Statistical inference based on generalized Lindley record values

This paper addresses the problems of frequentist and Bayesian estimation for the unknown parameters of generalized Lindley distribution based on lower record values. We first derive the exact explicit expressions for the single and product moments of lower record values, and then use these results to compute the means, variances and covariance between two lower record values. We next obtain the maximum likelihood estimators and associated asymptotic confidence intervals. Furthermore, we obtain Bayes estimators under the assumption of gamma priors on both the shape and the scale parameters of the generalized Lindley distribution, and associated the highest posterior density interval estimates. The Bayesian estimation is studied with respect to both symmetric (squared error) and asymmetric (linear-exponential (LINEX)) loss functions. Finally, we compute Bayesian predictive estimates and predictive interval estimates for the future record values. To illustrate the findings, one real data set is analyzed, and Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and prediction.     

Different worlds of motoring: choice, constraint and risk in household consumption

The article shows how the old economic variable of income can be related to spatial location and used to reinforce recent revisionism in sociology which questions the automatic link between consumption and identity. The diversity of motoring experience is demonstrated through a case study of motoring in inner and outer London which draws on a variety of resources, including interviews from two London boroughs, survey evidence on household expenditure and other official statistics. The analysis shows how income and space variably define household motoring experience as one of choice or constraint and risk. The end result is a cultural economy analysis that shows how motoring sustains multiple, overlapping identities in different worlds of enabling and enforced consumption which connect with production.     

Bayesian estimation and prediction based on lognormal record values

In this paper we consider the problems of estimation and prediction when observed data from a lognormal distribution are based on lower record values and lower record values with inter-record times. We compute maximum likelihood estimates and asymptotic confidence intervals for model parameters. We also obtain Bayes estimates and the highest posterior density (HPD) intervals using noninformative and informative priors under square error and LINEX loss functions. Furthermore, for the problem of Bayesian prediction under one-sample and two-sample framework, we obtain predictive estimates and the associated predictive equal-tail and HPD intervals. Finally for illustration purpose a real data set is analyzed and simulation study is conducted to compare the methods of estimation and prediction.     

Stressed by Choice: a Business Model Analysis of the BBC

This paper aims to explain the recent defensive strategy of downsizing at the BBC. The paper rejects the analysis of both industry practitioner and neoclassical economics academic critics who represent the corporation as an all-powerful, abusive player in a market and instead develops an alternative concept of the business model, which focuses the pressures of financial viability and stakeholder credibility to explain the restructuring. We argue that the BBC's business model is stressed because it struggles to deliver what key stakeholders want and expect from the corporation, from a pot of revenue that is limited by regulation. The BBC's problem is compounded by demands for more programming hours following its move into digital and by the increasingly formalized demands of regulators on behalf of an absent consumer. The paper concludes that without reflexive, business-model-centred regulation, it is likely that the BBC's business model will become unsustainable.