The Competitive Impact of Targeted Television Advertisements Using DVR Technology

Digital video recorders (DVRs) are an emerging technology that is fundamentally changing the competitive landscape in industries that advertise on television. Perhaps the most familiar impact of DVR technology is that it enables consumers to avoid advertisements (ads) by fast forwarding through them. However, this “zipping” of ads is only one aspect of the impact of DVR technology. DVRs also collect a wealth of information at the consumer level that can be used by firms to target their ads more effectively. We examine how this targeting capability moderates the impact of ad avoidance in a competitive setting. Insights are provided on how best to manage this emerging technology in terms of the key managerial decisions of product pricing and advertising efforts, as well as its impact on profits in a competitive (duopoly) marketplace.     

Some new factor screening designs using the search linear model

Factor screening designs for searching two and three effective factors using the search linear model are discussed. The construction of such factor screening designs involved finding a fraction with small number of treatments of a 2^m factorial experiment having the property P_2t (no 2t columns are linearly dependent) for t=2 and 3. A ‘Packing Problem’ is introduced in this connection. A complete solution of the problem in one case and partial solutions for the other cases are presented. Many practically useful new designs are listed.     

LGBTQ Activist Organizations as ‘Respectably Queer’ in India: Contesting a Western View

This study contests the distinction of LGBTQ (lesbian, gay, bisexual, transgender and queer) organizations suggested by earlier scholars as ‘respectable’ — i.e. normalizing, professionalizing and conforming to the dominant cultural and institutional patterns — and ‘queer’, meaning challenging the cultural and institutional forces that ‘normalize and commodify differences’. Using Bernstein's model of identity deployment, it is found problematic to distinguish LGBTQ organizations this way because when the actions of LGBTQ organizations are more complex to describe, it is not warranted to conflate identity goals with identity strategies — whether normalizing (respectable) or differentiating (queer). To examine these concerns, a qualitative inquiry was used to study five LGBTQ organizations in India where the intersections of post‐colonial ethnicity, gender, social class and sexuality offer an intriguing context through which to study queer activism. Based on the findings, it is argued from a post‐colonial perspective that when the socio‐cultural and historical existence of non‐homonormative queer communities and practices is strong, LGBTQ organizations challenge the heteronormative and/or other forms of domination to become ‘queer’. But they may simultaneously become ‘respectable′ by conforming to the diversity politics of non‐profit business, donors, and social movement organizations they seek support from, and turn out as ‘respectably queer’.     

Bayesian Variable Selection Under Collinearity

In this article, we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of the posterior distribution based on marginal and joint distributions may give conflicting results for assessing the importance of strongly correlated covariates. The natural question is which one should be used in practice. The simulation studies suggest that posterior inclusion probabilities and Bayes factors that evaluate the importance of correlated covariates jointly are more appropriate, and some priors may be more adversely affected in such a setting. To obtain a better understanding behind the phenomenon, we study some toy examples with Zellner’s g-prior. The results show that strong collinearity may lead to a multimodal posterior distribution over models, in which joint summaries are more appropriate than marginal summaries. Thus, we recommend a routine examination of the correlation matrix and calculation of the joint inclusion probabilities for correlated covariates, in addition to marginal inclusion probabilities, for assessing the importance of covariates in Bayesian variable selection.     

Negative and Positive Partial Mobility: A Study of the Relative Changes in Human Development

In this paper, we have considered the issues concerning dynamic changes in HDI and its various components from a relative standpoint. The analysis of HDI mobility should entail directional movement introduced by Fields (in Distribution and development: a new look at the developing world. MIT Press, Cambridge, 2001). However, Fields’ analysis was in the space of absolute values. Here, we will try to extend this exercise to the positional mobility index by considering the question of improvement and deterioration in relative positions. This requires the concept of partial mobility where mobility is judged from the viewpoint of a particular group. We then try to introduce directional changes in partial mobility through axiomatic framework. Finally, we provide an illustrative example from the Indian data.     

Bayesian unit-root tests for Stochastic Volatility models

In this article, we consider Bayesian inference procedures to test for a unit root in Stochastic Volatility (SV) models. Unit-root tests for the persistence parameter of the SV models, based on the Bayes Factor (BF), have been recently introduced in the literature. In contrast, we propose a flexible class of priors that is non-informative over the entire support of the persistence parameter (including the non-stationarity region). In addition, we show that our model fitting procedure is computationally efficient (using the software WinBUGS). Finally, we show that our proposed test procedures have good frequentist properties in terms of achieving high statistical power, while maintaining low total error rates. We illustrate the above features of our method by extensive simulation studies, followed by an application to a real data set on exchange rates.     

Estimating the distribution function using k-tuple ranked set samples

The basic assumption underlying the concept of ranked set sampling is that actual measurement of units is expensive, whereas ranking is cheap. This may not be true in reality in certain cases where ranking may be moderately expensive. In such situations, based on total cost considerations, k-tuple ranked set sampling is known to be a viable alternative, where one selects k units (instead of one) from each ranked set. In this article, we consider estimation of the distribution function based on k-tuple ranked set samples when the cost of selecting and ranking units is not ignorable. We investigate estimation both in the balanced and unbalanced data case. Properties of the estimation procedure in the presence of ranking error are also investigated. Results of simulation studies as well as an application to a real data set are presented to illustrate some of the theoretical findings.