A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.

PARTIAL LEAST-SQUARES(PLS)REGRESSION AND SPECTROPHOTOMETRY AS APPLIED TO THE ANALYSIS OF MULTICOMPONENT MIXTURES

The UV absorption spectra of o-naphthol,α-naphthylamine,2,7-dihydroxy naphthalene,2,4-dimethoxy ben- zaldehyde and methyl salicylate,overlap severely;therefore it is impossible to determine them in mixtures by traditional spectrophotometric methods.In this paper,the partial least-squares(PLS)regression is applied to the simultaneous determination of these compounds in mixtures by UV spectrophtometry without any pretreatment of the samples.Ten synthetic mixture samples are analyzed by the proposed method.The mean recoveries are 99.4%,996%,100.2%,99.3% and 99.1%,and the relative standard deviations(RSD) are 1.87%,1.98%,1.94%,0.960% and 0.672%,respectively.

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.

Double-Penalized Quantile Regression in Partially Linear Models

In this paper, we propose the double-penalized quantile regression estimators in partially linear models. An iterative algorithm is proposed for solving the proposed optimization problem. Some numerical examples illustrate that the finite sample performances of proposed method perform better than the least squares based method with regard to the non-causal selection rate (NSR) and the median of model error (MME) when the error distribution is heavy-tail. Finally, we apply the proposed methodology to analyze the ragweed pollen level dataset.

PARAMETRIC TEST IN PARTIAL LINEAR REGRESSION MODELS

Consider the regression model, n. Here the design points (xi,ti) are known and nonrandom, and ei are random errors. The family of nonparametric estimates of g() including known estimates proposed by Gasser & Muller[1] is also proposed to be a class of new nearest neighbor estimates of g(). Baed on the nonparametric regression procedures, we investigate a statistic for testing H0:g=0, and obtain some aspoptotic results about estimates.

Partial least squares regression for predicting economic loss of vegetables caused by acid rain

To predict the economic loss of crops caused by acid rain,we used partial least squares(PLS) regression to build a model of single dependent variable -the economic loss calculated with the decrease in yield related to the pH value and levels of Ca2+,NH4+,Na+,K+,Mg2+,SO42-,NO3-,and Cl-in acid rain. We selected vegetables which were sensitive to acid rain as the sample crops,and collected 12 groups of data,of which 8 groups were used for modeling and 4 groups for testing. Using the cross validation method to evaluate the performace of this prediction model indicates that the optimum number of principal components was 3,determined by the minimum of prediction residual error sum of squares,and the prediction error of the regression equation ranges from -2.25% to 4.32%. The model predicted that the economic loss of vegetables from acid rain is negatively corrrelated to pH and the concentrations of NH4+,SO42-,NO3-,and Cl-in the rain,and positively correlated to the concentrations of Ca2+,Na+,K+ and Mg2+. The precision of the model may be improved if the non-linearity of original data is addressed.

The Consistency of LSE Estimators in Partial Linear Regression Models under Mixing Random Errors

In this paper,we consider the partial linear regression model y_(i)=x_(i)β^(*)+g(ti)+ε_(i),i=1,2,...,n,where(x_(i),ti)are known fixed design points,g(·)is an unknown function,andβ^(*)is an unknown parameter to be estimated,random errorsε_(i)are(α,β)-mix_(i)ng random variables.The p-th(p>1)mean consistency,strong consistency and complete consistency for least squares estimators ofβ^(*)and g(·)are investigated under some mild conditions.In addition,a numerical simulation is carried out to study the finite sample performance of the theoretical results.Finally,a real data analysis is provided to further verify the effect of the model.

STRONG CONVERGENCE RATES OF SEVERAL ESTIMATORS IN SEMIPARAMETRIC VARYING-COEFFICIENT PARTIALLY LINEAR MODELS

This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.

Quantile Regression of Ultra-high Dimensional Partially Linear Varying-coefficient Model with Missing Observations

In this paper,we focus on the partially linear varying-coefficient quantile regression with missing observations under ultra-high dimension,where the missing observations include either responses or covariates or the responses and part of the covariates are missing at random,and the ultra-high dimension implies that the dimension of parameter is much larger than sample size.Based on the B-spline method for the varying coefficient functions,we study the consistency of the oracle estimator which is obtained only using active covariates whose coefficients are nonzero.At the same time,we discuss the asymptotic normality of the oracle estimator for the linear parameter.Note that the active covariates are unknown in practice,non-convex penalized estimator is investigated for simultaneous variable selection and estimation,whose oracle property is also established.Finite sample behavior of the proposed methods is investigated via simulations and real data analysis.

