Characterizing and estimating rice brown spot disease severity using stepwise regression,principal component regression and partial least-square regression

Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

Electricity price forecasting using generalized regression neural network based on principal components analysis

A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.

Leakage Rate Model of Urban Water Supply Networks Using Principal Component Regression Analysis

To analyze the factors affecting the leakage rate of water distribution system, we built a macroscopic "leakage rate–leakage factors"(LRLF) model. In this model, we consider the pipe attributes(quality, diameter,age), maintenance cost, valve replacement cost, and annual average pressure. Based on variable selection and principal component analysis results, we extracted three main principle components—the pipe attribute principal component(PAPC), operation management principal component, and water pressure principal component. Of these, we found PAPC to have the most influence. Using principal component regression, we established an LRLF model with no detectable serial correlations. The adjusted R2 and RMSE values of the model were 0.717 and 2.067, respectively.This model represents a potentially useful tool for controlling leakage rate from the macroscopic viewpoint.

Analysis of York Pigs Feeding Behavior Using Stepwise Regression and Principal Component Regression

A statistical analysis was conducted on the feeding behavior of 106 York breeding pigs.Pearson correlation analysis,principal component correlation analysis and multiple stepwise regression equation methods were applied to establish regression equations of the York breeding pigs total feed intake per time and average feed intake per time with corrected fat thickness,feed conversion rate,and corrected daily gain.The results showed that:①there were three peak feed intake periods for the pigs,and the correlation coefficient between the feed intake and the corrected fat thickness of the pigs in the 24 h period was positive or negative,that is,increasing the number of feeding times and the feed intake was not necessarily conducive to the fat thickness accumulation,but the breeding goal of fat thickness could be achieved by controlling the feeding times and feed intake;②the average feed intake of pigs in the 60-90 kg body weight stage was 30%-50%higher than that of the 30-60 kg body weight stage,but the number of feeding times decreased,the peak feeding time was more concentrated,and the feeding duration per time was 3.0 min longer,indicating that as the weight of pigs increased,the feed intake increased significantly;and③the stepwise regression equations and the principal component equations showed that the feeding behavior of York pigs in the 30-90 kg growth stage was not only affected by the feeding time within 24 h,but also by environmental factors such as temperature and humidity.The feeding behavior of York pigs is a complex process of interaction between environmental factors and animal factors.

Combining Principal Component Regression and Artificial Neural Network to Predict Chlorophyll-a Concentration of Yuqiao Reservoir’s Outflow

In order to investigate the eutrophication degree of Yuqiao Reservoir, a hybrid method, combining principal component regression (PCR) and artificial neural network (ANN), was adopted to predict chlorophyll-a concentration of Yuqiao Reservoir’s outflow. The data were obtained from two sampling sites, site 1 in the reservoir, and site 2 near the dam. Seven water variables, namely chlorophyll-a concentration of site 2 at time t and that of both sites 10 days before t, total phosphorus(TP), total nitrogen(TN), dissolved oxygen(DO), and temperature from January 2000 to September 2002, were utilized to develop models. To remove the collinearity between the variables, principal components extracted by principal component analysis were employed as predictors for models. The performance of models was assessed by the square of correlation coefficient, mean absolute error (MAE), root mean square error (RMSE) and average absolute relative error (AARE). Results show that the hybrid method has achieved more accurate prediction than PCR or ANN model. Finally, the three models were applied to predicting the chlorophyll-a concentration in 2003. The predictions of the hybrid method were found to be consistent with the observed values all year round, while the results of PCR and ANN models did not fit quite well from July to October.

Application of Principal Component Regression with Dummy Variable in Statistical Downscaling to Forecast Rainfall

Statistical downscaling (SD) analyzes relationship between local-scale response and global-scale predictors. The SD model can be used to forecast rainfall (local-scale) using global-scale precipitation from global circulation model output (GCM). The objectives of this research were to determine the time lag of GCM data and build SD model using PCR method with time lag of the GCM precipitation data. The observations of rainfall data in Indramayu were taken from 1979 to 2007 showing similar patterns with GCM data on 1st grid to 64th grid after time shift (time lag). The time lag was determined using the cross-correlation function. However, GCM data of 64 grids showed multicollinearity problem. This problem was solved by principal component regression (PCR), but the PCR model resulted heterogeneous errors. PCR model was modified to overcome the errors with adding dummy variables to the model. Dummy variables were determined based on partial least squares regression (PLSR). The PCR model with dummy variables improved the rainfall prediction. The SD model with lag-GCM predictors was also better than SD model without lag-GCM.

