Unified Noncrossing Multiple Quantile Regressions Tree

In this article, we consider the estimation problem of a tree model for multiple conditional quantile functions of the response. Using the generalized, unbiased interaction detection and estimation algorithm, the quantile regression tree (QRT) method has been developed to construct a tree model for an individual quantile function. However, QRT produces different tree models across quantile levels because it estimates several QRT models separately. Furthermore, the estimated quantile functions from QRT often cross each other and consequently violate the basic properties of quantiles. This undesirable phenomenon reduces prediction accuracy and makes it difficult to interpret the resulting tree models. To overcome such limitations, we propose the unified noncrossing multiple quantile regressions tree (UNQRT) method, which constructs a common tree structure across all interesting quantile levels for better data visualization and model interpretation. Furthermore, the UNQRT estimates noncrossing multiple quantile functions simultaneously by enforcing noncrossing constraints, resulting in the improvement of prediction accuracy. The numerical results are presented to demonstrate the competitive performance of the proposed UNQRT over QRT. Supplementary materials for this article are available online.     

Nonlife ratemaking and risk management with Bayesian generalized additive models for location,scale, and shape

Generalized additive models for location, scale and, shape define a flexible, semi-parametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the mean but also in other key parameters governing the claiming behavior, like the degree of residual heterogeneity or the no-claim probability. In this broader setting, the Negative Binomial regression with cell-specific heterogeneity and the zero-inflated Poisson regression with cell-specific additional probability mass at zero are applied to model claim frequencies. New models for claim severities that can be applied either per claim or aggregated per year are also presented. Bayesian inference is based on efficient Markov chain Monte Carlo simulation techniques and allows for the simultaneous estimation of linear effects as well as of possible nonlinear effects, spatial variations and interactions between risk factors within the data set. To illustrate the relevance of this approach, a detailed case study is proposed based on the Belgian motor insurance portfolio studied in Denuit and Lang (2004).     

槽式太阳能电站集热管热性能测试及回归分析

利用热平衡法对桑普生产的槽式太阳能电站集热管的热性能进行了实验测试。实验结果显示,集热管内金属吸热管温度为143.6℃、200.9℃、267.2℃和290.3℃时,集热管的漏热量为21.2 W/m、39.8 W/m、50 5 W/m、88.8 W/m。根据集热管漏热的热物理过程,提出了H=f(T_(abs),T_(amb))=aT_(abs)~4+bT_(amb)~4b+cT_(amb)形式的回归模型,并对所测试真空管漏热量进行了回归分析,得到漏热量的回归数学表达式H=9.376×10~(-10)T_(abs)~4-8.685×10~(-10)T_(amb)~4-2.309×10~(-7)T_(amb)将回归模型应用于美国国家可再生能源实验室所测试的两种集热管,回归值与实验值符合较好,说明所提出的回归模型较合理。     

Enabling reduced-order data-driven nonlinear identification and modeling through naïve elastic net regularization

This work discusses an improved method of reduced-order modeling for existing data-driven nonlinear identification techniques through the incorporation of naïve elastic net regularization. The data-driven methods considered for this study operate using basis functions to represent the observed nonlinearity. Elastic net regularization is used to minimize the number of non-zero coefficients, thus modifying the basis functions and providing a compact representation. The ability of the naïve elastic net to provide reduced-order nonlinear models that can both accurately fit various data sets and computationally simulate new responses is illustrated through studies considering both synthetic data and experimental data. In both cases, the results obtained with the naïve elastic net are shown to match or outperform those from other traditional methods.     

落叶松受雅氏落叶松尺蠖危害程度光谱检测

近年来蒙古国雅氏落叶松尺蠖灾害不断加剧，逐渐逼近大兴安岭地区，将威胁我国北方森林生态系统安全。以现代遥感监测方法替代传统检测方法，及早掌握该虫害发生发展规律对防控有重要意义。为快速、大范围遥感监测雅氏落叶松尺蠖灾害，利用光谱分析技术研究了该害虫危害下落叶松受害程度检测模型。通过实测健康和轻度、中度、重度受害落叶松光谱，计算与比较不同受害程度落叶松原始光谱和去除包络线光谱的敏感度，揭示光谱敏感波段及去除包络线光谱敏感性。然后对去除包络线光谱进行一阶导数变换获得光谱特征参数并分析其随受害程度的变化特征，构建基于CART(分类与回归树)算法的落叶松受害程度光谱检测模型。研究表明：去除包络线光谱敏感性比原始光谱更显著，尤其在480～520 nm(蓝边)、640～720 nm(红谷、红边)、1 416～1 500 nm(短波红外谷)等波段内光谱敏感度介于0.1～2.0，而且出现了敏感峰现象。随受害程度增加，去除包络线光谱敏感性增强趋势比原始光谱更明显；在蓝边波段上去除包络线光谱敏感峰位置向短波方向移动，即502 nm→490 nm，而在红谷及红边、短波红外谷等波段上光谱敏感峰位置向长波方向移动，即664 nm→672 nm和1 436 nm→1 448 nm；红谷位置和短波红外谷位置归一化反射率以及红谷和短波红外谷面积呈上升趋势。在蓝边与红边波段内去除包络线光谱一阶导数对受害程度有明显响应，出现了波峰现象。随害虫危害程度加剧红边位置蓝移(718 nm→700 nm)，红边斜率及面积和蓝边斜率及面积呈下降趋势。基于此，利用红边斜率、红谷位置和短波红外谷位置归一化反射率、红谷和短波红外谷面积、蓝边斜率及面积等去除包络线光谱特征参数构建的CART模型对落叶松受害程度有很好的检测能力。与多元线性回归模型相比，CART模型检测精度更高，其Kappa系数达0.875。研究结果对雅氏落叶松尺蠖灾害的防治有参考价值。     

