THE PERFORMANCE OF LEAST SQUARES AND ROBUST REGRESSION IN THE CALIBRATION OF ANALYTICAL METHODS UNDER NON-NORMAL NOISE DISTRIBUTIONS

By means of Monte Carlo simulations a comparison has been made between ordinary least squaresregression and robust regression. The robust regression procedure is based on the Huber estimate and iscomputed by means of the iteratively reweighted least squares algorithm. The performance of bothprocedures has been evaluated for estimation of the parameters of a calibration function and fordetermination of the concentration of unknown samples. The influence of the distributionalcharacteristics skewness and kurtosis has been studied, and the number of measurements used forconstructing the calibration curve has also been taken into account, Under certain conditions robustregression offers an advantage over least squares regression.     

辽宁省建筑业与经济增长关系的实证分析

建筑业是国民经济和社会发展的基础产业,为衡量一个国家和地区工业化水平的重要标志之一。近年来,中国建筑业占国民生产总值的比重一直保持在6%左右,且有逐年增长的趋势。在宏观分析全国建筑业发展现状的基础上,着重分析辽宁省建筑业发展的现状和走向,然后采用逐步回归分析方法和回归模型对辽宁省2001～2009年建筑业与经济增长的关系进行实证研究。认为辽宁省建筑业与经济增长的依存关系显著,即建筑业每增加1元可以带动全省GDP增长5.809元,然后从振兴辽宁老工业基地和全省"十二五"发展规划、建筑业的支柱产业地位、完善市场运行体制及拓宽建筑业发展空间等几个方面,提出辽宁省建筑业可持续发展的政策建议。     

SHORT COMMUNICATION THE USE OF TOPOLOGICAL MODELS IN ANALYTICAL CHEMISTRY

In the present paper,the possible analytical applications of two topological models,the DARC modeland the group contribution model,are discussed.Both models are applied to obtain calibration laws,which relate UV and IR characteristics with the chemical structure of ethylene oxide condensates.The group contribution model is also applied to determine the contribution of each part of thedifferent compounds involved in a chemical interaction process,having established the sensitizationparameters of benzodiazepines and anionic surfactants from the micellar enhancement fluorescence.     

基于栅格数字高程模型提取特征地貌技术研究

本文对近年来基于栅格数字高程模型提取特征地貌技术进行了详细的研究,认为该技术的关键在于两个方面：一是如何定义地貌形态结构,二是提取算法的设计。本文提出了基于地貌学角度来定义地貌形态结构的方法,利用有限个数的形态要素的空间组合和对比分析来获取特征地貌,并可以对各种特征地貌形态进行符合物理意义的改进。     

SHORT COMMUNICATION PLS FITS CLOSER THAN PCR

PLS1 regression is generally viewed as lying in between PCR and OLS regression.Proof is given thatthe coefficient of determination,R~2,for a PLS multivariate calibration model is at least as high as thatfor a PCR model with the same number of components.It appears that PLS can be linked to acorrelation-weighted polynomial regression of a constant response on the eigenvalues of the covariancematrix of the predictor variables.     

GENERALIZED SIMULATED ANNEALING FOR CALIBRATION SAMPLE SELECTION FROM AN EXISTING SET AND ORTHOGONALIZATION OF UNDESIGNED EXPERIMENTS

ealibrared.The diseussion in this PaPer foeuses on near一infrared(NIR)sPeetroseoPy as the examPle instrument.However,the Proeedures Presented are aPPlieable tomost methods of instrumental analysis.Essentially,ealibration eonsists of assembling a seriesof samPles eontaining the analyte or analytes at     

SPLITTING OF CALIBRATION DATA BY CLUSTER ANALYSIS

from eorresponding valuesof x.The most eommon aPProaeh to this Problem 15 linear regression(or ealibration),but1 inear methods are usually best suited for quite limited regions alld are not generallyaPPlieable.If a linear fit 15 not satisfactory,alternative aPProaehes are non一linear regression,non一Parametrie regression,transformations and sPlitting of the data into subgrouPs     

边界条件对曲流发育影响的过程响应模型实验研究

基于系统论模型化原理及地貌演化类比性法则的过程响应模型,有利于研究河型演化,河道过程及控制因素的作用。运用该模型所进行的边界条件对曲流发育影响的实验表明,河漫滩物质结构及河床上的抗蚀露头对曲流发育具有控制作用。     

GIS与土壤溶质运移模型结合研究进展

地理信息系统对土壤溶质模型研究而言，使用者的要求与GIS所能提供的功能之间，还存在巨大的差距。出于对环境问题的关注，土壤中水分及溶质运移的规律及其对环境的影响，成为当前研究的热点。随着对土壤溶质运移模型研究的深入，一方面由于田间土壤特性具有很大的空间变异；另一方面，在实际应用中，往往溶质运移的宏观特征而非微观特征，具有更重要的意义。将溶质运移模型与GIS技术结合，定量研究空间尺度的溶质运移，成为溶质运移研究的必然发展趋势。由于计算机软硬件技术的发展，GIS正处于一个高速发展的时期。将GIS与溶质运移模型相结合，或者进一步，以GIS理论和技术为基础，建立基于GIS的溶质运移模型，将对溶质运移的规律及其对环境的影响，做出更深入和准确的描述。     