Short-Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression

Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.

A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.

The Pre－test Principal Components Estimator in the Two Seemingly Unrelated Regression System

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Dimensioning a stockpile operation using principal component analysis

Mineral processing plants generally have narrow tolerances for the grades of their input raw materials,so stockpiles are often maintained to reduce material variance and ensure consistency.However,designing stockpiles has often proven difficult when the input material consists of multiple sub-materials that have different levels of variances in their grades.In this paper,we address this issue by applying principal component analysis(PCA)to reduce the dimensions of the input data.The study was conducted in three steps.First,we applied PCA to the input data to transform them into a lower-dimension space while retaining 80% of the original variance.Next,we simulated a stockpile operation with various geometric stockpile configurations using a stockpile simulator in MATLAB.We used the variance reduction ratio as the primary criterion for evaluating the efficiency of the stockpiles.Finally,we used multiple regression to identify the relationships between stockpile efficiency and various design parameters and analyzed the regression results based on the original input variables and principal components.The results showed that PCA is indeed useful in solving a stockpile design problem that involves multiple correlated input-material grades.

Research on Rural Consumer Demand in Hebei Province Based on Principal Component Analysis

By selecting the time sequence data concerning influencing factors of rural consumer demand in Hebei Province from 2000 to 2010,this paper uses the principal component analysis method in multiplex econometric statistical analysis,constructs the principal component of consumer demand in Hebei Province,conducts regression on the dependent variable of consumer spending per capita in Hebei Province and the principal component of consumer demand so as to get principal component regression,and then conducts quantitative and qualitative analysis on the principal component.The results show that total output value per capita (yuan),employment rate,and income gap,are correlative with rural residents' consumer demand in Hebei Province positively;consumer price index,upbringing ratio of children,and one-year interest rate are correlative with rural residents' consumer demand in Hebei Province negatively;the ratio of supporting the elderly and medical care spending per capita are correlative with rural residents' consumer demand in Hebei Province positively.The corresponding countermeasures and suggestions are put forward to promote residents' consumer demand in Hebei Province as follows:develop county economy in Hebei Province and increase rural residents' consumer demand;use industry to support agriculture and coordinate urban-rural development;improve rural medical care and health system and resolve actual difficulties of the masses.

Cyclic behavior of reinforced sand under principal stress rotation

Although the cyclic rotation of the principal stress direction is important,its effect on the deformation behavior and dynamic properties of the reinforced soil has not been reported to date.Tests carried out on large-scale hollow cylinder samples reveal that the cyclic rotation of the principal stress direction results in significant variations of strain components(ε,ε,εand γ) with periodic characteristics despite the deviatoric stress being constant during tests.This oscillation can be related to the corresponding variations in the stress components and the anisotropic fabric that rotate continuously along the principal stress direction.Sand under rotation appears to develop a plastic strain.Similar trends are observed for reinforced sand,but the shear interaction,the interlocking between particles and reinforcement layer,and the confinement result in significant reductions in the induced strains and associated irrecoverable plastic strains.Most of the strains occur in the first cycle,and as the number of cycles increases,the presence of strains becomes very small,which is almost insignificant.This indicates that the soil has reached anisotropic critical state(ACS),where a stable structure is formed after continuous orientation,realignment and rearrangement of the particles accompanied with increasing cyclic rotation.Rotation in the range of 60°-135° produces more induced strains even in the presence of the reinforcement,when compared with other ranges.This relates to the extension mode of the test in this range in which σ>σand to the relative approach between the mobilized plane and the weakest horizontal plane.Reinforcement results in an increase in shear modulus while it appears to have no effect on the damping ratio.Continuous cycles of rotation result in an increase in shear modulus and lower damping ratio due to the densification that causes a decrease in shear strain and less dissipation of energy.

