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11.
Carlos E. Alciaturi Thais Montero Carlos De La Cruz Marcos E. Escobar 《Analytica chimica acta》1997,340(1-3):233-240
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. 相似文献
12.
13.
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. 相似文献
14.
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. 相似文献
15.
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. 相似文献
16.
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. 相似文献
17.
一种区间数的因子分析技术及其在证券市场中的应用 总被引:1,自引:0,他引:1
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。 相似文献
18.
Joel Tellinghuisen 《Journal of Molecular Spectroscopy》2004,226(2):137-145
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. 相似文献
19.
Preparation and Molecular Structures of Oligofunctional Dirhenium Carbonyl Derivatives from Dirhenium Nonacarbonylphosphane Starting with dirheniumdecacarbonyl, one CO-Ligand was eliminated oxidatively and substituted by the labile acetonitrile ligand. As an intermediate eq-Re2(CO)9NCCH3 was received. The reaction of this labilised carbonyl with tris(trimethylsilyl)phosphine and subsequent methanolysis gave ax-Re2(CO)9PH3, which was isolated and characterized for the first time. Photochemical and thermal reaction of ax-Re2(CO)9PH3 led to the new bi- and trinuclear complexes Re2(μ-H)(μ3-PHRe(CO)5)(CO)8, Re2(μ-PH2)2(CO)8 and Re2(μ-H) · (μ-PH2)(CO)8, which were characterized by IR-, 1H- and 31P-NMR spectroscopy. The structures of ax-Re2(CO)9PH3, Re2(μ-H)(μ3-PHRe(CO)5)(CO)8 and Re2(μ-PH2)2(CO)8 were confirmed by single-crystal X-ray analysis. ax-Re2(CO)9PH3 has a very short Re? P bond length of 228(2) pm. 相似文献
20.
Véronique Cariou 《Computational Statistics》2006,21(2):325-341
Summary Several data can be presented as interval curves where intervals reflect a within variability. In particular, this representation
is well adapted for load profiles, which depict the electricity consumption of a class of customers. Electricity load profiling
consists in assigning a daily load curve to a customer based on their characteristics such as energy requirement. Within the
load profiling scope, this paper investigates the extension of multivariate regression trees to the case of interval dependent
(or response) variables. The tree method aims at setting up simultaneously load profiles and their assignment rules based
on independent variables. The extension of multivariate regression trees to interval responses is detailed and a global approach
is defined. It consists in a first stage of a dimension reduction of the interval response variables. Thereafter, the extension
of the tree method is applied to the first principal interval components. Outputs are the classes of the interval curves where
each class is characterized both by an interval load profile (e.g. the class prototype) and an assignment rule based on the
independent variables. 相似文献