Clustering of gamma-ray bursts through kernel principal component analysis

We consider the problem related to clustering of gamma-ray bursts (from “BATSE” catalogue) through kernel principal component analysis in which our proposed kernel outperforms results of other competent kernels in terms of clustering accuracy and we obtain three physically interpretable groups of gamma-ray bursts. The effectivity of the suggested kernel in combination with kernel principal component analysis in revealing natural clusters in noisy and nonlinear data while reducing the dimension of the data is also explored in two simulated data sets.     

A general class of non parametric tests for comparing scale parameters

In this paper, a general class of non parametric tests is proposed for the two-sample scale problem. Testing of the scale parameter is very useful in real-life situations commonly faced in engineering, trade, cultivation, industries, medicine, etc. In all these fields, one will prefer the method that gives more consistent results. Thus, it is worthwhile to test the equality of scale parameters. The distribution of the proposed test is established. To assess the performance of the proposed test, the asymptotic efficacies are studied for some underlying distributions and the results are interpreted with useful information. To see the working of the proposed test, an illustrative example for the real-life data set is provided. The simulation study is also carried out to find the asymptotic power of the proposed test. An extension of the general class of tests to the multiple-sample problem is also discussed.     

Analyzing Social Media Engagement and its Effect on Online Product Purchase Decision Behavior

The continuous progress of society in using online resources has given opportunity to many business executives to utilize social media for their brand promotion or recognition. Organizations utilize social media sites like Twitter, Facebook, LinkedIn, Flicker, Google Plus, and YouTube to promote their product, service, or brand worldwide. Increase of consumer engagement in online social activities has forced companies to integrate social media factors into their marketing strategy. The objective of this study is to find the important factors on which the online purchasing decision depends, based on the personality types of consumers and their attitude toward social media with brand perception.     

Prevalence of job strain among Indian foundry shop floor workers

Global competition in manufacturing sector demand higher productivity levels. In this context, workers in this sector are set with high output targets, leading to job strain. In addition to the strain, hazardous conditions also prevail in some of the manufacturing processes like foundry activities. This paper attempts to appraise the prevalence of job strain among foundry shop floor workers in India with the help of Demands-Control model [8]. In this study, data was collected through a survey using 49-item Job Content Questionnaire (JCQ) [9], a widely used and well-validated test for job strain. Then the data was subjected to statistical analysis after ascertaining the reliability. This survey has revealed that 25% of workers in foundry were experiencing high job strain. Hazardous working conditions, limited decision making authority, etc. appear to be the main contributing factors for the higher levels of strain.     

Spatial Sampling Design for a Demographic and Health Survey

The recent advances in global position systems (GPS), geographic information systems (GIS), and remote sensing (RS) can be exploited for spatial sampling design for demographic and health surveys. These technologies are particularly useful when a sampling frame is unavailable and/or location (of household) is important for data collection (e.g., location of residence might greatly impact exposure to ambient air pollution among members of a population). Building on these technologies, this article presents a methodology of spatial sampling adopted for the respiratory health and demographic survey conducted in Delhi and its environs from January through April 2004. The overall goal of the survey was to select households that adequately represented exposure to ambient air pollution. The proposed methodology involved constructing a sampling frame of residential areas and the simulation of weighted random points within residential areas. The simulated locations were navigated with the aid of GPS to identify households at these locations and to acquire residents’ consent to participate in the survey; a total of 1,576 households at the 2,000 simulated locations were found suitable and participated in the survey. The average ambient air pollution at the sample sites was not significantly different from the average air pollution observed in the study area, which demonstrates the robustness of the proposed sampling method.     

Estimation of smooth regression functions in monotone response models

We consider the estimation of smooth regression functions in a class of conditionally parametric co-variate-response models. Independent and identically distributed observations are available from the distribution of (Z,X)$(Z,X)$, where Z is a real-valued co-variate with some unknown distribution, and the response X conditional on Z   is distributed according to the density p(·,ψ(Z))$p(·,\psi (Z))$, where p(·,θ)$p(·,\theta )$ is a one-parameter exponential family. The function ψ$\psi$ is a smooth monotone function. Under this formulation, the regression function E(X|Z)$E(X|Z)$ is monotone in the co-variate Z   (and can be expressed as a one–one function of ψ$\psi$); hence the term “monotone response model”. Using a penalized least squares approach that incorporates both monotonicity and smoothness, we develop a scheme for producing smooth monotone estimates of the regression function and also the function ψ$\psi$ across this entire class of models. Point-wise asymptotic normality of this estimator is established, with the rate of convergence depending on the smoothing parameter. This enables construction of Wald-type (point-wise) as well as pivotal confidence sets for ψ$\psi$ and also the regression function. The methodology is extended to the general heteroscedastic model, and its asymptotic properties are discussed.     

CONFLICT RESOLUTION IN INVESTMENT DECISIONS: IMPLEMENTATION OF GOAL PROGRAMMING METHODOLOGY FOR DUAL-PURPOSE FUNDS*

Goal programming (GP) is designed to resolve allocation problems with conflicting goals. Both goals and constraints are incorporated in the allocational decision, and the objective function is stated in a way that, upon solution, yields a result “as close as possible” to the priority-weighted goals. The present paper applies GP methodology to the investment decision of dual-purpose funds (DPFs), that are required by law to pursue allocational decisions with potentially conflicting objectives. It provides an empirical demonstration that DPF managers could have improved their investment selection and subsequent performance by the use of GP methodology. Finally the paper stresses the importance of sensitivity analysis to improve both the goal-ranking and target-selection aspects of the methodology and provides a limited but illuminating empirical demonstration of post-optimality analysis.