High Dimensional Dataset Compression Using Principal Components

Until recently, computational power was insufficient to diagonalize atmospheric datasets of order 108 - 109 elements. Eigenanalysis of tens of thousands of variables now can achieve massive data compression for spatial fields with strong correlation properties. Application of eigenanalysis to 26,394 variable dimensions, for three severe weather datasets (tornado, hail and wind) retains 9 - 11 principal components explaining 42% - 52% of the variability. Rotated principal components (RPCs) detect localized coherent data variance structures for each outbreak type and are related to standardized anomalies of the meteorological fields. Our analyses of the RPC loadings and scores show that these graphical displays can efficiently reduce and interpret large datasets. Data is analyzed 24 hours prior to severe weather as a forecasting aid. RPC loadings of sea-level pressure fields show different morphology loadings for each outbreak type. Analysis of low level moisture and temperature RPCs suggests moisture fields for hail and wind which are more related than for tornado outbreaks. Consequently, these patterns can identify precursors of severe weather and discriminate between tornadic and non-tornadic outbreaks.

A Comparative Study of Locality Preserving Projection and Principle Component Analysis on Classification Performance Using Logistic Regression

There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.

Optimal Batching Plan of Deoxidation Alloying based on Principal Component Analysis and Linear Programming

As the market competition of steel mills is severe,deoxidization alloying is an important link in the metallurgical process.To solve this problem,principal component regression analysis is adopted to reduce the dimension of influencing factors,and a reasonable and reliable prediction model of element yield is established.Based on the constraint conditions such as target cost function constraint,yield constraint and non-negative constraint,linear programming is adopted to design the lowest cost batting scheme that meets the national standards and production requirements.The research results provide a reliable optimization model for the deoxidization and alloying process of steel mills,which is of positive significance for improving the market competitiveness of steel mills,reducing waste discharge and protecting the environment.

Cardiovascular age of aviation personnel: based on the principal component analysis of heart rate and blood pressure variability

Objective: To introduce a method to calculate cardiovascular age, a new, accurate and much simpler index for assessing cardiovascular autonomic regulatory function, based on statistical analysis of heart rate and blood pressure variability (HRV and BPV) and baroreflex sensitivity (BRS) data. Methods: Firstly, HRV and BPV of 89 healthy aviation personnel were analyzed by the conventional autoregressive (AR) spectral analysis and their spontaneous BRS was obtained by the sequence method. Secondly, principal component analysis was conducted over original and derived indices of HRV, BPV and BRS data and the relevant principal components, PCi orig and PCi deri (i=1, 2, 3,...) were obtained. Finally, the equation for calculating cardiovascular age was obtained by multiple regression with the chronological age being assigned as the dependent variable and the principal components significantly related to age as the regressors. Results: The first four principal components of original indices accounted for over 90% of total variance of the indices, so did the first three principal components of derived indices. So, these seven principal components could reflect the information of cardiovascular autonomic regulation which was embodied in the 17 indices of HRV, BPV and BRS exactly with a minimal loss of information. Of the seven principal components, PC2 orig , PC4 orig and PC2 deri were negatively correlated with the chronological age ( P <0 05), whereas the PC3 orig was positively correlated with the chronological age ( P <0 01). The cardiovascular age thus calculated from the regression equation was significantly correlated with the chronological age among the 89 aviation personnel ( r =0.73, P <0 01). Conclusion: The cardiovascular age calculated based on a multi variate analysis of HRV, BPV and BRS could be regarded as a comprehensive indicator reflecting the age dependency of autonomic regulation of cardiovascular system in healthy aviation personnel.