Chiral recognition by enantioselective liquid chromatography: Mechanisms and modern chiral stationary phases

An overview of the state-of-the-art in LC enantiomer separation is presented. This tutorial review is mainly focused on mechanisms of chiral recognition and enantiomer distinction of popular chiral selectors and corresponding chiral stationary phases including discussions of thermodynamics, additivity principle of binding increments, site-selective thermodynamics, extrathermodynamic approaches, methods employed for the investigation of dominating intermolecular interactions and complex structures such as spectroscopic methods (IR, NMR), X-ray diffraction and computational methods. Modern chiral stationary phases are discussed with particular focus on those that are commercially available and broadly used. It is attempted to provide the reader with vivid images of molecular recognition mechanisms of selected chiral selector–selectand pairs on basis of solid-state X-ray crystal structures and simulated computer models, respectively. Such snapshot images illustrated in this communication unfortunately cannot account for the molecular dynamics of the real world, but are supposed to be helpful for the understanding. The exploding number of papers about applications of various chiral stationary phases in numerous fields of enantiomer separations is not covered systematically.     

Efficient ML Estimation of the Multivariate Normal Distribution from Incomplete Data

It is well known that the maximum likelihood estimates (MLEs) of a multivariate normal distribution from incomplete data with a monotone pattern have closed-form expressions and that the MLEs from incomplete data with a general missing-data pattern can be obtained using the Expectation-Maximization (EM) algorithm. This article gives closed-form expressions, analogous to the extension of the Bartlett decomposition, for both the MLEs of the parameters and the associated Fisher information matrix from incomplete data with a monotone missing-data pattern. For MLEs of the parameters from incomplete data with a general missing-data pattern, we implement EM and Expectation-Constrained-Maximization-Either (ECME), by augmenting the observed data into a complete monotone sample. We also provide a numerical example, which shows that the monotone EM (MEM) and monotone ECME (MECME) algorithms converge much faster than the EM algorithm.     

Explanation of Human Skin Color by Multiple Linear Regression Analysis Based on the Modified Lambert-Beer Law

Quantitative analysis of human skin color is needed in the medical and cosmetic fields. Because of the strong light scattering by biological tissues, however, analysis of skin color has not yet been fully successful. Human skin color is dominated by the colors of blood and melanin which are modified by scattering. Exposure of human skin to hot water or UV-ray changes its color because the absorbance spectrum of reflection from the human skin, and the changes in the absorbance spectra of reflection reflect the absorbance spectra of blood and melanin which are the absorption spectrum distorted by scattering. By applying the modified Lambert-Beer law, the absorbance spectrum of reflection from human skin can be expressed proportional to those distorted absorbance spectra of blood and melanin. Multiple linear regression analysis is successfully used to reproduce the absorbance spectrum of reflection from human skin from the distorted absorbance spectra of blood and melanin.     

Role of Hansen solubility parameters in solid phase extraction

The sorbent–eluent systems combined from eight polymeric sorbents and seven solvents as eluents were used for the extraction of phenol and its oxidation products from water samples. The individual interactions between sorbents, eluents and analytes were characterized by Hansen solubility parameters. Principal components analysis (PCA) was used for revealing the dominant interactions (dispersive, polar, and hydrogen bonding type) in sorbent–analyte–eluent systems. The importance of solubility parameters was also determined by a novel procedure based on sum of ranking differences (SRD). Although PCA and ranking by SRD are based on different principles and calculations, they have provided very similar results. The recovery in a given system has been predicted from the magnitudes of mutual interactions (sorbent–analyte, sorbent–eluent, analyte–eluent) by multiple linear regression.     

Principles and applications of wavelet transformation to chemometrics

This paper aims at serving two purposes: firstly, it gives a quick summary of aspects and properties of wavelets and wavelet transforms which are needed in order to understand how to (pre-)process data from spectrometry with wavelet methods. Secondly, it shows on a typical example (wheat NIR spectra) how wavelet transforms can be used in order to extract quantitative information. In contrast to other approaches in the literature, we use special types of wavelets which allow analysing finitely extended signals without introducing artifacts near the boundaries, and we introduce a new way of wavelet coefficient regression in order to build our chemometrical models.