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.

A New Test for Large Dimensional Regression Coefficients

In the article, hypothesis test for coefficients in high dimensional regression models is considered. I develop simultaneous test statistic for the hypothesis test in both linear and partial linear models. The derived test is designed for growing p and fixed n where the conventional F-test is no longer appropriate. The asymptotic distribution of the proposed test statistic under the null hypothesis is obtained.

Function-on-Partially Linear Functional Additive Models

We consider a functional partially linear additive model that predicts a functional response by a scalar predictor and functional predictors. The B-spline and eigenbasis least squares estimator for both the parametric and the nonparametric components proposed. In the final of this paper, as a result, we got the variance decomposition of the model and establish the asymptotic convergence rate for estimator.

Linear Maximum Likelihood Regression Analysis for Untransformed Log-Normally Distributed Data

Medical research data are often skewed and heteroscedastic. It has therefore become practice to log-transform data in regression analysis, in order to stabilize the variance. Regression analysis on log-transformed data estimates the relative effect, whereas it is often the absolute effect of a predictor that is of interest. We propose a maximum likelihood (ML)-based approach to estimate a linear regression model on log-normal, heteroscedastic data. The new method was evaluated with a large simulation study. Log-normal observations were generated according to the simulation models and parameters were estimated using the new ML method, ordinary least-squares regression (LS) and weighed least-squares regression (WLS). All three methods produced unbiased estimates of parameters and expected response, and ML and WLS yielded smaller standard errors than LS. The approximate normality of the Wald statistic, used for tests of the ML estimates, in most situations produced correct type I error risk. Only ML and WLS produced correct confidence intervals for the estimated expected value. ML had the highest power for tests regarding β1.

Fuzzy Varying Coefficient Bilinear Regression of Yield Series

We construct a fuzzy varying coefficient bilinear regression model to deal with the interval financial data and then adopt the least-squares method based on symmetric fuzzy number space. Firstly, we propose a varying coefficient model on the basis of the fuzzy bilinear regression model. Secondly, we develop the least-squares method according to the complete distance between fuzzy numbers to estimate the coefficients and test the adaptability of the proposed model by means of generalized likelihood ratio test with SSE composite index. Finally, mean square errors and mean absolutely errors are employed to evaluate and compare the fitting of fuzzy auto regression, fuzzy bilinear regression and fuzzy varying coefficient bilinear regression models, and also the forecasting of three models. Empirical analysis turns out that the proposed model has good fitting and forecasting accuracy with regard to other regression models for the capital market.

近30年皖西大别山土壤侵蚀时空变化及其对景观格局的响应

[目的]为明确安徽省内长江流域和淮河流域重要的生态过渡区皖西大别山区的景观格局与土壤侵蚀及其关系,为该区域的景观格局调控和水土流失治理提供参考。[方法]借助RULSE模型计算皖西大别山区近30年土壤侵蚀模数,借助景观指数对其景观格局的变化进行描述,并使用偏最小二乘回归(PLSR)探究该区域景观指数对土壤侵蚀的影响关系。[结果]近30年,土壤侵蚀模数呈先减少后增加趋势;土壤侵蚀较严重地区主要集中在中西部和南部山区;大部分地区土壤侵蚀强度主要为微度和轻度,且各类土地利用类型的侵蚀强度有明显差别,表现为草地>耕地>林地;土地利用类型及景观格局总体较稳定,景观格局变化主要表现为景观破碎化的降低、景观异质性和连接性的提高;香农多样性指数(SHDI)、边界密度指数(ED)、相似邻近百分比(PLADJ)、景观形状指数(LSI)对皖西大别山区具有显著解释意义,且表现为SHDI、ED、LSI对土壤侵蚀起显著正向作用,PLADJ对土壤侵蚀起显著负向作用。[结论]在皖西大别山区,景观破碎化的提高和景观连通度的降低显著促进土壤侵蚀。