芙蓉李果实成熟期间的综合品质评价指标筛选与表观预测模型构建

目的科学评价芙蓉李果实成熟期间的营养品质,建立色度值表观特征与营养品质的关系。方法以福建省主栽品种芙蓉李为研究对象,对其成熟期间果糖、葡萄糖、蔗糖、苹果酸、奎尼酸、琥珀酸、柠檬酸、富马酸、矢车菊素-3-芸香糖苷、矢车菊素-3-葡萄糖苷、多酚、黄酮、类胡萝卜素等13个品质指标进行分析和综合评价。结果芙蓉李成熟期间,各品质指标的含量变化存在显著差异(P<0.05),综合运用相关分析、因子分析、绝对因子分析-多元线性回归(absolute principal component scores-multiple linear regression,APCS-MLR)分析筛选可反映芙蓉李综合品质的主要指标。因子分析提取出3个主因子,贡献率分别为52.677%、23.468%、11.649%,累计贡献率为87.794%。综合APCS-MLR等数理统计分析,主因子1主要对果糖、矢车菊素-3-芸香糖苷、矢车菊素-3-葡萄糖苷贡献较大,贡献率分别为53.00%、73.85%、55.54%;主因子2主要对蔗糖、富马酸、果糖、柠檬酸的贡献率较大,分别为28.26%、18.70%、16.14%、15.59%;主因子3主要对多酚(29.13%)和黄酮(28.28%)有较大贡献率;选取3个主因子总贡献率高于60%的果糖、葡萄糖、矢车菊素-3-芸香糖苷、矢车菊素-3-葡萄糖苷作为综合品质评价的主要指标。分别对已筛选出的4个主要评价指标与色度值进行多元线性逐步回归分析,建立4个主要指标与色度值的表观预测模型,各模型均具有较好的拟合度,预测值与实测值的均方根误差较小;进一步验证结果表明,通过色度值对4个指标的预测具有较高的可靠性和准确性。结论本研究筛选出的主要指标及预测模型可更加简单、便捷地评价芙蓉李果实成熟期间的综合品质。

P-GMAW起弧过程特征分析及稳定性判别方法

脉冲熔化极气体保护焊(pulsed gas metal arc welding,P-GMAW)起弧过程易产生不稳定现象,会严重影响电弧传感焊缝跟踪精度.针对这一问题,对摆动电弧窄间隙P-GMAW不稳定起弧过程的成因进行了研究,发现送丝速度对起弧过程稳定性具有重要影响.通过对电弧图像与电信号特征进行对比分析,提取了表征电弧稳定性的电信号特征变量;为减小变量冗余性和过拟合,采用最大似然估计法筛选并提取了8个变量,并通过主成分分析法(principal component analysis,PCA)对变量进行融合,提取了方差贡献率最高的前两个主成分;根据因子载荷发现,相比熔滴过渡阶段和基值阶段,脉冲峰值阶段是电弧更易发生不稳定现象的阶段.结合提取的主成分变量与二分类Logistic回归模型建立了起弧过程电弧稳定性判别模型.通过受试者工作特征(receiver operating characteristic,ROC)曲线得到了模型的最佳阈值.结果表明,该模型对脉冲稳定性判别准确率达到了80%以上,表明模型具有良好的判别性能.该模型对提高窄间隙高低跟踪精度、保证焊接质量具有一定应用价值.

基于“社-校-家-生”四维的大学生学业预警影响因素相关性分析——以安徽中医药大学中西医临床医学专业为例

通过回归分析探讨“社-校-家-生”四维影响因素对学业预警机制的相关性,对安徽中医药大学中西医临床医学专业的421名学生展开调研,采用主成分分析法,建立多因素回归分析模型。建立模型后,发现其中影响最大的五方面因素分别是家庭、课外活动、学习基础、人际关系、就业情况。其中,重要性分析中,父母最高文化水平、生活费/月、挂科数目、户籍所在地、担任班委、辅导员联系家长情况排序前六。通过对四维因素进行逐步回归分析与交互分析,发现对学业预警影响最为显著的为家庭因素和学校因素,且两者不存在交互关系。基于回归分析结果显示对学业成绩影响的主要因素为家庭因素、学校因素,其中家庭维度方面主要是生活费/月、户籍所在地起主要作用,与学业成绩呈负相关,学校维度层面主要是辅导员与家长的联系情况以及相关制度的制定与开展影响较大,与学业成绩呈正相关。