Testing the difference between spectral densities of two independent periodically correlated (cyclostationary) time series models

In this work, the asymptotic distribution for the discrete Fourier transform of periodically correlated (PC) processes is applied to test the equality of two PC time series. Then the performance of the proposed method is investigated through the Monte Carlo simulations.     

Spatial smoothing of low birth weight rate in Bangladesh using Bayesian hierarchical model

The term low birth weight refers an event where a newborn baby has a weight that is less than 2500?g. This is an essential indicator while the interest is in public health issues such as infant mortality, maternal complications, and antenatal care, etc. of a country, particularly, for a developing country like Bangladesh. The regional development programs are in the current priority list of Bangladesh government and other policy makers. Many of such regional development programs may need the spatial distribution of relative risk for low birth weight that can be obtained by mapping the risks over small area domains like the districts of Bangladesh. This study aims to find whether is there any spatial dependence among the relative risks of low birth weight for the districts of Bangladesh. This has been investigated using Moran's I statistic and a significant spatial dependence in the relative risks was found. Then, attempt has been made to rediscover the spatial distribution based on the idea of spatial smoothing. A Bayesian hierarchical model is used considering percent received antenatal care and female labor force participation as covariates to smooth the observed relative risks of low birth weight in 64 districts of Bangladesh. Revised spatial distribution taking the spatial dependence under consideration through intrinsic conditional autoregressive model is derived and showed in choropleth map along with its different behaviors.     

A mixed-effects least square support vector regression model for three-level count data

Hierarchical study design often occurs in many areas such as epidemiology, psychology, sociology, public health, engineering, and agriculture. This imposes correlation in data structure that needs to be account for in modelling process. In this study, a three-level mixed-effects least squares support vector regression (MLS-SVR) model is proposed to extend the standard least squares support vector regression (LS-SVR) model for handling cluster correlated data. The MLS-SVR model incorporates multiple random effects which allow handling unequal number of observations for each case at non-fixed time points (a very unbalanced situation) and correlation between subjects simultaneously. The methodology consists of a regression modelling step that is performed straightforwardly by solving a linear system. The proposed model is illustrated through numerical studies on simulated data sets and a real data example on human Brucellosis frequency. The generalization performance of the proposed MLS-SVR is evaluated by comparing to ordinary LS-SVR and some other parametric models.     

Comparing the transpirational and shading effects of two contrasting urban tree species

Urban Ecosystems - Urban trees are getting increasing attention as a tool to mitigate urban heat island effects. A more functional and quantitative view of transpirational and shading effect,...     

Key considerations for choosing a statistical method to deal with incomplete treatment adherence in pragmatic trials

Pragmatic trials offer practical means of obtaining real-world evidence to help improve decision-making in comparative effectiveness settings. Unfortunately, incomplete adherence is a common problem in pragmatic trials. The commonly used methods in randomized control trials often cannot handle the added complexity imposed by incomplete adherence, resulting in biased estimates. Several naive methods and advanced causal inference methods (e.g., inverse probability weighting and instrumental variable-based approaches) have been used in the literature to deal with incomplete adherence. Practitioners and applied researchers are often confused about which method to consider under a given setting. This current work is aimed to review commonly used statistical methods to deal with non-adherence along with their key assumptions, advantages, and limitations, with a particular focus on pragmatic trials. We have listed the applicable settings for these methods and provided a summary of available software. All methods were applied to two hypothetical datasets to demonstrate how these methods perform in a given scenario, along with the R codes. The key considerations include the type of intervention strategy (point treatment settings, where treatment is administered only once versus sustained treatment settings, where treatment has to be continued over time) and availability of data (e.g., the extent of measured or unmeasured covariates that are associated with adherence, dependent confounding impacted by past treatment, and potential violation of assumptions). This study will guide practitioners and applied researchers to use the appropriate statistical method to address incomplete adherence in pragmatic trial settings for both the point and sustained treatment strategies.     

On a generalization of the test of endogeneity in a two stage least squares estimation

In situations that the predictors are correlated with the error term, we propose a bridge estimator in the two-stage least squares estimation. We apply this estimator to overcome the multicollinearity and sparsity of the explanatory variables, when the endogeneity problem is present.The proposed estimator was applied to modify the Durbin-Wu-Hausman (DWH) test of endogeneity in the presence of multicollinearity. To compare our modified test with the existing DWH for detection of an endogenous problem in multi-collinear data, some numerical assessments are carried out. The numerical results showed that the proposed estimators and the suggested test perform better for the multi-collinear data. Finally, a genetical data set is applied for illustration the our results by estimating the coefficients parameters in the presence of endogeneity and multicollinearity.     

Deaf women: educational experiences and self-identity

Using life history interviews with 10 college educated Deaf¹ women this paper investigates connections between early education and college experience and how they identified as Deaf. The women developed strategies as they managed their impressions while employing Goffman's practices of loyalty, discipline and circumspection. Acknowledging deafness and their own decisions about education affected their identities. The women experienced a cultural shift after attending a college for the deaf or after their exposure to the Deaf community and learning American Sign Language. The women developed strategies of becoming 'lifetime educators' and 'self-advocates'. Their experiences show the role of language in the identity making process and how the women navigated this in their schools.     

Factors related to the intention to have additional children in the United States: A reanalysis of data from the 1965 and 1970 national fertility studies

Reproductive intentions of white mothers with no more than three children in 1965 and in 1970 were analyzed for their stability and change with respect to such factors as parity, age of the youngest child, wife’s employment, and husband’s education. Parity and age of the youngest child were found to have a much more important effect on the intent to have additional children than were such socioeconomic variables as wife’s employment and husband’s education. Parity and the interval since the latest birth (or the age of the youngest child) were found to have a nonlinear effect and to interact in affecting the reproductive intention. This suggests a convergence to a twochild family as the threshold size and that the length of childspacing is contingent on the parity. Change during 1965–1970 in this direction was found to be greater among mothers with husbands having college or higher educations.     

The Emerging Perspective of Governance and Poverty Alleviation: A Case of Bangladesh

The majority of the rural people in the developing world are subject to severe poverty, inequality and unemployment. Over the years, a number of institutional approaches have been experimented to eradicate poverty from society. Despite the historical role accorded to the state, the states in many developing countries have not been quite effective in poverty alleviation. Following the global trends many developing country states have created space for the market forces and civil society organizations to contribute to poverty alleviation. This is what is popularly known as the new perspective of governance. This article intends to analyze the new perspective of governance and its role in poverty alleviation with empirical evidence from Bangladesh.