Estimating of bootstrap confidence intervals for freight transport matrices

Freight transport studies require, as a preliminary step, a survey to be conducted on a sample of the universe of agents, vehicles and/or companies of the transportation system. The statistical reliability of the data determines the goodness of the outcomes and conclusions that can be inferred from the analyses and models generated. The methodology contained herein, based on bootstrapping techniques, allows us to generate the confidence intervals of origin-destination pairs defined by each cell of the matrix derived from a freight transport survey. To address this study a data set from a statistically reliable freight transport study conducted in Spain at the level of multi-province inter-regions has been used.Public Road Agency of the Andalusian Regional Government (AOP-JA, Spain Project G-GI3000/IDII)EU FEDE

Estimating of bootstrap confidence intervals for freight transport matrices

Freight transport studies require, as a preliminary step, a survey to be conducted on a sample of the universe of agents, vehicles and/or companies of the transportation system. The statistical reliability of the data determines the goodness of the outcomes and conclusions that can be inferred from the analyses and models generated. The methodology contained herein, based on bootstrapping techniques, allows us to generate the confidence intervals of origin-destination pairs defined by each cell of the matrix derived from a freight transport survey. To address this study a data set from a statistically reliable freight transport study conducted in Spain at the level of multi-province inter-regions has been used.Public Road Agency of the Andalusian Regional Government (AOP-JA, Spain Project G-GI3000/IDII)EU FEDE

Robust confidence regions for multinomial probabilities

Generally, confidence regions for the probabilities of a multinomial population are constructed based on the Pearson chi(2) statistic. Morales et al. (Bootstrap confidence regions in multinomial sampling. Appl Math Comput. 2004;155:295-315) considered the bootstrap and asymptotic confidence regions based on a broader family of test statistics known as power-divergence test statistics. In this study, we extend their work and propose penalized power-divergence test statistics-based confidence regions. We only consider small sample sizes where asymptotic properties fail and alternative methods are needed. Both bootstrap and asymptotic confidence regions are constructed. We consider the percentile and the bias corrected and accelerated bootstrap confidence regions. The latter confidence region has not been studied previously for the power-divergence statistics much less for the penalized ones. Designed simulation studies are carried out to calculate average coverage probabilities. Mean absolute deviation between actual and nominal coverage probabilities is used to compare the proposed confidence regions