Efficient estimation for time series following generalized linear models

In this paper, we consider James–Stein shrinkage and pretest estimation methods for time series following generalized linear models when it is conjectured that some of the regression parameters may be restricted to a subspace. Efficient estimation strategies are developed when there are many covariates in the model and some of them are not statistically significant. Statistical properties of the pretest and shrinkage estimation methods including asymptotic distributional bias and risk are developed. We investigate the relative performances of shrinkage and pretest estimators with respect to the unrestricted maximum partial likelihood estimator (MPLE). We show that the shrinkage estimators have a lower relative mean squared error as compared to the unrestricted MPLE when the number of significant covariates exceeds two. Monte Carlo simulation experiments were conducted for different combinations of inactive covariates and the performance of each estimator was evaluated in terms of its mean squared error. The practical benefits of the proposed methods are illustrated using two real data sets.     

Forecasting models for developing control scheme to improve furnace run length

In petrochemical industries, the gaseous feedstock like ethane and propane are cracked in furnaces to produce ethylene and propylene as main products and the inputs for the other plant in the downstream. A problem of low furnace run length (FRL) increases furnace decoking and reduces productivity along with the problem of reducing life of the coil. Coil pressure ratio (CPR) and tube metal temperature (TMT) are the two most important performance measures for the FRL to decide upon the need for furnace decoking. This article, therefore, makes an attempt to develop the prediction models for CPR and TMT based on the critical process parameters, which would lead to take the necessary control measures along with a prior indication for decoking. Regression-based time series and double exponential smoothing techniques are used to build up the models. The effective operating ranges of the critical process parameters are found using a simulation-based approach. The models are expected to be the guiding principles eventually to increase the average run length of furnace.     

Estimation of parameters of yamada et al. model and modification of the model

Yamada, Ohba and Osaki (1983) suggested an important NHPP model for software failure phenomenon. So far little work has been done on the problem of estimating its parameters. We present here some conditions for the likelihood estimates to be finite, positive and unique. We also suggest a modification of the model. The performance measures and statistical inferences of the modified model are discussed here. The modified model is applied to software failure data and the results are compared with Jelinski-Moranda [4] and some existing important NHPP models     

LASSO and shrinkage estimation in Weibull censored regression models

In this paper we address the problem of estimating a vector of regression parameters in the Weibull censored regression model. Our main objective is to provide natural adaptive estimators that significantly improve upon the classical procedures in the situation where some of the predictors may or may not be associated with the response. In the context of two competing Weibull censored regression models (full model and candidate submodel), we consider an adaptive shrinkage estimation strategy that shrinks the full model maximum likelihood estimate in the direction of the submodel maximum likelihood estimate. We develop the properties of these estimators using the notion of asymptotic distributional risk. The shrinkage estimators are shown to have higher efficiency than the classical estimators for a wide class of models. Further, we consider a LASSO type estimation strategy and compare the relative performance with the shrinkage estimators. Monte Carlo simulations reveal that when the true model is close to the candidate submodel, the shrinkage strategy performs better than the LASSO strategy when, and only when, there are many inactive predictors in the model. Shrinkage and LASSO strategies are applied to a real data set from Veteran's administration (VA) lung cancer study to illustrate the usefulness of the procedures in practice.     

Social assistance programs during the COVID-19 pandemic in Bangladesh: Does faith-based approach matters?

This paper aimed to examine the role of faith-based organizations (FBOs) in the distribution of social assistance (SA) during the COVID-19 pandemic in Bangladesh. The paper adopts a qualitative case-study approach to explore the best practices in the distribution of SA by five (5) organizations including FBOs, governmental organizations (GOs), Non-governmental organizations (NGOs), political groups, and private voluntary groups. In this study, 14 beneficiaries of SA were selected by stratified proportionate random sampling method, and five (5) key personnel were selected purposively based on their experiences in the management of SA distribution. The study found that “no-move, no touch” approach followed by FBOs is more likely to establish human rights and social justice and reduce the transmission of diseases. In contrast to the modern idea, which undermines the faith-based charity, the study proved that faith-based charity is gaining attraction as an effective approach combating global pandemic. The findings of this paper will be useful for policymakers, voluntary service workers, GO, and NGO workers to ensure the distribution of SA in a more productive and disciplined way during and after an emergency like the COVID-19 pandemic.     

Social Work Practice for Social Development in Bangladesh: Issues and Challenges

Social work addresses social issues that constrain the betterment of a community. However, social work practice is struggling to deal effectively with the development challenges of Bangladesh. Based on a literature review and the experiences of the authors, this paper explores the emerging issues where social work practice is anticipated as a promising alternative for bringing sustainable social development in Bangladesh. Simultaneously, the paper discovers the limitation of social work practice in Bangladesh addressing these challenges.     

Shrinkage estimator of regression model under asymmetric loss

This article investigates the performance of the shrinkage estimator (SE) of the parameters of a simple linear regression model under the LINEX loss criterion. The risk function of the estimator under the asymmetric LINEX loss is derived and analyzed. The moment-generating functions and the first two moments of the estimators are also obtained. The risks of the SE have been compared numerically with that of pre-test and least-square estimators (LSEs) under the LINEX loss criterion. The numerical comparison reveals that under certain conditions the LSE is inadmissible, and the SE is the best among the three estimators.     

Women’s status and domestic violence in rural Bangladesh: Individual- and community-level effects

We explore the determinants of domestic violence in two rural areas of Bangladesh. We found increased education, higher socioeconomic status, non-Muslim religion, and extended family residence to be associated with lower risks of violence. The effects of women's status on violence was found to be highly context-specific. In the more culturally conservative area, higher individual-level women's autonomy and short-term membership in savings and credit groups were both associated with significantly elevated risks of violence, and community-level variables were unrelated to violence. In the less culturally conservative area, in contrast, individual-level women's status indicators were unrelated to the risk of violence, and community-level measures of women's status were associated with significantly lower risks of violence, presumably by reinforcing nascent normative changes in gender relations.     

Detection of influential observations in multivariate regression

Several methods have been suggested, in the literature, to detect influential observations from the data fitting usual linear model y=X???+???, ???∽N(0, ???2I). Recently, Chatterjee & Hadi (1986) have reviewed most of these available methods and described the inter-relationships between them. In this article, we extend some of these methods to the case of multivariate regression data. We consider several data sets to illustrate the methods.