A simple approach to analyzing clustered longitudinal data

When modeling correlated binary data in the presence of informative cluster sizes, generalized estimating equations with either resampling or inverse-weighting, are often used to correct for estimation bias. However, existing methods for the clustered longitudinal setting assume constant cluster sizes over time. We present a subject-weighted generalized estimating equations scheme that provides valid parameter estimation for the clustered longitudinal setting while allowing cluster sizes to change over time. We compare, via simulation, the performance of existing methods to our subject-weighted approach. The subject-weighted approach was the only method that showed negligible bias, with excellent coverage, for all model parameters.     

On estimation of population mean based on two measurements

There are many estimators for population mean: arithmetic mean, median, root mean square, geometric mean, harmonic mean, and so forth. Which one is the best? Here, we answer this question given two measurements from a uniform distribution.     

How to Reduce the Impact of Equivalence Scales on Poverty Measurement: Evidence from Turkey

The main aim of the paper is to contribute to the poverty measurement literature by demonstrating a method to reduce the impact of equivalence scales in poverty measurement. This is accomplished by choosing the most appropriate reference household type. The results showed that one adult household is certainly not suitable for being the reference household type. When one adult household is set as the reference, in the range of no equivalence scale and per capita equivalence scale, poverty head count ratio changes from 1 to 48 %, showing the huge effect of the equivalence scale choice. Also the analyses at household size level showed that one adult household type is not convenient to be the reference household type. On the other hand, no clear distinction could be made between central household types, but the importance of choosing a household type close to the center was demonstrated for Turkish data.     

Microfinance as a Development and Poverty Alleviation Tool in Rural Bangladesh: A Critical Assessment

Poverty is multidimensional in nature and exists in every part of the world. Microfinance is considered one of the most important programs to achieve poverty reduction, particularly in Bangladesh. It has been estimated that nearly 45% of Bangladesh's population lives below the poverty line. This qualitative research study was conducted in order to determine the effectiveness of microfinance programs and the contributing factors to the high‐level of poverty among microfinance beneficiaries in the district of Bogra, Bangladesh. Study findings indicated that microfinance programs were ineffective as a result of high interest rates; insufficient loans; unproductive use of loans; corruption and poor skills of microfinance institution staff; weekly repayment schedules; and physical and mental harassment of poor women. Additionally, the lack of employment opportunities, education, healthcare facilities and social safety nets; natural disasters; the dowry system; and the rising cost of basic daily needs have also contributed to chronic poverty.     

Doubly sparse regression incorporating graphical structure among predictors

Recent research has demonstrated that information learned from building a graphical model on the predictor set of a regularized linear regression model can be leveraged to improve prediction of a continuous outcome. In this article, we present a new model that encourages sparsity at both the level of the regression coefficients and the level of individual contributions in a decomposed representation. This model provides parameter estimates with a finite sample error bound and exhibits robustness to errors in the input graph structure. Through a simulation study and the analysis of two real data sets, we demonstrate that our model provides a predictive benefit when compared to previously proposed models. Furthermore, it is a highly flexible model that provides a unified framework for the fitting of many commonly used regularized regression models. The Canadian Journal of Statistics 47: 729–747; 2019 © 2019 Statistical Society of Canada     

Construction of circular partially balanced-Repeated measurement designs using cyclic shifts

Repeated measurement designs are widely used in medicine, pharmacology, animal sciences and psychology. These designs balance out the residual effects. The situations where balanced repeated measurements designs require a large number of the subjects, partially-balanced repeated measurements designs should be used. In this paper some infinite series are developed which provide circular partially-balanced repeated measurement designs for p (periods) even. Catalogues of circular partially-balanced repeated measurement designs are also presented for v (treatments) ≤ 100 with p = 5, 7 & 9.     

A new lifetime model with regression models,characterizations and applications

In this article, we introduce a new extension of Burr XII distribution called Topp Leone Generated Burr XII distribution. We derive some of its properties. Useful characterizations are presented. Simulation study is performed to assess the performance of the maximum likelihood estimators. Censored maximum likelihood estimation is presented in the general case of multi-censored data. The new location-scale regression model based on the proposed distribution is introduced. The usefulness of the proposed models is illustrated empirically by means of three real datasets.     

DISTRIBUTION OF THE LEAST SQUARES ESTIMATOR IN A FIRST-ORDER AUTOREGRESSIVE MODEL

This paper investigates the finite sample distribution of the least squares estimator of the autoregressive parameter in a first-order autoregressive model. A uniform asymptotic expansion for the distribution applicable to both stationary and nonstationary cases is obtained. Accuracy of the approximation to the distribution by a first few terms of this expansion is then investigated. It is found that the leading term of this expansion approximates well the distribution. The approximation is, in almost all cases, accurate to the second decimal place throughout the distribution. In the literature, there exist a number of approximations to this distribution which are specifically designed to apply in some special cases of this model. The present approximation compares favorably with those approximations and in fact, its accuracy is, with almost no exception, as good as or better than these other approximations. Convenience of numerical computations seems also to favor the present approximations over the others. An application of the finding is illustrated with examples.     

Disadvantage and Discrimination Compounded: the experience of Pakistani and Bangladeshi parents of disabled children in the UK

This paper discusses a qualitative and quantitative study of the circumstances of 20 Pakistani and Bangladeshi families with one or more severely disabled children living in Birmingham, England. Parents and other adult carers were interviewed using a combination of structured questionnaires and a semi-structured interview schedule focusing on the families' material circumstances, their use of formal services, informal care arrangements, and aspects of the parents' social and psychological well-being. The study suggests that previous national surveys of disabled families may have under-estimated the extent of material disadvantage, while it confirms that health and social care professionals should not assume that Pakistani and Bangladeshi parents have recourse to high levels of extended family support. The combination of disadvantaged circumstances and difficulties in securing access to appropriate services, which are found for the majority of families with a disabled child, was particularly acute for these ethnic minority families, suggesting the additional dimension of institutional racism.