Equilibrium agenda formation

We develop a definition of equilibrium for agenda formation in general voting settings. The definition is independent of any protocol. We show that the set of equilibrium outcomes for any Pareto efficient voting rule is uniquely determined, and in fact coincides with that of the outcomes generated by considering all full agendas. Under voting by successive elimination (or amendment), the set of equilibrium outcomes corresponds with the Banks set. We also examine the implications in several specific settings and show that studying equilibrium agendas can lead to sharp predictions, in contrast with well-known chaos theorems.Financial support under NSF grant SES-9986190 and an RTDF grant from the University of Warwick are gratefully acknowledged. We thank John Duggan, Martin Osborne, and an anonymous referee for valuable suggestions, and participants at the Sixth International Meeting of the Society for Social Choice and Welfare for helpful comments.     

Determinants of RFID adoption intention by SMEs: an empirical investigation

Radio frequency identification (RFID) technology has been considered as one of the 10 technologies that will transform firm across industries. However, the adoption and use of the technology has been slower than predicted, mainly because of technological, organisation and environment factors related to RFID. This study develops a conceptual model that explores the role that technological, organisational, environmental and managerial characteristics of small and mid-sized enterprises (SMEs) play in their intention to adopt RFID technology. To test the model, a web-based survey was administered to 453 SME managers from the USA, the UK, Australia and India. Logistic hierarchical regression is used to test the proposed model. Implications for RFID technology research, theory and practice are discussed.     

Inapproximability results for the lateral gene transfer problem

This paper concerns the Lateral Gene Transfer Problem. This minimization problem, defined by Hallett and Lagergren (2001), is that of finding the most parsimonious lateral gene transfer scenario for a given pair of gene and species trees. Our main results are the following:

Optimal Order Quantities with Remanufacturing Across New Product Generations

We address the problem of determining the optimal retailer order quantities from a manufacturer who makes new products in conjunction with ordering remanufactured products from a remanufacturer using used and unsold products from the previous product generation. Specifically, we determine the optimal order quantity by the retailer for four systems of decision‐making: (a) the three firms make their decisions in a coordinated fashion, (b) the retailer acts independently while the manufacturer and remanufacturer coordinate their decisions, (c) the remanufacturer acts independently while the retailer and manufacturer coordinate their decisions, and (d) all three firms act independently. We model the four options described above as centralized or decentralized decision‐making systems with the manufacturer being the Stackelberg leader and provide insights into the optimal order quantities. Coordination mechanisms are then provided which enable the different players to achieve jointly the equivalent profits in a coordinated channel.     

Consistency and asymptotic normality of the estimated effective doses in bioassay

In order to estimate the effective dose such as the 0.5 quantile _ED50${\mathrm{ED}}_{50}$ in a bioassay problem various parametric and semiparametric models have been used in the literature. If the true dose–response curve deviates significantly from the model, the estimates will generally be inconsistent. One strategy is to analyze the data making only a minimal assumption on the model, namely, that the dose–response curve is non-decreasing. In the present paper we first define an empirical dose–response curve based on the estimated response probabilities by using the “pool-adjacent-violators” (PAV) algorithm, then estimate effective doses _ED100p${\mathrm{ED}}_{100p}$ for a large range of p by taking inverse of this empirical dose–response curve. The consistency and asymptotic distribution of these estimated effective doses are obtained. The asymptotic results can be extended to the estimated effective doses proposed by Glasbey [1987. Tolerance-distribution-free analyses of quantal dose–response data. Appl. Statist. 36 (3), 251–259] and Schmoyer [1984. Sigmoidally constrained maximum likelihood estimation in quantal bioassay. J. Amer. Statist. Assoc. 79, 448–453] under the additional assumption that the dose–response curve is symmetric or sigmoidal. We give some simulations on constructing confidence intervals using different methods.     

Strategic Candidacy and Voting Procedures

We study the incentives of candidates to strategically affect the outcome of a voting procedure. We show that the outcomes of every nondictatorial voting procedure that satisfies unanimity will be affected by the incentives of noncontending candidates (i.e., who cannot win the election) to influence the outcome by entering or exiting the election.     

Employee turnover forecasting for human resource management based on time series analysis

In some organizations, the hiring lead time is often long due to responding to human resource requirements associated with technical and security constrains. Thus, the human resource departments in these organizations are pretty interested in forecasting employee turnover since a good prediction of employee turnover could help the organizations to minimize the costs and impacts from the turnover on the operational capabilities and the budget. This study aims to enhance the ability to forecast employee turnover with or without considering the impact of economic indicators. Various time series modelling techniques were used to identify optimal models for effective employee turnover prediction. More than 11-years of monthly turnover data were used to build and validate the proposed models. Compared with other models, a dynamic regression model with additive trend, seasonality, interventions, and a very important economic indicator effectively predicted the turnover with training R²?=?0.77 and holdout R²?=?0.59. The forecasting performance of optimal models confirms that time series modelling approach has the ability to predict employee turnover for the specific scenario observed in our analysis.     

Optimal use of historical information

When historical data are available, incorporating them in an optimal way into the current data analysis can improve the quality of statistical inference. In Bayesian analysis, one can achieve this by using quality-adjusted priors of Zellner, or using power priors of Ibrahim and coauthors. These rules are constructed by raising the prior and/or the sample likelihood to some exponent values, which act as measures of compatibility of their quality or proximity of historical data to current data. This paper presents a general, optimum procedure that unifies these rules and is derived by minimizing a Kullback–Leibler divergence under a divergence constraint. We show that the exponent values are directly related to the divergence constraint set by the user and investigate the effect of this choice theoretically and also through sensitivity analysis. We show that this approach yields ‘100% efficient’ information processing rules in the sense of Zellner. Monte Carlo experiments are conducted to investigate the effect of historical and current sample sizes on the optimum rule. Finally, we illustrate these methods by applying them on real data sets.     

A Bayesian semiparametric approach to learning about gene–gene interactions in case-control studies

Gene–gene interactions are often regarded as playing significant roles in influencing variabilities of complex traits. Although much research has been devoted to this area, to date a comprehensive statistical model that addresses the various sources of uncertainties, seem to be lacking. In this paper, we propose and develop a novel Bayesian semiparametric approach composed of finite mixtures based on Dirichlet processes and a hierarchical matrix-normal distribution that can comprehensively account for the unknown number of sub-populations and gene–gene interactions. Then, by formulating novel and suitable Bayesian tests of hypotheses we attempt to single out the roles of the genes, individually, and in interaction with other genes, in case-control studies. We also attempt to identify the significant loci associated with the disease. Our model facilitates a highly efficient parallel computing methodology, combining Gibbs sampling and Transformation-based MCMC (TMCMC). Application of our ideas to biologically realistic data sets revealed quite encouraging performance. We also applied our ideas to a real, myocardial infarction dataset, and obtained interesting results that partly agree with, and also complement, the existing works in this area, to reveal the importance of sophisticated and realistic modeling of gene–gene interactions.     

Multi-arm response-adaptive designs for circular responses

A multi-arm response-adaptive allocation design is developed for circular treatment outcomes. Several exact and asymptotic properties of the design are studied. Stage-wise treatment selection procedures based on the proposed response-adaptive design are also suggested to exclude the worse performing treatment(s) at earlier stages. Detailed simulation study is carried out to evaluate the proposed selection procedures. The applicability of the proposed methodologies is illustrated through a real clinical trial data on cataract surgery.