He Named Me Malala: connecting the historical,the local,and the global

My essay highlights how this (otherwise excellent) documentary film He Named Me Malala suffers from a historical amnesia in failing to connect the historical, local, and global/Western factors that have set the political-social context within which occurred the Malala incident in 2012. This is because the documentary – exposing the voice of a Pakistani female activist as a postcolonial/global agent – does not look into the historical-colonial, Cold War, or the post-Cold War dynamics that have set the ‘context’ within which the theme of the documentary unfolded. My essay addresses these issues of historical amnesia, arguing that to better comprehend the Malala incident (and broadly the issue of gender violence in Pakistan’s socio-cultural context) it remains imperative to connect how factors of power, politics, and vested interests have intersected at historical, local, and global levels to explain the 2012 Malala incident.     

On maximum Wiener index of trees and graphs with given radius

Let G be a connected graph of order n. The long-standing open and close problems in distance graph theory are: what is the Wiener index W(G) or average distance \(\mu (G)\) among all graphs of order n with diameter d (radius r)? There are very few number of articles where were worked on the relationship between radius or diameter and Wiener index. In this paper, we give an upper bound on Wiener index of trees and graphs in terms of number of vertices n, radius r, and characterize the extremal graphs. Moreover, from this result we give an upper bound on \(\mu (G)\) in terms of order and independence number of graph G. Also we present another upper bound on Wiener index of graphs in terms of number of vertices n, radius r and maximum degree \(\Delta \), and characterize the extremal graphs.     

Regression for doubly inflated multivariate Poisson distributions

Dependent multivariate count data occur in several research studies. These data can be modelled by a multivariate Poisson or Negative binomial distribution constructed using copulas. However, when some of the counts are inflated, that is, the number of observations in some cells are much larger than other cells, then the copula-based multivariate Poisson (or Negative binomial) distribution may not fit well and it is not an appropriate statistical model for the data. There is a need to modify or adjust the multivariate distribution to account for the inflated frequencies. In this article, we consider the situation where the frequencies of two cells are higher compared to the other cells and develop a doubly inflated multivariate Poisson distribution function using multivariate Gaussian copula. We also discuss procedures for regression on covariates for the doubly inflated multivariate count data. For illustrating the proposed methodologies, we present real data containing bivariate count observations with inflations in two cells. Several models and linear predictors with log link functions are considered, and we discuss maximum likelihood estimation to estimate unknown parameters of the models.     

Transportation Approach to Locating Plants in Relation to Potential Markets and Raw Material Sources

Proximity to the market place has been considered the most important locational factor in the literature so far. However, the growing need for frequent deliveries of materials in smaller lot sizes for just-in-time (JIT) manufacturing prefers plants closer to the raw materials sources. Locating plants for JIT manufacturing is, therefore, a problem of finding the right balance between the requirements of procurement and distribution. A comprehensive model is proposed in this paper to solve this problem. The model is solved by using the transportation algorithm and produces simultaneous decisions on procurement, production, and distribution. The model does not require plant sizes to be known a priori, but it allows upper limits to be placed on the size of each plant and raw material source. As such, optimal plant capacities along with the corresponding procurement and distribution quantities are all determined by the model solution.     

Charting the Course: An Overview of Domestic Violence in the South Asian Community in the United States

In the South Asian community in the United States, domestic violence is a prevalent problem of significant magnitude. Although the community stridently denies the existence of this horror, women have been systematically organizing antiviolence-against-women work for the last 15 years. At this time, it is a vibrant movement struggling with several complex issues that are perhaps less common in the dominant white community. As in the lives of immigrant women of color, much of the intricacies of domestic violence in the South Asian context emerge from the intersections of race, class, and residency status problems. Consequently, a slew of personal, institutional, and cultural barriers commingle to form roadblocks for battered South Asian women, who attempt to escape family violence. As the needs of battered South Asian first and second generation women enlarge and become more perceptible, the community-based organizations have to ready themselves for more complicated activities in the future.     

