Estimation of noncentrality parameters

We propose some estimators of noncentrality parameters which improve upon usual unbiased estimators under quadratic loss. The distributions we consider are the noncentral chi-square and the noncentral F. However, we give more general results for the family of elliptically contoured distributions and propose a robust dominating estimator.     

Citizens’ Initiatives for Crisis Management and the Use of Social Media: An Analysis of COVID-19 Crisis in Bangladesh

Public Organization Review - This study analyses different social media-based citizens’ initiatives taken during the COVID-19 crisis in Bangladesh. By applying netnography on various Facebook...     

Women’s Tertiary Education Masks the Gender Wage Gap in Turkey

This paper investigates the gender wage gap for full-time formal sector employees, disaggregated by education level. The gap between the labor force participation rate of women with tertiary education and those with lower levels of education is substantial. There is no such gap for men. Hence, existing gender wage gap studies for Turkey, where we observe lopsided labor force participation rates by education levels, compare two very different populations. We disaggregate the whole sample by education level to create more homogenous sub-groups. For Turkey, without disaggregation, the gender wage gap was 13% in 2011, and women are significantly over-qualified relative to men on observed characteristics. Once we disaggregate the sample by education level, we show that the gender wage gap is 24% for less educated women and 9% for women with tertiary education in full-time formal employment. Observed characteristics only explain 1 % of this gap in absolute terms. We further disaggregate the data by public and private employment. The gender gap is higher in the private sector. However, women with tertiary education in the public sector are significantly better qualified compared to men, and consequently the adjusted gender wage gap is higher for women with tertiary education in the public sector. Our estimates also indicate a rise in the gender wage gap between 2004 and 2011.     

Health Disorder of Climate Migrants in Khulna City: An Urban Slum Perspective

As the most vulnerable climatic region in the country, Khulna City is increasingly experiencing climate‐change‐induced urban problems. For instance, occupancy by climate migrants (delete the term “illegal migrant”), drainage congestion, water logging and reduced fresh water availability are all increasing problems. In the last decade, the population in the city increased by more than 20 per cent due to migration from nearby climate vulnerable districts. This study explores the health disorders of climate migrants occupying the urban slums and squats of the Khulna City area. This study found that these climate migrants settled in the urban slums and squats and, as such, they do not have access to urban amenities such as clean drinking water, hygiene services, and health facilities. This study noted that these displaced people are at increased risk of health issues from unhygienic and overcrowded living conditions and from water and sanitation problems. They often suffer from different waterborne diseases, under‐nutrition and micronutrient deficiencies. This study suggests that there is a need for better planning, preparation and training in Bangladesh to which migrants move, including better training in health and related services on how to recognize and respond to health problems that may be slow to manifest. Education and training also need to be provided for the migrants themselves, to help them adapt culturally and to enhance their skills and potential for employment.     

Effect of a new selection procedure on adaptive estimation

Adaptive estimation of parameters of some failure time distributionsis considered. A new procedure named the F-procedure has beendeveloped for selecting an appropriate model out of two possible models by Pandey et.al. (1991). Applying this F-procedure adaptive estimatorsof parameters of exponential, Wei bull, inverse Gaussian (IG) and Wald failure time distributions have been proposed in this paper. Comparison of these estimators has been undertaken with MLE's of the respective parameters and with some previous adaptiveestimators by simulation of samples using the Monte Carlo method.Adaptive estimation of parameters of some failure time distributions is considered. A new procedure named the F-procedure has been developedfor selecting an appropriate model out of two possible models by Pandey et.al. (1991). Applying this F-procedure adaptive estimators of parameters of exponential, Wei bull, inverse Gaussian (IG) and Wald failure time distributions have been proposed in this paper. Comparison of these estimators has been undertaken with MLE's of the respective parameters and with some previous adaptive estimators by simulation of samples using the Monte Carlo method.     

Comparative Study of Generalized Estimating Equations and Logistic Regressions on Different Sample Sizes and Correlation Levels

In this study, it was aimed to determine accuracy of generalized estimating equations versus logistic regressions on different correlation levels and sample sizes. For this aim, two methods were compared with different sample sizes 10, 25, 50 and 100 and correlation levels 0.0, 0.3, 0.5 and 0.8. Result of this study showed that using generalized estimating equations could be preferred versus logistic regression when the sample size is over than 25 and correlation level is higher than 0.3 on data taken from studies with repeated measurements, but logistic regression could be better when the autocorrelations do not exist.     

Variable selection for survival data with a class of adaptive elastic net techniques

The accelerated failure time (AFT) models have proved useful in many contexts, though heavy censoring (as for example in cancer survival) and high dimensionality (as for example in microarray data) cause difficulties for model fitting and model selection. We propose new approaches to variable selection for censored data, based on AFT models optimized using regularized weighted least squares. The regularized technique uses a mixture of \(\ell _1\) and \(\ell _2\) norm penalties under two proposed elastic net type approaches. One is the adaptive elastic net and the other is weighted elastic net. The approaches extend the original approaches proposed by Ghosh (Adaptive elastic net: an improvement of elastic net to achieve oracle properties, Technical Reports 2007) and Hong and Zhang (Math Model Nat Phenom 5(3):115–133 2010), respectively. We also extend the two proposed approaches by adding censoring observations as constraints into their model optimization frameworks. The approaches are evaluated on microarray and by simulation. We compare the performance of these approaches with six other variable selection techniques-three are generally used for censored data and the other three are correlation-based greedy methods used for high-dimensional data.     

The nested joint clustering via Dirichlet process mixture model

This article focuses on the clustering problem based on Dirichlet process (DP) mixtures. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model is flexible in that both the common and unique patterns are taken into account simultaneously. Furthermore, by jointly clustering subjects and the associated variables, the intrinsic complex shared patterns among subjects and among variables are expected to be captured. The number of clusters and cluster assignments are directly inferred with the use of DP. Simulation studies illustrate the effectiveness of the proposed method. An application to wheal size data is discussed with an aim of identifying novel temporal patterns among allergens within subject clusters.     

Modified profile likelihood estimation for the Weibull regression models in survival analysis

In this study, adjustment of profile likelihood function of parameter of interest in presence of many nuisance parameters is investigated for survival regression models. Our objective is to extend the Barndorff–Nielsen’s technique to Weibull regression models for estimation of shape parameter in presence of many nuisance and regression parameters. We conducted Monte-Carlo simulation studies and a real data analysis, all of which demonstrate and suggest that the modified profile likelihood estimators outperform the profile likelihood estimators in terms of three comparison criterion: mean squared errors, bias and standard errors.