温州的垂直绿化植物资源

攀援植物具有生长迅速,又易于栽培管理等特点,是城镇进行垂直绿化的优质材料,可美化环境,改善小气候．据调查统计,温州地区有攀援植物40科235种（变种）左右．这些资源可用于墙面、棚架、栅栏等的绿化,但目前这类资源的利用还很低,尚有许多优良的攀援植物有待进一步开发利用．     

温州市区垂直绿化现状分析

据调查统计，温州市区有攀援植物18科32种（变种）。就总体而言，温州市区的街绿化现状还十分薄弱，主要表现为垂直绿化植物种类单一和垂直绿化意识谈薄。为了提高城市绿化面积，应利用攀援植物对墙面、阳台、棚架、栅栏等进行绿化。对此，本文对可用于垂直绿化的植物资源和如何进行垂直绿化作了初步探讨。     

Pair-Copula Bayesian Networks

Pair-copula Bayesian networks (PCBNs) are a novel class of multivariate statistical models, which combine the distributional flexibility of pair-copula constructions (PCCs) with the parsimony of conditional independence models associated with directed acyclic graphs (DAGs). We are first to provide generic algorithms for random sampling and likelihood inference in arbitrary PCBNs as well as for selecting orderings of the parents of the vertices in the underlying graphs. Model selection of the DAG is facilitated using a version of the well-known PC algorithm that is based on a novel test for conditional independence of random variables tailored to the PCC framework. A simulation study shows the PC algorithm’s high aptitude for structure estimation in non-Gaussian PCBNs. The proposed methods are finally applied to modeling financial return data. Supplementary materials for this article are available online.     

Generating random correlation matrices based on vines and extended onion method

We extend and improve two existing methods of generating random correlation matrices, the onion method of Ghosh and Henderson [S. Ghosh, S.G. Henderson, Behavior of the norta method for correlated random vector generation as the dimension increases, ACM Transactions on Modeling and Computer Simulation (TOMACS) 13 (3) (2003) 276–294] and the recently proposed method of Joe [H. Joe, Generating random correlation matrices based on partial correlations, Journal of Multivariate Analysis 97 (2006) 2177–2189] based on partial correlations. The latter is based on the so-called D-vine. We extend the methodology to any regular vine and study the relationship between the multiple correlation and partial correlations on a regular vine. We explain the onion method in terms of elliptical distributions and extend it to allow generating random correlation matrices from the same joint distribution as the vine method. The methods are compared in terms of time necessary to generate 5000 random correlation matrices of given dimensions.