed in historical data from industrialized nations. Regional adaptations of the model validated with local data can be used to evaluate the impacts of transportation planning and safety interventions, such as helmets, seat belts, and enforcement of traffic laws, on traffic fatalities.     

Correlated random effects regression analysis for a log-normally distributed variable

In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.     

“A stitch in time”: group work during the covid-19 scare in the Nicobar Islands,India

ABSTRACT

When India suddenly went into its first coronavirus-induced nationwide lockdown in March 2020, several members of a historically isolated indigenous community in the eastern Indian ocean- the Nicobarese, began to panic. Their leaders came together and formed a group to boost community solidarity and helped the vulnerable indigenes to safely navigate through the crisis. In these uncertain times of Covid-19, when the “modern” world appears more fissured than ever, leaving its “others” to fend for themselves; the Nicobarese’s group has set an example for everyone to follow.     

Bayesian inference for stable Lévy–driven stochastic differential equations with high‐frequency data

In this paper, we consider parametric Bayesian inference for stochastic differential equations driven by a pure‐jump stable Lévy process, which is observed at high frequency. In most cases of practical interest, the likelihood function is not available; hence, we use a quasi‐likelihood and place an associated prior on the unknown parameters. It is shown under regularity conditions that there is a Bernstein–von Mises theorem associated to the posterior. We then develop a Markov chain Monte Carlo algorithm for Bayesian inference, and assisted with theoretical results, we show how to scale Metropolis–Hastings proposals when the frequency of the data grows, in order to prevent the acceptance ratio from going to zero in the large data limit. Our algorithm is presented on numerical examples that help verify our theoretical findings.     

Enrollment,Educational Expenditures and Work Among One-Parent Children in India

In this study, I examined the effect of single parenthood due to death, separation, divorce, or migration of spouse on educational inputs among school-age children in India. I used the nationally representative India Human Development Survey to study the implications of different types of parenthood on educational expenditure among children ages 6 to 11 and 12 to 18 years and work participation among the older age group. The study finds that the separated or divorced mothers on average spend less on education and older children of widows are more likely to work because of resource constraints associated with single parenthood. The results highlight the need for public policies that would support widows as well as separated or divorced mothers.     

Comparing Social Work Students’ Perceptions of Risk Using Online Discussion Fora: Lessons Learned from a European Pilot Project

Comparisons of international child welfare systems have identified two basic orientations to practice; a ‘child protection’ orientation and a ‘child welfare’ orientation, which are founded upon fundamentally different values and assumptions regarding the family, the origins of childcare problems, and the proper role of the state in relation to the family. This paper describes a project which sought to compare how undergraduate social work students from three European Universities perceive risk in referrals about the welfare of children and to explore the impact of different cultural, ideological and educational contexts on the way in which risk is constructed by students. Students from Northern Ireland, Germany and Poland examined three vignettes via 10 online discussion fora each of which provided a narrative summary of their discussion. The paper presents some findings from the analysis of the qualitative data emerging from the student discussions and draws out the lessons learned in terms of how the project was designed and implemented using online discussion fora.     

Optimum covariate designs in partially balanced incomplete block (PBIB) design set-ups

The use of covariates in block designs is necessary when the covariates cannot be controlled like the blocking factor in the experiment. In this paper, we consider the situation where there is some flexibility for selection in the values of the covariates. The choice of values of the covariates for a given block design attaining minimum variance for estimation of each of the parameters has attracted attention in recent times. Optimum covariate designs in simple set-ups such as completely randomised design (CRD), randomised block design (RBD) and some series of balanced incomplete block design (BIBD) have already been considered. In this paper, optimum covariate designs have been considered for the more complex set-ups of different partially balanced incomplete block (PBIB) designs, which are popular among practitioners. The optimum covariate designs depend much on the methods of construction of the basic PBIB designs. Different combinatorial arrangements and tools such as orthogonal arrays, Hadamard matrices and different kinds of products of matrices viz. Khatri–Rao product, Kronecker product have been conveniently used to construct optimum covariate designs with as many covariates as possible.