‘Others’ Within the ‘Others’: An Intersectional Analysis of Gender Violence in India

Gender Issues - In 2018, the rapes of two young girls shook India. The ruling government blatantly supported the perpetrators in both cases. It was also highlighted that if the victims were high...     

Estimation of scale parameters in mixture distributions

Simultaneous estimation of scale parameters is considered in mixture distributions under squared-error loss. A general class of estimators is obtained which dominates the componentwise best multiple estimators and the moment estimators. As special cases, improved estimators are obtained for the multivariate t-distribution and the p-variate Lomax distribution.     

On Some Chemical Balance Weighing Designs

The present paper deals with certain chemical balance weighing designs. The incidence matrices of BIB designs and balanced ternary designs have been used to construct these designs. Conditions have been obtained to make some of these designs “optimum”.     

Discriminating between the Weibull and log-normal distributions for Type-II censored data

Log-normal and Weibull distributions are the two most popular distributions for analysing lifetime data. In this paper, we consider the problem of discriminating between the two distribution functions. It is assumed that the data are coming either from log-normal or Weibull distributions and that they are Type-II censored. We use the difference of the maximized log-likelihood functions, in discriminating between the two distribution functions. We obtain the asymptotic distribution of the discrimination statistic. It is used to determine the probability of correct selection in this discrimination process. We perform some simulation studies to observe how the asymptotic results work for different sample sizes and for different censoring proportions. It is observed that the asymptotic results work quite well even for small sizes if the censoring proportions are not very low. We further suggest a modified discrimination procedure. Two real data sets are analysed for illustrative purposes.     

Zero-inflated Poisson model with clustered regression coefficients: Application to heterogeneity learning of field goal attempts of professional basketball players

Although basketball is a dynamic process sport, played between two sides of five players each, learning some static information is essential for professional players, coaches, and team managers. In order to have a deep understanding of field goal attempts among different players, we propose a zero-inflated Poisson model with clustered regression coefficients to learn the shooting habits of different players over the court and the heterogeneity among them. Specifically, the zero-inflated model captures a large portion of the court with zero field goal attempts, and the mixture of finite mixtures model captures the heterogeneity among different players based on clustered regression coefficients and inflated probabilities. Both theoretical and empirical justification through simulation studies validate our proposed method. We apply our proposed model to data from the National Basketball Association (NBA), for learning players' shooting habits and heterogeneity among different players over the 2017–2018 regular season. This illustrates our model as a way of providing insights from different aspects.     

Bayesian analysis of multivariate survival data using Monte Carlo methods

This paper deals with the analysis of multivariate survival data from a Bayesian perspective using Markov-chain Monte Carlo methods. The Metropolis along with the Gibbs algorithm is used to calculate some of the marginal posterior distributions. A multivariate survival model is proposed, since survival times within the same group are correlated as a consequence of a frailty random block effect. The conditional proportional-hazards model of Clayton and Cuzick is used with a martingale structured prior process (Arjas and Gasbarra) for the discretized baseline hazard. Besides the calculation of the marginal posterior distributions of the parameters of interest, this paper presents some Bayesian EDA diagnostic techniques to detect model adequacy. The methodology is exemplified with kidney infection data where the times to infections within the same patients are expected to be correlated.     

The Impact of Lean Management Practices and Sustainably‐Oriented Innovation on Sustainability Performance of Small and Medium‐Sized Enterprises: Empirical Evidence from the UK

While lean management practices (LMP) help small and medium‐sized enterprises (SMEs) to be efficient, sustainability‐oriented innovation (SOI) facilitates adopting environmental and social practices. Although prior research looks into the effect of LMP on the economic performance (EP) of SMEs, less is known about the effect of LMP on sustainability (economic, environmental and social) performance. Studies on the effect of SOI on sustainability and economic performance are also scant. Additionally, examining the mediating effect of corporate social responsibility (CSR) practices (environmental and social practices) on both LMP and SOI achieving sustainability performance (SP) is rare. This research bridges these knowledge gaps by answering the question of how LMP, SOI, CSR practices, sustainability and economic performance are correlated. Through hypothesis testing using structural equation modelling, this study reveals the impact of LMP, SOI, CSR (environmental and social) practices on sustainability and economic performance. The study uses data from 119 SMEs within manufacturing industries in the Midlands, UK. The analysis reveals that LMP and SOI facilitate achieving both sustainability and economic performance, and SOI mediates LMP to achieve sustainability performance. Additionally, although CSR practices mediate LMP to achieve sustainability performance, they only borderline mediate SOI to achieve sustainability performance.     

Bayesian robustness for multiparameter problems

Bayesian robustness is studied for ε-contamination classes of prior distributions. Nonparametric classes of contsminations such as the class of all unimodal spherically symmetric densities are considered here. Posterior φ-divergence and its curvature are used to measure the sensitivity of priors on the resulting posterior densities. Examples are provided to illustrate our results.