An efficient sampling method for stochastic inverse problems

A general framework is developed to treat inverse problems with parameters that are random fields. It involves a sampling method that exploits the sensitivity derivatives of the control variable with respect to the random parameters. As the sensitivity derivatives are computed only at the mean values of the relevant parameters, the related extra cost of the present method is a fraction of the total cost of the Monte Carlo method. The effectiveness of the method is demonstrated on an example problem governed by the Burgers equation with random viscosity. It is specifically shown that this method is two orders of magnitude more efficient compared to the conventional Monte Carlo method. In other words, for a given number of samples, the present method yields two orders of magnitude higher accuracy than its conventional counterpart.     

The prediction of coal properties using compressed infrared data from osculating polynomials

Reduction of the representation of infrared spectra from coal samples by osculating polynomials of degree nine is discussed. The reduced representation contains polynomial coefficients of order zero to four. Mathematical models of the original spectra are obtained by linear combination of the coefficients. These compressed models are statistically correlated to coal properties, namely, volatile matter, fixed carbon, ash content, heating value, hydrogen, carbon, sulphur, nitrogen, and maximum vitrinite reflectance, and the results are compared with those previously obtained from second derivatives of the same spectra. The use of compressed data, while giving slightly better correlations for some of the properties, has the advantage of requiring less computational time.     

热声驱动系统中的压力测量

介绍了自制的用于热声驱动脉管制冷装置的计算机压力波数据采集系统。该系统由硅压电压力传感器、数据采集卡、PC机和基于 Labview软件的图形化用户界面 GUI( Graphical User In-terface)组成。试验证明 ,它具有结构简单、高效精确、操作简便等特点 ,在捕捉被测系统内的动态压力变化方面具有优势 ,不但能很好地满足热声驱动器的压力测量要求 ,而且亦可移植到任何类似的测量气体压力的场合     

Improved customer choice predictions using ensemble methods

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard approach for this problem. The ensemble models are found to improve upon individual decision trees and outperform logistic regression.     

Bivariate Free Knot Splines

We consider the least squares approximation of gridded 2D data by tensor product splines with free knots. The smoothing functional to be minimized—a generalization of the univariate Schoenberg functional—is chosen in such a way that the solution of the bivariate problem separates into the solution of a sequence of univariate problems in case of fixed knots. The resulting optimization problem is a constrained separable least squares problem with tensor product structure. Based on some ideas developed by the authors for the univariate case, an efficient method for solving the specially structured 2D problem is proposed, analyzed and tested on hand of some examples from the literature.     

Clustering Effect on the Statistical Estimation Accuracy of Distribution Density

The paper is devoted to statistical nonparametric estimation of multivariate distribution density. The influence of data pre-clustering on the estimation accuracy of multimodal density is analyzed by means of the Monte Carlo method. It is shown that the soft clustering is more advantageous than the hard one. While a moderate increase in the number of clusters also increases the calculation time, it considerably reduces the estimation error.     

Evaluation of Commodity Trading Advisors using fixed and variable and benchmark models

This paper examines the performance of Commodity Trading Advisors (CTAs) using fixed and variable benchmarking models. In order to avoid the troublesome passive and active commodity and managed futures benchmarks (indices) when examining the performance of CTAs, we innovate by using data envelopment analysis (DEA). Because this alternative class has non-linear returns due to long/short positions, and derivatives (i.e., dynamic trading strategies), DEA can alleviate the problems usually associated with these indices. The effectiveness of using benchmarking models in a DEA setting will provide investors with an alternative technique in assessing the performance and identifying efficient CTAs.     

基于EPP协议的便携机数据采集系统

设计了基于EPP协议的便携机数据采集系统 ,给出了技术解决方案。方案具有软硬件设计简捷、无需插卡等优点 ,可使外设工作于PC并行口的多种模式下。解决了线阵CCD图像采集系统与笔记本电脑之间的数据通讯 ,达到了便携数据采集的目的。     

一种区间数的因子分析技术及其在证券市场中的应用

传统的因子分析技术能够有效地对高维变量空间进行降维处理,但它对于样本空间却缺乏行之有效的降维效果.为了解决这一问题,一种针对大量样本数据、新的因子分析技术———区间数因子分析技术(intervaldatafactoranalysis,IFA)被提出并得到了迅速的发展。IFA方法对传统的数据概念做了本质性的扩张,运用'数据打包'的理念,对海量原始数据在不破坏其原有内在逻辑关系的前提下,可以进行变量和样本点维度的双重降维。本文详细阐述了区间数因子分析技术的原理,并以中国股票市场为案例研究背景,结果表明IFA分析技术对大规模多维数据系统做综合简化是十分有效的。     

A statistical study of the analysis of congested spectra by total spectrum fitting

Heavily overlapped, or congested spectra often display much structure but few individual “lines.” Methods have been devised for analyzing such spectra through nonlinear least-squares fitting of the intensity as a function of wavelength or wavenumber. Such total spectrum fitting (TSF) methods are examined statistically for a simple diatomic model and compared with the standard “measure-assign-fit” (MAF) approach in use since the dawn of spectroscopy. Monte Carlo computations on typically 1000 synthetic spectra at a time verify that the predictions of the variance-covariance matrix are reliable under many circumstances. However in regions where the P and R branches double up, the predicted standard errors in the key spectroscopic constants rise sharply and greatly exceed estimates from the Monte Carlo ensemble statistics. In the same regions, the MAF method actually gives better precision. However, for imperfectly overlapped R and P branches, the MAF standard errors are typically three times larger than the TSF values; moreover, the MAF statistical errors are dwarfed by bias. The TSF approach, while clearly superior in these tests, has a practical drawback: it, too, can give significant bias if the spectra are analyzed with an incorrect model, as illustrated here through calculations employing the wrong function to describe the spectral lineshape.