基于最小二乘支持向量机回归的背景偏振光谱二向反射分布建模分析

详细分析了土壤背景偏振光谱二向反射分布函数与探测角及探测方位角之间关系.提出了基于最小二乘支持向量机回归的偏振光谱二向反射分布函数建模,将有限实验观测条件下测量得到的少数偏振光谱二向反射分布函数扩展到2π空间范围内任意入射及观测条件.通过模型结果和实验结果分析比较,表明该模型能很好地满足准确度要求.     

Acoustic detection and classification of river boats

We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces false alarms rate. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real time detection while minimizing the false alarms rate.     

取代苯甲腈类有机污染物水解行为的研究

研究了10种二取代苯甲腈类有机污染物的水解行为,用反相高效液相色谱测定了不同pH值和不同溫度下的水解速度常数。结果表明,含羟基的化合物在酸,碱和中性水介质中均不易水解;其它化合物仅在碱性介质中水解,其碱性催化水解速度常数与Hammett取代基常数以及酸常数之间呈近似线性关系。相关方程为:lgk_B=1.45σ-0.859和lgk_B=-1.34pK_(?)-4.83。     

Quantile regression for modelling distributions of profit and loss

Quantile regression is applied in two retail credit risk assessment exercises exemplifying the power of the technique to account for the diverse distributions that arise in the financial service industry. The first application is to predict loss given default for secured loans, in particular retail mortgages. This is an asymmetric process since where the security (such as a property) value exceeds the loan balance the banks cannot retain the profit, whereas when the security does not cover the value of the defaulting loan then the bank realises a loss. In the light of this asymmetry it becomes apparent that estimating the low tail of the house value is much more relevant for estimating likely losses than estimates of the average value where in most cases no loss is realised. In our application quantile regression is used to estimate the distribution of property values realised on repossession that is then used to calculate loss given default estimates. An illustration is given for a mortgage portfolio from a European mortgage lender. A second application is to revenue modelling. While credit issuing organisations have access to large databases, they also build models to assess the likely effects of new strategies for which, by definition, there is no existing data. Certain strategies are aimed at increasing the revenue stream or decreasing the risk in specific market segments. Using a simple artificial revenue model, quantile regression is applied to elucidate the details of subsets of accounts, such as the least profitable, as predicted from their covariates. The application uses standard linear and kernel smoothed quantile regression.     

非对称相异性矩阵的一种非度量多维尺度变换

本文将Okada ＆ Imaizumi等的模型加以推广,提出了一种用于处理非对称相异性矩阵的非度量多维尺度变换新方法.在模型中,我们假定每个研究对象可以表示为Minkowski度量空间中的一个点和一个超球面,超球面的半径揭示了相应研究对象的非对称性.文中我们给出了一种计算点坐标及球半径的算法.该算法使用了代数方法,比原来的方法收敛速度快,节省计算时间.最后给出了一个数值例子.     

The Projection onto a Direct Product of Convex Cones

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Reliability of calculating the cepstral peak without linear regression analysis

Measures of cepstral peak prominence, using the smoothing algorithm and linear regression analysis software developed by Hillenbrand, have been shown to be reliable predictors of dysphonia in voice samples.(1-4) Recently, the Computerized Speech Laboratory [(CSL) Kay Elemetrics, Pinebrook, New Jersey] has introduced cepstral analysis as a component of that software package. The cepstral peak, in this instance, is calculated by the voice clinician analyzing the phonatory sample by subtracting the value of the peak from the apparent baseline signal. This study compares the ability of cepstral peak values calculated from the CSL software to predict dysphonia reliably with that of the values produced by the smoothing algorithm and linear regression analysis of Hillenbrand. The results of this study show that linear regression analysis is an important step in calculating the cepstral peak prominence, thus limiting the usefulness of software programs that do not employ this step.     

A mesh simplification strategy for a spatial regression analysis over the cortical surface of the brain

We present a new mesh simplification technique developed for a statistical analysis of a large data set distributed on a generic complex surface, topologically equivalent to a sphere. In particular, we focus on an application to cortical surface thickness data. The aim of this approach is to produce a simplified mesh which does not distort the original data distribution so that the statistical estimates computed over the new mesh exhibit good inferential properties. To do this, we propose an iterative technique that, for each iteration, contracts the edge of the mesh with the lowest value of a cost function. This cost function takes into account both the geometry of the surface and the distribution of the data on it. After the data are associated with the simplified mesh, they are analyzed via a spatial regression model for non-planar domains. In particular, we resort to a penalized regression method that first conformally maps the simplified cortical surface mesh into a planar region. Then, existing planar spatial smoothing techniques are extended to non-planar domains by suitably including the flattening phase. The effectiveness of the entire process is numerically demonstrated via a simulation study and an application to cortical surface thickness data.     

Prediction of work-related accidents according to working conditions using support vector machines

Support vector machines (SVMs), which are a kind of statistical learning methods, were applied in this research work to predict occupational accidents with success. In the first place, semi-parametric principal component analysis (SPPCA) was used in order to perform a dimensional reduction, but no satisfactory results were obtained. Next, a dimensional reduction was carried out using an innovative and intelligent computing regression algorithm known as multivariate adaptive regression splines (MARS) model with good results. The variables selected as important by the previous MARS model were taken as input variables for a SVM model. This SVM technique was able to classify, according to their working conditions, those workers that have suffered a work-related accident in the last 12 months and those that have not. SVM technique does not over-fit the experimental data and gives place to a better performance than back-propagation neural network models. Finally, the results and conclusions of this study are presented.     

Least squares estimators of the regression function with twice censored data

We propose least squares estimators of E(Y/X=x) for Y censored on the right by R and min(Y,R) left censored. We establish their convergence in the L₂-norm. This work extends a known result in the context of right censoring.