香港降水的短期气候预测

利用正规化周期回归分析方法和门限自回归理论对香港1853-1995年月降水资料进行了建模和拟合，并对1996年进行了预报试验，正规化周期回归拟合效果较好，对香港7个时距的试报准确率为71%，建立了7个时距的10年滑动平均序列的门限自回归模型，模型对序列拟合效果较理想，预报误差较小，试报准确率主国100%。     

裸地蒸发过程的数值模拟

本文以能量平衡为基础,研究了裸地蒸发过程,并提出一个用Fonran语言编写的,在IBM-PC微机上通过的裸地蒸发过程的模拟程序。这一程序能根据地表红外温度或辐射资料计算裸地蒸发量,并分析能量分配过程与土壤中的含水量、温度分布。初步的田间试验说明,计算值与实测值是比较一致的。     

THE EFFECT OF MEAN CENTERING ON PREDICTION IN MULTIVARIATE CALIBRATION

Traditionally,one form of preprocessing in multivariate calibration methods such as principal componentregression and partial least squares is mean centering the independent variables(responses)and thedependent variables(concentrations).However,upon examination of the statistical issue of errorpropagation in multivariate calibration,it was found that mean centering is not advised for some datastructures.In this paper it is shown that for response data which(i)vary linearly with concentration,(ii)have no baseline(when there is a component with a non-zero response that does not change inconcentration)and(iii)have no closure in the concentrations(for each sample the concentrations of allcomponents add to a constant,e.g.100%)it is better not to mean center the calibration data.That is,the prediction errors as evaluated by a root mean square error statistic will be smaller for a model madewith the raw data than a model made with mean-centered data.With simulated data relativeimprovements ranging from 1% to 13% were observed depending on the amount of error in thecalibration concentrations and responses.     

RECENT DEVELOPMENTS IN MULTIVARIATE CALIBRATION

With the goal of understanding global chemical processes,environmental chemists have some of the mostcomplex sample analysis problems.Multivariate calibration is a tool that can be applied successfully inmany situations where traditional univariate analyses cannot.The purpose of this paper is to reviewmultivariate calibration,with an emphasis being placed on the developments in recent years.The inverseand classical models are discussed briefly,with the main emphasis on the biased calibration methods.Principal component regression(PCR)and partial least squares(PLS)are discussed,along with methodsfor quantitative and qualitative validation of the calibration models.Non-linear PCR,non-linear PLSand locally weighted regression are presented as calibration methods for non-linear data.Finally,calibration techniques using a matrix of data per sample(second-order calibration)are discussed briefly.     

USE OF THE KALMAN FILTER FOR MULTIVARIATE CALIBRATION IN A REAL SYSTEM AND ITS COMPARISON WITH CLS AND PURE COMPONENT CALIBRATION METHODS

The usefulness of the Kalman filter as an algorithm for calibration in a real system is shown. Results arecompared with classical least squares and pure component calibration. The prediction of four prioritypollutant chlorophenols in binary, ternary and quaternary mixtures was also carried out by Kalmanfiltering. The condition number, standard deviation and prediction error have been employed to choosethe most suitable wavelength range. Comparison of the standard error of prediction in the validation setshows significant differences between the evaluated chlorophenols, the best results being obtained withKalman multivariate calibration.     

THREE-WAY METHODS FOR THE CALIBRATION OF CHROMATOGRAPHIC SYSTEMS:COMPARING PARAFAC AND THREE-WAY PLS

For the calibration of chromatographic systems,different methods can be used.One class of methodsutilizes three-way approaches.The calibration problem is stated in such a way that the decompositionof a three-way array can serve for the prediction of retention on new stationary phases.Two three-way approaches are presented:the Unfold-PCA and PARAFAC models.The theory ofboth methods is presented and the differences are highlighted,the main difference being that PARAFACis a trilinear decomposition whereas Unfold-PCA is not.Both three-way methods are evaluated on asmall data set consisting of retention measurements of eight solutes at six mobile phase compositions onsix stationary phases.The differences in performance of the two models are minor,For calibration purposes,two variants of the methods are discussed:three-way PLS and an extensionof PARAFAC.Again the theory and differences between the two methods are explained.The predictiveperformance of the two methods is compared using the same data set as earlier.The differences inpredictive performance,however,are minor.Both methods are capable of predicting 98% of thevariation in the test sets.Yet,there are other considerations when comparing methods than predictiveperformance,e.g.the quality of the predictions.     

