Fuel type based vehicles choice

The aim of the paper is to analyse the researches performed so far on vehicle choice according to the fuel type. There are different reasons to be interested in this theme. Among the most relevant we recall the following: a. increasing costs of conventional fuel; b. development of new fuel types; c. different fuel efficiency; d. higher productivity standards, due to crisis of car corporations; e. Italy’s car fleet has a 30% of vehicles that are ten years or older and also by a strong preference towards buying gasoline and diesel fuelled vehicles. The paper proposes a critical analysis of vehicle choice analysis based on fuel type (e.g. gasoline/diesel, CNG and hybrid). A significant number of studies are centred on the consumer. As noted by Achtnicht (2008) the choice depends on the person’s age, gender and level of schooling. Other authors have inquired the actual gap between the performance of conventional fuels (diesel/gasoline) and that of alternative fuel (hybrid). The lack of a diffused network of refuelling stations, particularly with reference to the CNG (compressed natural gas), has also been highlighted by Achtnicht, Buhler e Hermeling (2009). Several electrical car market development researches, such as the Salerno’s and Zito’s ones (2004), have stressed its high purchasing price and its maintenance costs. We will consider, for the different studies, the methodologies used by the authors, their specific area of research, the results obtained, the criticalities, and eventually the trends and developments.

Bayesian Fused Lasso regression for dynamic binary networks

We propose a multinomial logistic regression model for link prediction in a time series of directed binary networks. To account for the dynamic nature of the data we employ a dynamic model for the model parameters that is strongly connected with the fused lasso penalty. In addition to promoting sparseness, this prior allows us to explore the presence of change points in the structure of the network. We introduce fast computational algorithms for estimation and prediction using both optimization and Bayesian approaches. The performance of the model is illustrated using simulated data and data from a financial trading network in the NYMEX natural gas futures market. Supplementary material containing the trading network data set and code to implement the algorithms is available online

Bayesian semiparametric analysis for two-phase studies of gene-environment interaction

The two-phase sampling design is a cost-efficient way of collecting expensive covariate information on a judiciously selected subsample. It is natural to apply such a strategy for collecting genetic data in a subsample enriched for exposure to environmental factors for gene-environment interaction (G x E) analysis. In this paper, we consider two-phase studies of G x E interaction where phase I data are available on exposure, covariates and disease status. Stratified sampling is done to prioritize individuals for genotyping at phase II conditional on disease and exposure. We consider a Bayesian analysis based on the joint retrospective likelihood of phases I and II data. We address several important statistical issues: (i) we consider a model with multiple genes, environmental factors and their pairwise interactions. We employ a Bayesian variable selection algorithm to reduce the dimensionality of this potentially high-dimensional model; (ii) we use the assumption of gene-gene and gene-environment independence to trade off between bias and efficiency for estimating the interaction parameters through use of hierarchical priors reflecting this assumption; (iii) we posit a flexible model for the joint distribution of the phase I categorical variables using the nonparametric Bayes construction of Dunson and Xing [J. Amer. Statist. Assoc. 104 (2009) 1042-1051].Comment: Published in at http://dx.doi.org/10.1214/12-AOAS599 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Frequent sampling in discrete choice

Sampling;Choice Theory;Models;econometrics

Designs efficiency for non-market valuation with choice modelling: how to measure it, what to report and why

We review the basic principles for the evaluation of design efficiency in discrete choice modelling with a focus on efficiency of WTP estimates from the multinomial logit model. The discussion is developed under the realistic assumption that researchers can plausibly define a prior on the utility coefficients. Some new measures of design performance in applied studies are proposed and their rationale discussed. An empirical example based on the generation and comparison of fifteen separate designs from a common set of assumptions illustrates the relevant considerations to the context of non-market valuation, with particular emphasis placed on C-efficiency. Conclusions are drawn for the practice of reporting in non-market valuation and for future work on design research