基于高光谱变换的枸杞冠层含水率预测模型

为实现枸杞冠层水分信息的快速无损监测,以“宁杞7号”枸杞为试验对象,测定枸杞冠层叶片光谱和叶片含水率,对原始光谱进行一阶微分和连续统去除2种数学变换,将获取的原始光谱(OS)、一阶微分光谱(FDS)及连续统去除光谱(CRS)与含水率进行相关性分析,筛选出敏感波长并构建预测含水率的随机森林回归模型(RFRM)、偏最小二乘回归模型(PLSRM)、岭回归模型(RRM)及一元回归模型(URM),最后对模型的精度进行检验与评价。结果表明:从敏感波长分析,基于FDS构建的模型,其拟合度为0.716~0.938;基于CRS构建的模型,其拟合度为0.710~0.920;基于OS构建的模型,其拟合度为0.710~0.874;可见,基于FDS和CRS构建的模型,拟合度均高于基于OS构建的模型。从模型类型分析,RFRM的拟合度最高(0.874~0.938),其次为PLSRM(0.826~0.866)和RRM(0.737~0.889),URM的拟合度最低(0.710~0.730)。综合分析,基于一阶微分光谱构建的随机森林回归模型(FDS+RFRM)预测效果最优,其训练集和测试集的拟合度分别为0.938和0.893,检验集R^(2)、RMSE、MAE及RPD分别为0.872、0.561、0.466和2.156。研究将光谱变换与机器学习相结合,开发一套适用于枸杞冠层叶片含水率的且预测精度很高的高光谱探测模型,为枸杞冠层含水率的监测提供适宜高效的方法。

广西农业社会化服务对农业生产的影响研究

基于2013—2021年广西统计数据构建了评价指标体系,采用熵值法和DEA模型测算了广西农业社会化服务水平和农业生产效率水平,采用灰色关联度和偏最小二乘回归模型分析了农业社会化服务对农业生产的影响。结果表明:广西农业社会化服务发展水平逐年上升,各分项服务之间变化差异较大;农业生产效率总体不断上升,但受规模效率影响,大多年份处于DEA无效状态;信息服务与农业生产效率、农业产业结构和农业收入的关联度最大;流通服务对农业生产效率、农业产业结构和农业收入的促进作用最明显。鉴于此,提出了促进广西农业社会化服务体系发展、加强广西农业社会化服务中弱项服务以及提高广西农业生产效率发展水平的对策建议。

黄金矿山岩体质量分级知识库与PLS简化预测模型

针对黄金矿山工程岩体特征,分析了岩石单轴抗压强度、RQD值、节理结构面状态、节理结构面间距、地下水状态、节理结构面方向对工程影响和地应力值这7个主要因素对岩体稳定性的影响,对7个指标进行修正,建立了地下矿山M-RMR岩体质量评价指标体系。采用M-RMR岩体质量评价指标体系划分了焦家金矿直属矿区、寺庄矿区和望儿山矿区工程岩体质量等级,建立了焦家金矿地下矿山岩体质量与其影响因素的神经网络知识库模型,达到了焦家金矿工程岩体质量智能分级的目的。为简化M-RMR指标体系中指标数量,更利于实际应用,采用变量投影重要性指标VIP对7个指标所携带信息量的大小进行排序,并逐个删除不重要的指标,利用单因变量的偏最小二乘回归方法(PLS1)建立了精简指标的简化预测模型。简化预测模型可使用较少的评价指标对岩体质量给出准确的分级,具有实际使用价值。

基于高光谱技术的穿心莲药材中穿心莲内酯类成分检测研究

基于高光谱技术结合化学计量学,建立不同种质穿心莲药材中穿心莲内酯类成分含量的检测方法。采集穿心莲样品的高光谱信息,获得原始光谱数据(Raw Data)。采用一阶导数(D1)、二阶导数(D2)、SG平滑(SG)、乘性散射校正(MSC)对Raw Data预处理,结合偏最小二乘判别分析(PLS-DA)建立分类模型,结合偏最小二乘回归(PLSR)、反向传播神经网络(BPNN)、随机森林回归(RFR)建立回归模型。应用连续投影算法(SPA)简化模型。不同种质的穿心莲最佳分类模型为D1-PLS-DA。穿心莲内酯、新穿心莲内酯、去氧穿心莲内酯、脱水穿心莲内酯4种穿心莲内酯类化合物总含量的最佳回归模型分别为SG-PLSR、MSC-PLSR、Raw Data-SPA-BPNN、MSC-SPA-BPNN和Raw Data-PLSR。应用高光谱技术可实现穿心莲品质的快速准确检测。

基于PAD模型的内饰色彩情感评价研究

从情感角度,探究座舱内饰色彩的评价方法,为内饰的色彩设计提供一种高效的情感测量工具。根据HSV色彩体系制作内饰颜色样本,借助眼动实验和PAD情感量表得出被测试者对汽车内饰的情绪状态,分析眼动指标、内饰色彩属性与情绪维度的相互映射关系,提出座舱内饰色彩设计原则,并采用偏最小二乘方法建立三者之间的关系模型。得到眼动指标、情绪维度与色彩属性之间的PLS模型和座舱内饰色彩设计原则。该模型可用于座舱内饰色彩的情感体验预测和评价。