MULTIVARIATE SELECTION OF VARIABLES IN INDUSTRIAL QUALITY CONTROL:OPTIMIZING AVIATION FUEL FINAL CONTROL

This paper deals with a typical question encountered in all industrial analytical laboratories:are all thetests performed in the laboratory to characterize the final product really necessary?In this work the cross-validation technique,Procrustes rotation techniques and statistical variable selection have been used toreduce the 26 initial British Petroleum and ASTM kerosene specification tests to ten'essential'ones.Statistical as well as chemical considerations were used to select the optimum subset of original variablesto be retained from all the possible ones.     

PREDICTIVE ABILITY OF REGRESSION MODELS.PART Ⅰ:STANDARD DEVIATION OF PREDICTION ERRORS (SDEP)

The standard deviation of prediction errors(SDEP)is used to evaluate and compare the predictive abilityof some regression models,namely MLR,ACE and linear and non-linear PLS,the last being the bestone.The parameter is determined by a cross-validation approach as an average of several runs obtainedon forming groups in a random way.The variation in SDEP with the number of latent variables in PLSis also discussed.     

神经网络模型预报湖北汛期降水量的应用研究

使用人工神经网络方法建立了湖北省汛期 (6～ 8月 )总降水量的短期气候预测模型 ,该神经网络模型的输入是汛期前期 (2～ 4月 )的北半球月平均 5 0 0 h Pa高度场、海平面气压场和太平洋海温场的扩展自然正交展开 (EEOF)的前几个主要模态的时间系数 ,输出了湖北汛期降水场的自然正交展开 (EOF)的前 2个主要模态的时间系数。41 a历史资料的交叉检验表明 :样本试验的预报技巧评分平均为 0 .2 4 6 ,虽然该模型对各年的预报效果仍存在一定的不稳定性 ,但它可为湖北汛期降水的短期气候预测提供一种具有明显统计预报正技巧的预报方法     

SHORT COMMUNICATION CLASSIFICATION METHODS FOR MULTIVARIATE QUALITY PARAMETERS

There are many chemical products where product conformity is decided upon by qualitative humanjudgements of overall product quality.Nowadays,quantitative instrumentally determined qualityparameters become available which are intended to replace such qualitative judgements by means ofautomatic decision rules using multivariate specification limits.Six classification methods to derive suchlimits are compared in terms of their power to predict corresponding human judgements on overall colorconformity of 17 dyestuffs based on historical quality data.Standard statistical classification methodsturned out to be unacceptable for the routine generation of decision rules because of the frequent distinctsuboptimality of their predictive power.Instead,a simple non-statistical classification method utilizinga priori knowledge about the underlying data structure yielded uniformly satisfactory decision rules.     

Estimation of the generalized covariance function: II. A response surface approach

A procedure is proposed that employs first-moment estimation (kriging), cross-validation, and response surface analysis to estimate parameters of a generalized covariance function. Results from application of this procedure to two data sets are given.     

Geostatistical radar–raingauge merging: A novel method for the quantification of rain estimation accuracy

Compared to other estimation techniques, one advantage of geostatistical techniques is that they provide an index of the estimation accuracy of the variable of interest with the kriging estimation standard deviation (ESD). In the context of radar–raingauge quantitative precipitation estimation (QPE), we address in this article the question of how the kriging ESD can be transformed into a local spread of error by using the dependency of radar errors to the rain amount analyzed in previous work. The proposed approach is implemented for the most significant rain events observed in 2008 in the Cévennes-Vivarais region, France, by considering both the kriging with external drift (KED) and the ordinary kriging (OK) methods. A two-step procedure is implemented for estimating the rain estimation accuracy: (i) first kriging normalized ESDs are computed by using normalized variograms (sill equal to 1) to account for the observation system configuration and the spatial structure of the variable of interest (rainfall amount, residuals to the drift); (ii) based on the assumption of a linear relationship between the standard deviation and the mean of the variable of interest, a denormalization of the kriging ESDs is performed globally for a given rain event by using a cross-validation procedure. Despite the fact that the KED normalized ESDs are usually greater than the OK ones (due to an additional constraint in the kriging system and a weaker spatial structure of the residuals to the drift), the KED denormalized ESDs are generally smaller the OK ones, a result consistent with the better performance observed for the KED technique. The evolution of the mean and the standard deviation of the rainfall-scaled ESDs over a range of spatial (5–300 km²) and temporal (1–6 h) scales demonstrates that there is clear added value of the radar with respect to the raingauge network for the shortest scales, which are those of interest for flash-flood prediction in the considered region.     