Nonparametric Estimation of the Conditional Distribution at Regression Boundary Points

Abstract

Nonparametric regression is a standard statistical tool with increased importance in the Big Data era. Boundary points pose additional difficulties but local polynomial regression can be used to alleviate them. Local linear regression, for example, is easy to implement and performs quite well both at interior and boundary points. Estimating the conditional distribution function and/or the quantile function at a given regressor point is immediate via standard kernel methods but problems ensue if local linear methods are to be used. In particular, the distribution function estimator is not guaranteed to be monotone increasing, and the quantile curves can “cross.” In the article at hand, a simple method of correcting the local linear distribution estimator for monotonicity is proposed, and its good performance is demonstrated via simulations and real data examples. Supplementary materials for this article are available online.     

Threshold knot selection for large-scale spatial models with applications to the Deepwater Horizon disaster

Large spatial datasets are typically modelled through a small set of knot locations; often these locations are specified by the investigator by arbitrary criteria. Existing methods of estimating the locations of knots assume their number is known a priori, or are otherwise computationally intensive. We develop a computationally efficient method of estimating both the location and number of knots for spatial mixed effects models. Our proposed algorithm, Threshold Knot Selection (TKS), estimates knot locations by identifying clusters of large residuals and placing a knot in the centroid of those clusters. We conduct a simulation study showing TKS in relation to several comparable methods of estimating knot locations. Our case study utilizes data of particulate matter concentrations collected during the course of the response and clean-up effort from the 2010 Deepwater Horizon oil spill in the Gulf of Mexico.     

Robust designs for multinomial models

Abstract

Model misspecification in generalized linear models (GLMs) occurs usually when the linear predictor and/or the link function assumed are incorrect. This article discusses the effect of such misspecification on design selection for multinomial GLMs and proposes the use of quantile dispersion graphs to select robust designs. Due to misspecification in the model, parameter estimates are usually biased and the designs are compared on the basis of their mean squared error of prediction. Several numerical examples including a real data set are presented to illustrate the proposed methodology.     

Estimation in Second-Order Models with Errors in the Factor Levels

The impact of errors in the factor levels is examined on the estimation of parameters in second-order response models. Errors can occur in setting the factor levels for response surface and robust parameter design models. These errors can lead to heterogeneity of variances in model errors that make ordinary least squares estimation inappropriate. Weighted least squares and maximum likelihood estimation approaches are developed as viable alternatives where it is assumed the variances and covariances of the errors are known. Performance of these estimation techniques are examined in simulation studies for two examples. Another example is given that applies these results.     

Designs Robust Against the Presence of an Aberration in a Diallel Cross Design Setup

In this article, we find designs insensitive to the presence of an outlier in a diallel cross design setup for estimating a complete set of orthonormal contrasts among the effects of the general combining abilities of a set of parental lines. The criterion of robustness, suggested by Mandal (1989 Mandal , N. K. ( 1989 ). On robust designs . Cal. Stat. Assoc. Bull. 38 : 115 – 119 . [Google Scholar]) in block design setup and used by Biswas (2012 Biswas , A. ( 2012 ). Block designs robust against the presence of an aberration in a treatment-control setup . Commun. Statist. Theor. Meth. 41 : 920 – 933 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) in treatment-control setup, is adapted here. Complete diallel cross designs, suggested by Gupta and Kageyama (1994 Gupta , S. , Kageyama , S. ( 1994 ). Optimal complete diallel crosses . Biometrika 81 : 420 – 424 .[Crossref], [Web of Science ®] , [Google Scholar]), and partial diallel cross designs, suggested by Gupta et al. (1995 Gupta , S. , Das , A. , Kageyama , S. ( 1995 ). Single replicate orthogonal block designs for circulant partial diallel crosses . Commun. Statist. A 24 : 2601 – 2607 .[Taylor & Francis Online] , [Google Scholar]) and Mukerjee (1997 Mukerjee , R. ( 1997 ). Optimal partial diallel crosses . Biometrika 84 : 939 – 948 .[Crossref], [Web of Science ®] , [Google Scholar]), are found to be robust under certain conditions.