On shrinkage r-estimation in a multiple regression model

For a multiple regression model, bearing the plausibility of a subset of the regression parameters being close to a pivot, for the complementary subset, based on the usual James-Stein rule, a general formulation of shrinkage R-estimation is considered. In the light of asymptotic distributional risks of estimators, performance characteristics ( under local alternatives) of the classical R-est-imator and its preliminary test and shrinkage versions (all based on the common score function ) are studied. These shed light on the relative dominance picture in a meaningful asymptotic setup.     

Estimation of parameters of location-scale family of distributions in complete and type ii censored samples

In this paper we consider the estimation of location, scale and quantile-function of a location-scale family of distributions in complete and Type II multicensored samples. Our method integrates the two well-known approaches to probability plotting, namely, the analytic method of Blom (1958) and the hazard plotting method due to Nelson (1972). Some important special cases of location-scale family of distributions are considered for details.     

Tests of homogeneity of intercepts after a preliminary test

For the multl-sample regression model , the problem of testing equality of intercepts following a preliminarytest on the equality o f slopes is considered. The t e s t s are based on the conventional least squares estimators (without necessarily assuming the normality of the underlying error distributions ) , and the impact of a

preliminary test on the asymptotic size and power of the final test is studied.     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.     

Geriatric Care during Public Health Emergencies: Lessons Learned from Novel Corona Virus Disease (COVID-19) Pandemic

ABSTRACT

Geriatric care in public health emergencies is a serious concern, while, high case-fatalities among older adults across the globe in COVID-19 pandemic implies lack of preparedness. Most of the countries irrespective of developing and developed one enormously struggling because of an inchoate response system ignorant of geriatric health needs. Therefore, a robust approach is highly essential that requires an integrated emergency preparedness by addressing geriatric care.     

Predictive Influence of Unavailable Values of Future Explanatory Variables in a Linear Model

We consider an approach to prediction in linear model when values of the future explanatory variables are unavailable, we predict a future response y ^f at a future sample point x ^f when some components of x ^f are unavailable. We consider both the cases where x ^f are dependent and independent but normally distributed. A Taylor expansion is used to derive an approximation to the predictive density, and the influence of missing future explanatory variables (the loss or discrepancy) is assessed using the Kullback–Leibler measure of divergence. This discrepancy is compared in different scenarios including the situation where the missing variables are dropped entirely.     

Are rural–urban migrants living in urban slums more vulnerable in terms of housing,health knowledge,smoking, mental health and general health?

The magnitude of rural–urban migration in Bangladesh is increasing. Rapid urbanisation and a growing number of slums (dominated by migrants) pose many challenges to health. To our knowledge, studies regarding internal migration and health are scarce and results are mixed. Therefore, we compared several aspects, namely: housing, health knowledge, smoking, mental and general health, for three groups of migrants, designated urban natives/urban to urban migrants (UN/UU), rural to urban migrants (RU) and rural natives/rural to rural migrants (RN/RR). Results based on a sample of 5,136 adults indicated that the majority of respondents were less than 50 years old, female, married and uneducated. The percentages of UN/UU, RU and RN/RR migrants were 9.6, 69.2 and 21.3, respectively. As both bivariable and multivariable analyses indicated greater vulnerability among RU migrants in terms of the above‐mentioned aspects, this particular group deserves more attention from policy‐makers and other stakeholders. Some implications are also discussed. Key Practitioner Message: ● This study provides information regarding internal migration and explains push–pull factors in Bangladesh; ● It provides evidence regarding greater vulnerability in terms of health and other determinants among rural–urban migrants living in Dhaka slums; ● Lastly, the study justifies the importance of intervention strategies targeting poor migrants in urban slums in developing countries.