Multi-Scale Physical Season Onset Statistic Prediction Date in Guangdong Model for Rain Province

采用小波分析、Lanczos滤波器、相关分析、最优子集回归和交叉检验等方法,研究了广东开汛日期的多尺度变化特征及其与全球不同地区前期海温场、500hPa高度场的关系,建立了广东开汛日期的多尺度最优子集回归预测模型并进行了检验。结果表明,广东开汛日期存在显著的准6年和较明显的准17年周期振荡。广东开汛日期在年际和年代际变化尺度上与前冬海温场和500hPa高度场上共有20个显著相关区域,分别取对应时间尺度上显著相关区域的平均值作为预报因子,对相应时间尺度的广东开汛日期做最优子集回归,建立了相应的预测模型,以年际和年代际尺度上的预测值之和为广东开汛日期的预测值。所建立的预测模型具有较好的拟合效果,其中拟合值与实况值相差在5天以内的事件命中率为41．5％,i0天以内的为60．4％。1951-2010年的交叉检验结果表明,广东开汛日期预测值和实况值之间的相关系数为0．33,通过了α-0．Ol的显著性水平检验。预测值与实况值相差在5天以内的事件命中率为26．7％,10天以内的为45．0％,因此,所建立的多尺度最优子集回归预测模型对广东开汛日期具有较好的预测能力。     

气象要素插值的空间化精度提高方法研究

为了提高气象要素空间化的精度,本文提出通过预先对气象数据进行处理,然后再进行空间化,以比较直接插值与原始数据处理之后再插值的精度的变化。文中采用数据为全国743个常规气象站40 a(1961—2000年)整编气象资料及2005年的常规气象资料;插值方法有反距离加权法(IDW)、克立格法(Kriging)和样条函数法(Spline);数据预处理方法采用距平处理。结果发现:使用IDW、Kriging和spline对平均温度距平进行插值精度比较,发现IDW方法最优;温度距平精度的提高比降水和相对湿度要好;降水距平误差呈现由东向西递增的趋势。由此可见,对气象要素做距平处理可以有效提高插值精度。     

基于国家气象观测站逐日降水格点数据的交叉检验误差分析

利用2006-2008年2 403个国家气象观测站地面雨量计的逐日降水量资料,采用与网格点最近的观测站有、无降水确定该网格点有、无降水和Barnes插值方案确定网格点降水大小的混合插值方案,得到全国空间分辨率为0.1°×0.1°（约10km×10km）的逐日降水量格点数据,在此基础上通过交叉检验方法统计格点数据的误差,...     

广东开汛日期的多尺度物理统计预测模型

采用小波分析、Lanczos滤波器、相关分析、最优子集回归和交叉检验等方法,研究了广东开汛日期的多尺度变化特征及其与全球不同地区前期海温场、500hPa高度场的关系,建立了广东开汛日期的多尺度最优子集回归预测模型并进行了检验。结果表明,广东开汛日期存在显著的准6年和较明显的准17年周期振荡。广东开汛日期在年际和年代际变化尺度上与前冬海温场和500hPa高度场上共有20个显著相关区域,分别取对应时间尺度上显著相关区域的平均值作为预报因子,对相应时间尺度的广东开汛日期做最优子集回归,建立了相应的预测模型,以年际和年代际尺度上的预测值之和为广东开汛日期的预测值。所建立的预测模型具有较好的拟合效果,其中拟合值与实况值相差在5天以内的事件命中率为41.5%,10天以内的为60.4%。1951—2010年的交叉检验结果表明,广东开汛日期预测值和实况值之间的相关系数为0.33,通过了α=0.01的显著性水平检验。预测值与实况值相差在5天以内的事件命中率为26.7%,10天以内的为45.0%,因此,所建立的多尺度最优子集回归预测模型对广东开汛日期具有较好的预测能力。