Moving up the energy ladder: does socio-religious status matter?

This paper investigates the role played by socio-religious categories in determining primary cooking fuel choices among Indian households. We study this role in the broader context of climbing up the energy ladder. Our estimates based on a sample of 601,509 households and using multinomial probit regression suggest that socio-religious status along with economic status is critical in the choice of modern eco-friendly fuels. We find that belonging to a marginalized community in Hindu religion significantly dampens a households’ likelihood to move up the ladder when compared with upper caste households. While intra-religion differences among Hindu castes in terms of their probability of using modern fuels are wider, differences among Muslims appear smaller. Also, though Muslims perform worse than Hindu upper castes in terms of probability of using modern fuels they are much better off in comparison with other Hindu castes. Our results remain robust to alternative specifications and several robustness checks.

     

Application of digital human modeling and simulation for vision analysis of pilots in a jet aircraft: a case study

Ergonomic evaluation of visual demands becomes crucial for the operators/users when rapid decision making is needed under extreme time constraint like navigation task of jet aircraft. Research reported here comprises ergonomic evaluation of pilot's vision in a jet aircraft in virtual environment to demonstrate how vision analysis tools of digital human modeling software can be used effectively for such study. Three (03) dynamic digital pilot models, representative of smallest, average and largest Indian pilot population were generated from anthropometric database and interfaced with digital prototype of the cockpit in Jack software for analysis of vision within and outside the cockpit. Vision analysis tools like view cones, eye view windows, blind spot area, obscuration zone, reflection zone etc. were employed during evaluation of visual fields. Vision analysis tool was also used for studying kinematic changes of pilot's body joints during simulated gazing activity. From present study, it can be concluded that vision analysis tool of digital human modeling software was found very effective in evaluation of position and alignment of different displays and controls in the workstation based upon their priorities within the visual fields and anthropometry of the targeted users, long before the development of its physical prototype.     

A systematic literature review of the etiology of borderline personality disorder from an ecological systems PERSPECTIVE

Borderline Personality Disorder (BPD) is one of the most common, complex, costly, and severely impairing personality disorders, affecting an estimated 2% to 9% of the general population and 40% to 44% of the inpatient psychiatric population. A review of the literature was conducted using a systematic methodology. By incorporating an ecological systems perspective, a holistic and comprehensive critique of the literature surrounding the etiology of BPD is presented. The findings reveal that the etiology of BPD is a complex integration of psychological, biological, and social factors. More specifically, however, this review found that: (1) the etiology of BPD is complex and has many factors, (2) the dominant discourse about the etiology of BPD is based primarily in the psychological and biological literature, and (3) the examination of BPD etiology has focused solely on the individual and microsystems levels, neglecting to consider systemic factors such as the impact of discriminatory health and mental healthcare practices. Findings and future directions are explored through the ecological systems theory lens.     

Rao and Wu's re-scaling bootstrap modified to achieve extended coverages

Horvitz and Thompson's (HT) [1952. A generalization of sampling without replacement from a finite universe. J. Amer. Statist. Assoc. 47, 663–685] well-known unbiased estimator for a finite population total admits an unbiased estimator for its variance as given by [Yates and Grundy, 1953. Selection without replacement from within strata with probability proportional to size. J. Roy. Statist. Soc. B 15, 253–261], provided the parent sampling design involves a constant number of distinct units in every sample to be chosen. If the design, in addition, ensures uniform non-negativity of this variance estimator, Rao and Wu [1988. Resampling inference with complex survey data. J. Amer. Statist. Assoc. 83, 231–241] have given their re-scaling bootstrap technique to construct confidence interval and to estimate mean square error for non-linear functions of finite population totals of several real variables. Horvitz and Thompson's estimators (HTE) are used to estimate the finite population totals. Since they need to equate the bootstrap variance of the bootstrap estimator to the Yates and Grundy's estimator (YGE) for the variance of the HTE in case of a single variable, i.e., in the linear case the YG variance estimator is required to be positive for the sample usually drawn.     

Optimum designs for parameter estimation in a mixture experiment with two correlated responses

In this paper, we investigate a mixture problem with two responses, which are functions of the mixing proportions, and are correlated with known dispersion matrix. We obtain D- and A-optimal designs for estimating the parameters of the response functions, when none or some of the regression coefficients of the two functions are the same. It is shown that when no prior knowledge about the regression coefficients is available, the D-optimal design is independent of the dispersion matrix, while the A-optimal design depends on it, provided the response functions are of different degree. On the other hand, when some of the regression coefficients are known to be the same for both the functions, the D-optimal design depends on the dispersion matrix when the two response functions are not of the same degree.     

Estimation of the smaller and larger of two exponential location parameters

Assume independent random samples are drawn from two populations which are exponentially distributed with unknown location parameters and a common known scale parameter. We want to estimate the maximum and the minimum of the unknowo location paremeters. In this paper several estimators are proposed which are better than the natural estimations in terms of absolute bias and /or meaqn squared error.     

Optimum mixture designs in a restricted region

In a mixture experiment, the response depends on the proportions of the mixing components. Canonical models of different degrees and also other models have been suggested to represent the mean response. Optimum designs for estimation of the parameters of the models have been investigated by different authors. In most cases, the optimum design includes the vertex points of the simplex as support points of the design, which are not mixture combinations in the true non-trivial sense. In this paper, optimum designs have been obtained when the experimental region is an ellipsoidal subspace of the entire factor space which does not cover the vertex points of the simplex.