On some consistent tests of mutual independence among several random vectors of arbitrary dimensions

Statistics and Computing - Testing for mutual independence among several random vectors is a challenging problem, and in recent years, it has gained significant attention in statistics and machine...     

An econometric analysis of the U.S. postwar G.N.P.

Whilst most developed countries have experienced stable economic conditions during the postwar period, the acceptance of the unit root null hypothesis implies wild fluctuations in the major economic variables over time. This paper investigates the sensitivity of the decisions to accept the unit root hypothesis to the specification of the trends underlying the U.S. postwar GNP and other macro-variables. In particular, the relationship between the nominal GNP and the resident population is found to be a non-linear one. The unit root null hypothesis can be firmly rejected when the conditional mean of the nominal GNP series is represented by a quadratic trend variable and the assumptions that changes in the price level and the resident population lead to equiproportionate changes in the GNP are not enforced on the data. The case of quarterly observations is also investigated for seasonally unadjusted and adjusted data.The author is indebted to two anonymous referees and the Managing Editor for helpful comments.     

The Effect of Environmental Disclosure Requirements on Willingness to Pay for Residential Properties in Borderlands Community*

Objective. This study examines the effect of environmental information disclosure requirements on future real estate transactions. The setting involves pollution from a concrete products and quarrying site near a largely Hispanic, residential community. Methods. The survey‐based contingent valuation (CV) method is used to test the hypothesis that a split‐sample treatment for an information disclosure requirement with a potentially negative affective quality will reduce the willingness to pay (WTP) of potential buyers for a typical property. Results. Results indicate that the information disclosure treatment reduces WTP, and that this effect is significantly exacerbated when the surveys are conducted in Spanish. Conclusions. In addition to identifying significant reductions in the WTP of potential home buyers, our findings demonstrate the importance of cultural and regional considerations for how information disclosure requirements are conducted in real estate transactions. Future CV studies should also consider Spanish‐language options in applications involving significant Hispanic populations.     

An Empirical Investigation of the “Dynamic McFadden” Model of Purchase Timing and Brand Choice: Implications for Market Structure

Purchase timing and brand-choice decisions of households are jointly investigated using the “dynamic McFadden” model of Heckman and Singer. The hazard of brand purchase is decomposed into the category purchase hazard and the probability of brand choice conditional on category purchase. The former is modeled using the hazard-function approach and the latter using a logit model. Unobserved heterogeneity in brand preferences, marketing effects, and baseline hazard parameters is accounted for in the empirical analysis. The distribution of preference heterogeneity identifies the locations of brands in multiattribute perceptual space and the distribution of attribute importance weights across households.     

An empirical analysis of innovation strategies of biotechnology firms in the U.S.

The paper explores the strategies of biotechnology firms in the U.S. through a mail questionnaire study. Based on the responses of 89 companies we have developed strategy archetypes of these firms in R&D, marketing, and technology acquisition. In R&D, we found the firms to follow either incremental or radical strategy. In marketing, the firms use either a defender or an innovator strategy. In technology acquisition, firms differ in terms of their emphasis on licensing or developing new technology. The interrelationship among the strategy groups is weak. The R&D and technology acquisition strategies are related in the sense that aggressive technology strategy dictates radical R&D behavior with emphasis on development of new technology. We have found that firms following aggressive technology strategy tend to follow conservative marketing strategy. This is consistent with an earlier study by of German firms where it was found that firms tend to balance their technical and marketing risks. The paper also provides additional information about the factors considered to be important in product decisions for various strategies.     

