Response-based multiple imputation method for minimizing the impact of covariate detection limit in logistic regression

Abstract

Presence of detection limit (DL) in covariates causes inflated bias and inaccurate mean squared error to the estimators of the regression parameters. This paper suggests a response-driven multiple imputation method to correct the deleterious impact introduced by the covariate DL in the estimators of the parameters of simple logistic regression model. The performance of the method has been thoroughly investigated, and found to outperform the existing competing methods. The proposed method is computationally simple and easily implementable by using three existing R libraries. The method is robust to the violation of distributional assumption for the covariate of interest.     

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.     

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.     

Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components

Differential analysis techniques are commonly used to offer scientists a dimension reduction procedure and an interpretable gateway to variable selection, especially when confronting high-dimensional genomic data. Huang et al. used a gene expression profile of breast cancer cell lines to identify genomic markers which are highly correlated with in vitro sensitivity of a drug Dasatinib. They considered three statistical methods to identify differentially expressed genes and finally used the results from the intersection. But the statistical methods that are used in the paper are not sufficient to select the genomic markers. In this paper we used three alternative statistical methods to select a combined list of genomic markers and compared the genes that were proposed by Huang et al. We then proposed to use sparse principal component analysis (Sparse PCA) to identify a final list of genomic markers. The Sparse PCA incorporates correlation into account among the genes and helps to draw a successful genomic markers discovery. We present a new and a small set of genomic markers to separate out the groups of patients effectively who are sensitive to the drug Dasatinib. The analysis procedure will also encourage scientists in identifying genomic markers that can help to separate out two groups.     

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.     

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     

Model Selection When a Key Parameter is Constrained to be in an Interval

This article considers model selection procedures based on choosing the model with the largest maximized log-likelihood minus a penalty, when key parameters are restricted to be in a closed interval. Its main emphasis is how these penalties might be chosen in small samples to give good properties of the resultant procedure. We illustrate two model selection problems in the context of Box–Cox transformations and their application to the linear regression model. Simulation results for both problems indicate that the new procedure clearly dominates existing procedures in terms of having higher probabilities of correctly selecting the true model.     

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.     

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.