An analytical study of the classification of highly skewed data

This article proposes a discriminant function and an algorithm to analyze the data addressing the situation, where the data are positively skewed. The performance of the suggested algorithm based on the suggested discriminant function (LNDF) has been compared with the conventional linear discriminant function (LDF) and quadratic discriminant function (QDF) as well as with the nonparametric support vector machine (SVM) and the Random Forests (RFs) classifiers, using real and simulated datasets. A maximum reduction of approximately 81% in the error rates as compared to QDF for ten-variate data was noted. The overall results are indicative of better performance of the proposed discriminant function under certain circumstances.     

Adaptive c-chart with estimated parameter

In the design of control charts, it is usually assumed that process parameters are known. However, in many practical applications the values of these parameters are unknown and should be estimated using historical in-control process observations. In this study, the performance of adaptive c-chart with estimated parameter is evaluated. It is demonstrated that by increasing the size and the number of samples in estimating the process parameter, the performance of the chart converges to that of the known parameter case. Finally the best phase I sampling scenarios are presented to make the chart with the estimated parameter perform as well as the chart with the known parameter.     

A new perspective in functional EIV linear model: Part I

Simple linear regression in the functional errors-in-variables (EIV) model is revisited from a different perspective, where the problem is addressed by using the small-sigma model instead of large sample theory. A general analysis is developed to study the slope’s estimator that minimizes a family of objective functions, of which the least-squares fit and the maximum likelihood estimator are minimizers of such special functions. General formulas for the higher-order terms of the bias, the variance, and the mean square error are derived. Accordingly, two efficient estimators are proposed after implementing the pre- and the post-bias elimination techniques. Numerical tests confirm the superiority of the proposed estimators over others.     

Health Interventions and Health Equity: The Example of Measles Vaccination in Bangladesh

Although the existence of socioeconomic differentials in infant and childhood mortality in developing countries is well established. little consensus exists as to the most effective approaches to reducing such differentials. This article utilizes longitudinal data from the Matlab study area in rural Bangladesh to investigate the impact of an efficacious child survival intervention—measles vaccination—on reductions in gender and socioeconomic differentials in childhood mortality. The article analyzes data from 16,270 vaccinated children and randomly matched controls, and evaluates their subsequent mortality risks. Proportional hazards analysis demonstrates that unvaccinated children from very poor families face more than a threefold higher risk of subsequent early child mortality, compared to vaccinated children from families of high economic status. While measles vaccination has little impact on mortality risks among children of higher economic status, the improvement in survival among children from poorer households is pronounced. The provision of measles vaccination markedly reduces mortality risks for poorer children—from over three times higher to just over 1.5 times higher relative to vaccinated children from wealthier families. The findings of this study are evaluated in terms of the potential of child survival interventions such as measles vaccination to promote greater health equity.     

Political Democracy and the Physical Quality of Life: The Cross-National Evidence

Why do some countries have a high quality of life? One important explanation is that political democracy fosters life quality. Proponents of this perspective argue that political democracy acts to center the public agenda on state actions that enhance life quality. The cross-national effects of democracy (and several alternative variables) on the physical quality of life were examined for 87 countries at three points in time: 1970, 1980, and 1990. Results indicate the existence of a strong positive relationship between democracy and life quality for all three time periods.

     

Simple graphs and bounds for the elements of the hat matrix

In regression analysis, the matrix H = X (XTX)-1XT is known as the 'hat' or 'projection' matrix, among other names. It has been studied by many authors from different perspectives. The main area of study has been the type of measure best adapted to detect leverage points in linear regression. For computational reasons, these measures were originally based on the diagonal elements of the hat matrix. In the present paper, we propose a very simple procedure for identifying leverage groups. The procedure is based on upper and lower bounds for the diagonal and the off-diagonal elements of H. These upper and lower bounds can easily be shown on an index plot of the elements of H.     

Professional and Functional Alternative Social Workers: A Case Study of Malaysia

The objective of this study was to compare “professional” (PW) and “functional alternative” (FA) social workers. The findings suggested that there were no significant differences between PWs and FAs in intervention, evaluation methods, work ethics, and values. The PWs, in clients' eyes, were better at delivering services, while FAs' services were more satisfactory to the clients. While PWs were supposed to be “professional,” FAs were more “professional” than PWs in their performance despite their lack of recognition as “professional social workers”. These critical findings should encourage academics and practitioners for further discussion on conceptual implications of both practices and expand future research.     

Modelling functional additive quantile regression using support vector machines approach

This work deals with conditional quantiles estimation when several functional covariates are involved, via a support vector machines nonparametric methodology. We establish weak consistency of this estimator. To fit the additive components, we use an ordinary backfitting procedure combined with an iterative reweighted least-squares procedure to solve the penalised minimisation problem. This procedure makes it possible to derive a split sample method for choosing the hyper-parameters of the model. The performances of the proposed technique, in terms of forecast accuracy, are evaluated through simulation and a real dataset study.     

On computing standard errors for marginal structural Cox models

In recent decades, marginal structural models have gained popularity for proper adjustment of time-dependent confounders in longitudinal studies through time-dependent weighting. When the marginal model is a Cox model, using current standard statistical software packages was thought to be problematic because they were not developed to compute standard errors in the presence of time-dependent weights. We address this practical modelling issue by extending the standard calculations for Cox models with case weights to time-dependent weights and show that the coxph procedure in R can readily compute asymptotic robust standard errors. Through a simulation study, we show that the robust standard errors are rather conservative, though corresponding confidence intervals have good coverage. A second contribution of this paper is to introduce a Cox score bootstrap procedure to compute the standard errors. We show that this method is efficient and tends to outperform the non-parametric bootstrap in small samples.