Methods for estimating relative risk in studies of common binary outcomes

Studying the effect of exposure or intervention on a dichotomous outcome is very common in medical research. Logistic regression (LR) is often used to determine such association which provides odds ratio (OR). OR often overestimates the effect size for prevalent outcome data. In such situations, use of relative risk (RR) has been suggested. We propose modifications in Zhang and Yu and Diaz-Quijano methods. These methods were compared with stratified Mantel Haenszel method, LR, log binomial regression (LBR), Zhang and Yu method, Poisson/Cox regression, modified Poisson/Cox regression, marginal probability method, COPY method, inverse probability of treatment weighted LBR, and Diaz-Quijano method. Our proposed modified Diaz-Quijano (MDQ) method provides RR and its confidence interval similar to those estimated by modified Poisson/Cox and LBRs. The proposed modifications in Zhang and Yu method provides better estimate of RR and its standard error as compared to Zhang and Yu method in a variety of situations with prevalent outcome. The MDQ method can be used easily to estimate the RR and its confidence interval in the studies which require reporting of RRs. Regression models which directly provide the estimate of RR without convergence problems such as the MDQ method and modified Poisson/Cox regression should be preferred.     

An Integrated Approach for Modeling Sustainability Risks in Freight Transportation Systems

Integrating sustainability into freight transportation systems (FTSs) is a complex and challenging task due to the sheer number of inherent sustainability risks. Sustainability risks disrupt the economic, social and environmental objectives of freight operations and act as impediments in the development of sustainable freight transportation systems. The area of sustainability risk management is still unexplored and immature in the operational research domain. This study addresses these research gaps and contributes in a threefold manner. First, a total of 36 potential sustainability risks related to FTSs are identified and uniquely classified into seven categories using a rigourous approach. Second, the research proposes two prominent perspectives, namely, ontological and epistemological perspectives to understand risks and develops a novel framework for managing sustainability risks in FTSs. Third, a novel approach by integrating fuzzy evidential reasoning algorithm (FERA) with expected utility theory is developed to quantitatively model and profile sustainability risk for different risk preferences, namely, risk-averse, risk-neutral, and risk-taking scenarios. The proposed FERA is a flexible and robust approach, which transforms the experts’ inputs into belief structures and aggregates them using the evidence combination rule proposed in Dempster–Shafer theory to overcome the problem of imprecise results caused by average scoring in existing models. A sensitivity analysis is conducted to demonstrate the robustness of the proposed model. Unlike conventional perception, our study suggests that most of the high priority sustainability risks are behaviorally and socially induced rather than financially driven. The results provide significant managerial implications including a focus on skills development, and on social and behavioral dimensions while managing risks in FTSs.     

Adaptive Tactical Pricing in Multi‐Agent Supply Chain Markets Using Economic Regimes

In today's complex and dynamic supply chain markets, information systems are essential for effective supply chain management. Complex decision making processes on strategic, tactical, and operational levels require substantial timely support in order to contribute to organizations' agility. Consequently, there is a need for sophisticated dynamic product pricing mechanisms that can adapt quickly to changing market conditions and competitors' strategies. We propose a two‐layered machine learning approach to compute tactical pricing decisions in real time. The first layer estimates prevailing economic conditions—economic regimes—identifying and predicting current and future market conditions. In the second layer, we train a neural network for each regime to estimate price distributions in real time using available information. The neural networks compute offer acceptance probabilities from a tactical perspective to meet desired sales quotas. We validate our approach in the trading agent competition for supply chain management. When competing against the world's leading agents, the performance of our system significantly improves compared to using only economic regimes to predict prices. Profits increase significantly even though the prices and sales volume do not change significantly. Instead, tactical pricing results in a more efficient sales strategy by reducing both finished goods and components inventory costs.     

Bayesian Estimation of Parameters and Comparison of Shared Gamma Frailty Models

In this article, we introduce shared gamma frailty models with three different baseline distributions namely, Weibull, generalized exponential and exponential power distributions. We develop Bayesian estimation procedure using Markov Chain Monte Carlo(MCMC) technique to estimate the parameters involved in these models. We present a simulation study to compare the true values of the parameters with the estimated values. Also we apply these three models to a real life bivariate survival dataset of McGilchrist and Aisbett (1991 McGilchrist, C. A. and Aisbett, C. W. 1991. Regression with frailty in survival analysis. Biometrics, 47: 461–466. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) related to kidney infection data and a better model is suggested for the data.