Qualitative inequalities for squared partial correlations of a Gaussian random vector

We describe various sets of conditional independence relationships, sufficient for qualitatively comparing non-vanishing squared partial correlations of a Gaussian random vector. These sufficient conditions are satisfied by several graphical Markov models. Rules for comparing degree of association among the vertices of such Gaussian graphical models are also developed. We apply these rules to compare conditional dependencies on Gaussian trees. In particular for trees, we show that such dependence can be completely characterized by the length of the paths joining the dependent vertices to each other and to the vertices conditioned on. We also apply our results to postulate rules for model selection for polytree models. Our rules apply to mutual information of Gaussian random vectors as well.Comment: 21 pages, 13 figure

A Conditional Empirical Likelihood Based Method for Model Parameter Estimation from Complex survey Datasets

We consider an empirical likelihood framework for inference for a statistical model based on an informative sampling design. Covariate information is incorporated both through the weights and the estimating equations. The estimator is based on conditional weights. We show that under usual conditions, with population size increasing unbounded, the estimates are strongly consistent, asymptotically unbiased and normally distributed. Our framework provides additional justification for inverse probability weighted score estimators in terms of conditional empirical likelihood. In doing so, it bridges the gap between design-based and model-based modes of inference in survey sampling settings. We illustrate these ideas with an application to an electoral survey

Reversing the Stein Effect

The Reverse Stein Effect is identified and illustrated: A statistician who shrinks his/her data toward a point chosen without reliable knowledge about the underlying value of the parameter to be estimated but based instead upon the observed data will not be protected by the minimax property of shrinkage estimators such as that of James and Stein, but instead will likely incur a greater error than if shrinkage were not used.Comment: Published in at http://dx.doi.org/10.1214/09-STS278 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Estimation of a Covariance Matrix with Zeros

We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call Iterative Conditional Fitting, for computing the maximum likelihood estimator of the constrained covariance matrix, under the assumption of multivariate normality. In contrast to previous approaches, this algorithm has guaranteed convergence properties. Dropping the assumption of multivariate normality, we show how to estimate the covariance matrix in an empirical likelihood approach. These approaches are then compared via simulation and on an example of gene expression.Comment: 25 page

The Influence of Cause-Related Marketing on Millennials’ Purchase Intentions: Evidence of CSR from an Emerging Economy

Corporate social responsibility (CSR) has been implemented through sponsorships, philanthropy, and cause-related marketing (CRM), amongst which CRM has aroused the interest of many academicians and stakeholders. The study aims to examine the antecedents of cause-related marketing while considering attitude as a mediator to test its relationship with the purchase intention. The snowball sampling technique for data collection was administered to Indian millennial consumers from the regions of Karnataka and Kerala. A total of 313 valid cases were selected for the analysis, which employed partial least squares (PLS) based on structural equation modeling (SEM). The findings have shown that a positive relationship exists between cause participation and purchase intention. Further, product/cause congruence & consumer/cause identification had a positive impact on attitude, while attitude, in turn, showed a favorable association with the purchase intention. This study disclosed the relative importance of the compatibility between the social causes supported by the company with its engaged business while adopting CRM campaigns, and highlighted the need for the involvement of consumers in the CRM programs for their effectiveness

Insulin Initiation with Insulin Degludec/Insulin Aspart versus Insulin Glargine in Oral Antidiabetic Drugs Failure Patients with Type 2 Diabetes Mellitus: A Real-World Study from India

Context: Oral antidiabetic drug (OAD) failure is an indication for starting insulin therapy, but there is still a dilemma as to whether basal insulin or a premixed/co-formulation analog should be the choice. Aim: To compare the safety and efficacy of once daily (OD) insulin degludec/insulin aspart (IDegAsp) to OD insulin glargine (IGlar U100) in insulin-naïve Indian subjects with type 2 diabetes mellitus (T2DM), inadequately controlled with OADs alone. Setting and design: Retrospective study. Methods and material: Data was retrieved from the author’s clinic database of OAD failure patients (18-80 years), who were started either with (IGlar U100, n = 120) or IDegAsp (n = 89) OD over and above the standard of care. Data of fasting plasma glucose (FPG), postprandial plasma glucose (PPG) and glycated hemoglobin (HbA1c) from baseline and at last follow-up visits were collected. Statistical analysis used: Baseline characteristics and change in study parameters during the follow-up period were computed between two groups (IGlar U100 vs. IDegAsp) by unpaired t-test and paired t-test, respectively. ANCOVA test was used to compute percentage reduction in body weight, body mass index (BMI), FPG, PPG and HbA1c in between two groups (IGlar U100 vs. IDegAsp). Results: IDegAsp caused a significantly greater reduction in FPG, PPG and HbA1c as compared to the IGlar U100 arm. There was no significant difference in the proportion of patients with hypoglycemia between IDegAsp and IGlar U100 groups (p = 0.208). No episodes of severe hypoglycemia were reported. Conclusion: Comparison of IDegAsp and IGlar U100 OD in T2DM patients indicated that both were relatively safe but the former controlled FPG and PPG levels more effectively