Lean Trees—A General Approach for Improving Performance of Lattice Models for Option Pricing

The well-known binomial and trinomial tree models for option pricing are examined from the point of view of numerical efficiency. Common lattices use a large part of time resources for calculations which are almost irrelevant for the solution. To avoid this waste of resources, the tree is reduced to a lean form which yields the same order of convergence, but with a reduction of numerical effort. In numerical tests it is shown that the proposed method leads to a significant improvement in real calculation time without loss of accuracy for a broad class of derivatives.     

观赏草在上海城市绿地中的应用 调查研究

通过对上海市25个城市绿地的实地调查,总结归纳出在上海城市绿地中应用的观赏草种类。通过对实地调研获得的实景照片、走访记录、观测数据等进行统计分析,总结得出观赏草在上海城市绿地中的应用情况。结合前人经验,利用灰色关联度分析法对调查的观赏草进行综合评价和排序,为观赏草的合理应用提供支持     

On the determinants of happiness: a classification and regression tree (CART) approach

This article studies the determinants of the individual’s subjective well-being by applying a classification and regression tree (CART) analysis to data from the Survey on Household Income and Wealth (SHIW) provided by the Bank of Italy. The results support the primary importance of economic conditions but show that their effect is heterogeneously dependent on other individual characteristics.     

树文化之再思考

“树化”是指以树为载体的社会化现象。树化有宗教形态、社会形态、民俗形态、美学形态等不同形态之分。促进树与化的互动发展,实现树与化的共生共荣,是我们应该弘扬的先进树化的发展方向。     

黑龙江省乡土彩叶植物应用现状及筛选评价

对彩叶树种在城市园林绿化中的研究进展、应用状况进行基本概述,了解目前国内彩叶树种在园林绿化中的发展趋势;同时对黑龙江省城市园林绿化中彩叶树种的应用状况进行分析,明确了黑龙江省彩叶树种在品种应用方面的匮乏,进而结合我省乡土树种抗性强、变异小、易存活等特点开发本土彩叶植物资源,结合层次分析方法筛选出具有较高观赏价值的秋叶观...     

Data accuracy's impact on segmentation performance: Benchmarking RFM analysis,logistic regression,and decision trees

Companies greatly benefit from knowing how problems with data quality influence the performance of segmentation techniques and which techniques are more robust to these problems than others. This study investigates the influence of problems with data accuracy – an important dimension of data quality – on three prominent segmentation techniques for direct marketing: RFM (recency, frequency, and monetary value) analysis, logistic regression, and decision trees. For two real-life direct marketing data sets analyzed, the results demonstrate that (1) under optimal data accuracy, decision trees are preferred over RFM analysis and logistic regression; (2) the introduction of data accuracy problems deteriorates the performance of all three segmentation techniques; and (3) as data becomes less accurate, decision trees retain superior to logistic regression and RFM analysis. Overall, this study recommends the use of decision trees in the context of customer segmentation for direct marketing, even under the suspicion of data accuracy problems.     

A graphically based machine learning approach to predict secondary schools performance in Tunisia

The main purpose of this paper is to identify the key factors that impact schools' academic performance and to explore their relationships through a two-stage analysis based on a sample of Tunisian secondary schools. In the first stage, we use the Directional Distance Function approach (DDF) to deal with undesirable outputs. The DDF is estimated using Data Envelopment Analysis method (DEA). In the second stage we apply machine-learning approaches (regression trees and random forests) to identify and visualize variables that are associated with a high school performance. The data is extracted from the Program for International Student Assessment (PISA) 2012 survey. The first stage analysis shows that almost 22% of Tunisian schools are efficient and that they could improve their students’ educational performance by 15.6% while using the same level of resources. Regression trees findings indicate that the most important factors associated with higher performance are school size, competition, class size, parental pressure and proportion of girls. Only, school location appears with no impact on school efficiency. Random forests algorithm outcomes display that proportion of girls at school and school size have the most powerful impact on the predictive accuracy of our model and hence could more influence school efficiency. The findings disclose also the high non-linearity of the relationships between these key factors and school performance and reveal the importance of modeling their interactions in influencing efficiency scores.