India’s Social Development in a Decade of Reforms: 1990–91/1999–2000

The economic reforms initiated in India in 1991 have brought about visible upliftment of economic conditions of the country. This paper examines if the economic process is associated with an enhancement of India’s social development in equal measure in the reform decade of nineties. Ray (1989) considered thirteen social indicators of India and constructed the country’s social development index (SDI) as a certain weighted average of the selected indicators, for the years between 1950–51 and 1975–76. The present work broadly follows Ray (1989) in tracking the social development in the decade of economic reforms from 1990–91 to 1999–2000. The movement of SDI in nineties has been compared with the movements of India’s per capita income and plan expenditure on the social sector. The results show that though there has been growth in the social sector, it is not by far quite encouraging and perhaps more needs to be done in the social sector. The views expressed in the article are those of the author and not of the institution he serves.     

Penalized image averaging and discrimination with facial and fishery applications

In this paper we use a penalized likelihood approach to image warping in the context of discrimination and averaging. The choice of average image is formulated statistically by minimizing a penalized likelihood, where the likelihood measures the similarity between images after warping and the penalty is a measure of distortion of a warping. The notions of measures of similarity are given in terms of normalized image information. The measures of distortion are landmark based. Thus we use a combination of landmark and normalized image information. The average defined in the paper is also extended by allowing random perturbation of the landmarks. This strategy improves averages for discrimination purposes. We give here real applications from medical and biological areas.     

A multivariate von mises distribution with applications to bioinformatics

Motivated by problems of modelling torsional angles in molecules, Singh, Hnizdo & Demchuk (2002) proposed a bivariate circular model which is a natural torus analogue of the bivariate normal distribution and a natural extension of the univariate von Mises distribution to the bivariate case. The authors present here a multivariate extension of the bivariate model of Singh, Hnizdo & Demchuk (2002). They study the conditional distributions and investigate the shapes of marginal distributions for a special case. The methods of moments and pseudo‐likelihood are considered for the estimation of parameters of the new distribution. The authors investigate the efficiency of the pseudo‐likelihood approach in three dimensions. They illustrate their methods with protein data of conformational angles     

Consistency of Procrustes Estimators

Lele has shown that the Procrustes estimator of form is inconsistent and raised the question about the consistency of the Procrustes estimator of shape. In this paper the consistency of estimators of form and shape is studied under various assumptions. In particular, it is shown that the Procrustes estimator of shape is consistent under the assumption of an isotropic error distribution and that consistency breaks down if the assumption of isotropy is relaxed. The relevance of these results for practical shape analysis is discussed. As a by-product, some new results are derived for the offset uniform distribution from directional data.     

Factors Affecting Infant Mortality in Rural Bangladesh: Results from a Retrospective Sample Survey1

Abstract The primary objective of this paper is to assess the relative contribution of different socioeconomic and environmental factors affecting infant mortality in a rural area of Bangladesh. Data from detailed interviews with 1,787 women of reproductive ages were collected and analyzed. A multivariate approach was employed to examine the relative importance of different correlates of infant mortality. Birth interval and contraceptive use emerged as the strongest correlates of infant mortality. In addition, landholding size, birth of preferred sex, mother's age, and prior pregnancy loss had significant effects on infant mortality. The analysis suggests that some aspects of childbearing result in higher risks of infant mortality. Infant mortality could be reduced by altering the spacing of births and avoiding births at early reproductive ages. A positive impact on infant mortality could also result from improvements in economic conditions.     

Mixtures of concentrated multivariate sine distributions with applications to bioinformatics

Motivated by examples in protein bioinformatics, we study a mixture model of multivariate angular distributions. The distribution treated here (multivariate sine distribution) is a multivariate extension of the well-known von Mises distribution on the circle. The density of the sine distribution has an intractable normalizing constant and here we propose to replace it in the concentrated case by a simple approximation. We study the EM algorithm for this distribution and apply it to a practical example from protein bioinformatics.     

A bootstrap approach to Pluto's origin

The solar nebula theory hypothesizes that planets are formed from an accretion disk of material that, over time, condenses into dust, small planetesimals, and that the planets should have, on average, coplanar, nearly circular orbits. If the orbit of Pluto has a different origin to the other planets in the solar system, then there will be tremendous repercussions on modelling the spacecrafts for a mission to Pluto. We test here the nebula theory for Pluto, using both parametric and non-parametric methods. We first develop asymptotic distributions of extrinsic means on a manifold, and then derive bootstrap and large sample distributions of the sample mean direction. Our parametric and non-parametric analyses provide very strong evidence that the solar nebula theory does not hold for Pluto.     

A Bayesian kriged Kalman model for short-term forecasting of air pollution levels

Summary.  Short-term forecasts of air pollution levels in big cities are now reported in news-papers and other media outlets. Studies indicate that even short-term exposure to high levels of an air pollutant called atmospheric particulate matter can lead to long-term health effects. Data are typically observed at fixed monitoring stations throughout a study region of interest at different time points. Statistical spatiotemporal models are appropriate for modelling these data. We consider short-term forecasting of these spatiotemporal processes by using a Bayesian kriged Kalman filtering model. The spatial prediction surface of the model is built by using the well-known method of kriging for optimum spatial prediction and the temporal effects are analysed by using the models underlying the Kalman filtering method. The full Bayesian model is implemented by using Markov chain Monte Carlo techniques which enable us to obtain the optimal Bayesian forecasts in time and space. A new cross-validation method based on the Mahalanobis distance between the forecasts and observed data is also developed to assess the forecasting performance of the model implemented.     

COVID-19 pandemic and the determinants of depressive symptoms among senior citizens of India: An empirical investigation

The COVID-19 pandemic inflicted multiple threats to individuals' physical, mental, and financial health conditions. The pandemic-related restrictive behaviors pose serious consequences for public health and increase the risk of mental illness among individuals, particularly among older citizens. The combination of their pre-existing illnesses, social isolation, COVID fear, and financial adversity frequently aggravates their condition and leads to depression and mental illness. Thus, the present study investigates the mental health status and the determinants of depressive symptoms among older adults of Bhubaneswar during the COVID pandemic context. The study used the Geriatric Depression Scale (GDS-15) to measure their depressive symptoms. The social isolation parameter is measured with the De Jong Gierveld Loneliness Scale. Financial self-efficacy, COVID-19-related psychological fear, and comorbidity health status are other determinants considered. A chi-square test and multinomial logistic regression (MLR) models are adopted to find the probable risk factors that may influence depressive symptoms among older people. The results indicate that comorbidity health conditions, a social isolation mindset, and financial efficacy issues are the significant determinants that drive an older person towards different depression categories. The improvement of these influential factors can lead senior citizens to avoid any health emergency like COVID pandemic. In the event of a public health emergency, such as COVID pandemic, the government could use the study's findings to devise methods for assisting the elderly. Society as a whole should be aware of these findings, which can lead to depressive symptoms, and offer support to the elderly. Future research may concentrate on identifying the causes of depressive symptoms in different age groups or in the presence of specific comorbidity health conditions. Future research may also investigate the factors influencing depressive symptoms in a specific occupation.