A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules

Cellular automata (CA) have emerged as a primary tool for urban growth modeling due to its simplicity, transparency, and ease of implementation. Sensitivity analysis is an important component in CA modeling for a better understanding of errors or uncertainties and their propagation. Most studies on sensitivity analyses in urban CA modeling focus on specific component such as neighborhood configuration or stochastic perturbation. However, sensitivity analysis of transition rules, which is one of the core components in CA models, has not been systematically done. This article proposes a systematic sensitivity analysis of major operational components in urban CA modeling using a stepwise comparison approach. After obtaining transition rules, three stages (i.e. static calibration of transition rules, dynamic evolution with varied time steps, and incorporation with stochastic perturbation) are designed to facilitate a comprehensive analysis. This scheme implemented with a case study in Guangzhou City (China) reveals that gaps in performance from static calibration with different transition rules can be reduced when dynamic evolution is considered. Moreover, the degree of stochastic perturbation is closely related to obtain urban morphology. However, a more realistic (i.e. fragmented) urban landscape is achieved at the cost of decreasing pixel-based accuracy in this study. Thus, a trade-off between pixel-based and pattern-based comparisons should be balanced in practical urban modeling. Finally, experimental results illustrate that models for transition rules extraction with good quality can do an assistance for urban modeling through reducing errors and uncertainty range. Additionally, ensemble methods can feasibly improve the performance of CA models when coupled with nonparametric models (i.e. classification and regression tree).     

A TEST OF THREE FITTING CRITERIA FOR MULTIRESPONSE NON-LINEAR MODELING

This work evaluates objective functions for multiresponse non-linear modeling using computersimulations.Tests are performed under a variety of signal-to-noise ratios and noise variance-covariancestructures.The standard error of prediction for the model parameters,computed from 50 trials,is usedfor performance comparisons.The full rank and rank-deficient problems are considered.For the fullrank problem one model was investigated,a first-order two-step consecutive reaction model,and twoobjective functions were considered,the total sum of squares and the determinant criterion.Nodistinction could be made between the two objective functions for this model.For the rank-deficient case two models were investigated,a first-order two-step consecutive reactionas in the full rank case,and a pH titration model described by the Henderson-Hasselbalch equation.Three objective functions were investigated for the rank-deficient case,the total sum of squares,aweighted total sum of squares and the determinant criterion.The total sum of squares was found toperform poorly under all conditions tested compared to the weighted total sum of squares and thedeterminant criterion.The determinant criterion was found to perform much better than the other twocriteria when the data have a combination of a low signal-to-noise ratio and high variance-covariancenoise structure.     

《区域生态环境评价技术》实验课教学模式探索

《区域生态环境评价技术》课程是本科生专业必修课,于2007学年列入专业系统实验的教学计划。该课程旨在利用现代计算机新技术,学会和掌握区域生态环境的评价技术及方法。通过几年来的课程建设和结合该实验课教学的实际情况,研究探索《区域生态环境评价技术》实验课教学的新模式,探讨实验教学改革的新思路。将相关研究内容和解决问题的方法引入到《区域生态环境评价技术》实验课的教学当中,将综合能力培养实验、设计提高型实验等教学模式相结合。针对教学过程发现的新问题和学生需求,及时调整教学手段和更新教学内容,达到了知识与技能的传授与素质培养相互促进和统一的作用。     

Geographically weighted regression with parameter-specific distance metrics

Geographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is generally used to calibrate a standard GWR model. However, variations in spatial relationships within a GWR model might also vary in intensity with respect to location and direction. This assertion has led to extensions of the standard GWR model to mixed (or semiparametric) GWR and to flexible bandwidth GWR models. In this article, we present a strongly related extension in fitting a GWR model with parameter-specific distance metrics (PSDM GWR). As with mixed and flexible bandwidth GWR models, a back-fitting algorithm is used for the calibration of the PSDM GWR model. The value of this new GWR model is demonstrated using a London house price data set as a case study. The results indicate that the PSDM GWR model can clearly improve the model calibration in terms of both goodness of fit and prediction accuracy, in contrast to the model fits when only one metric is singly used. Moreover, the PSDM GWR model provides added value in understanding how a regression model’s relationships may vary at different spatial scales, according to the bandwidths and distance metrics selected. PSDM GWR deals with spatial heterogeneities in data relationships in a general way, although questions remain on its model diagnostics, distance metric specification, and computational efficiency, providing options for further research.     

AN IMPROVED ALGORITHM FOR THE GENERALIZED RANK ANNIHILATION METHOD

An improved algorithm for the generalized rank annihilation method(GRAM)is presented.GRAM isa method for multicomponent calibration using two-dimensional instruments,such as GC-MS.In thispaper an orthonormal base is first computed and used to project the calibration and unknown sampleresponse matrices into a lower-dimensional subspace.The resulting generalized eigenproblem is thensolved using the QZ algorithm.The result of these improvements is that GRAM is computationally morestable,particularly in the case where the calibration sample contains chemical constituents not present inthe unknown sample and the unknown contains constituents not present in the calibration(the mostgeneral case).