GIS-Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression

Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness that may yield misleading result when it is applied to dataset with spatial context. To strengthen this weakness, a new method that accounts for heterogeneity in relationships across geographic space has been presented. This is one of the family of local spatial statistical techniques referred to as geographically weighted regression (GWR). The method captures non-stationarity of relationship in spatial data that the ordinary least square (OLS) regression fails to account for. Thus, the paper is designed to explore and analyze the spatial relationships between cholera occurrence and household sources of water supply using GIS-based GWR, also to compare the modeling fitness of OLS and GWR. Vector dataset (spatial) of the study region by state levels and statistical data (non-spatial) on cholera cases, household sources of water supply and population data were used in this exploratory analysis. The result shows that GWR is a significant improvement on the global model. Comparing both models with the AICc value and the R2 value revealed that for the former, the value is reduced from 698.7 (for OLS model) to 691.5 (for GWR model). For the latter, OLS explained 66.4 percent while GWR explained 86.7 percent. This implies that local model’s fitness is higher than global model. In addition, the empirical analysis revealed that cholera occurrence in the study region is significantly associated with household sources of water supply. This relationship, as detected by GWR, largely varies across the region.

Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform

The magnetic information measured on the smartphone platform has a large fluctuation and the research of indoor localization algorithm based on smart-phone platform is less. Indoor localization algorithm on smartphone platform based on particle filter is studied. Robust local weighted regression is used to smooth the original magnetic data in the process of constructing magnetic map. Use moving average filtering model to filter the online magnetic observation data in positioning process. Compare processed online magnetic data with processed magnetic map collected by smartphone platform and the average matching error is 0.3941uT. Average positioning error is 0.229 meter when using processed online and map data.

Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

FUNCTIONAL-COEFFICIENT REGRESSION MODEL AND ITS ESTIMATION

In this paper,a class of functional-coefficient regression models is proposed and an estimation procedure based on the locally weighted least equares is suggested.This class of models,with the proposed estimation method,is a powerful means for exploratory data analysis.

血清镁离子浓度与直肠癌患者谷氨酰转肽酶的关系

直肠癌患者血清镁离子(Mg^(2+))浓度对γ-谷氨酰转肽酶(γ-GT)的影响尚不明确,收集分析临床数据,Mg^(2+)浓度不呈正态分布(D=0.737,P<0.05),按照中位数分组,比较不同组间临床病理特征的差异,用局部加权散点平滑(LOWESS)、分段线性回归方法,拟合血清Mg^(2+)浓度与γ-GT的关系。结果发现,与Mg^(2+)浓度小于0.92 mmol/L组的直肠癌患者比较,Mg^(2+)浓度≥0.92 mmol/L组的γ-GT和血清总胆固醇较高(P均<0.05)。LOWESS的结果表明,原始数据中血清Mg^(2+)浓度的两个变化点分别为0.92 mmol/L和0.99 mmol/L,自然对数转换的数据中血清Mg^(2+)浓度的另外两个变化点分别为-0.167和-0.01。利用原始数据建立的两段线性回归模型表明,血清Mg^(2+)浓度的变化点为0.98 mmol/L,Mg^(2+)浓度<0.98 mmol/L对γ-GT有正向影响,Mg^(2+)≥0.98 mmol/L时有负向影响。另一个使用自然对数转换数据的两段线性回归模型显示,血清Mg^(2+)浓度的变化点为0.01,其中Mg^(2+)浓度的自然对数<0.01对γ-GT有正向影响,当Mg^(2+)浓度的自然对数≥0.01时则有负向影响。用原始数据LOWESS回归的2个拐点,即0.92 mmol/L和0.99 mmol/L的Mg^(2+)浓度,进行三段线性回归分析,结果显示,<0.980 mmol/L的Mg^(2+)浓度,对γ-GT的影响是正向的,且与<0.929 mmol/L的Mg^(2+)浓度区间的系数不同,影响程度不同,而在≥0.980 mmol/L的Mg^(2+)浓度区间,对γ-GT的影响是负向的,此外,用对数转换数据LOWESS的2个拐点进行的三段线性回归,也显示Mg^(2+)浓度的自然对数以-0.020为分界点时,对γ-GT的影响方向是相反的,但三段线性回归模型均没有统计学意义,总之,具有较高Mg^(2+)浓度的直肠癌患者,其γ-GT的水平也较高,Mg^(2+)浓度对γ-GT的影响存在拐点效应,为γ-GT的关联因素研究提供了基础。

MRI对局部进展期直肠癌患者术前新辅助放化疗后病理完全反应的预测效能

目的:探讨磁共振成像(magnetic resonance imaging,MRI)对局部进展期直肠癌(locally advanced rectal cancer,LARC)患者术前新辅助放化疗(neo-adjuvant chemoradiotherapy,NCRT)后病理完全反应(pathological complete response,pCR)的预测效能。方法:回顾性纳入2019年11月至2023年10月在陕西省西安市某三甲医院的92例LARC患者,并依据NCRT后手术病理结果分为pCR组(n=21)和非pCR组(n=71)。所有患者NCRT前、后均实施直肠MRI常规扫描及弥散加权成像(diffusion-weighted imaging,DWI)检查,收集其临床特征与影像学特征,运用单因素、多因素Logistic回归分析确定pCR相关影响因素,并基于多因素Logistic回归分析绘制受试者工作特征(receiver operating characteristic,ROC)曲线,运用曲线下面积(area under curve,AUC)分析影像学特征对pCR预测价值。结果:单因素分析结果显示:pCR组NCRT后ADC与△ADC%高于非pCR组,奥沙利铂治疗占比与NCRT前ADC低于非pCR组(P<0.05)。多因素Logistic回归分析结果显示:NCRT前ADC、NCRT后ADC及△ADC%为LARC患者NCRT后pCR的独立影响因素(P<0.05)。ROC曲线分析显示:NCRT前ADC、NCRT后ADC、△ADC%联合预测LARC患者NCRT后pCR的AUC高于单一指标的AUC(P<0.05)。结论:NCRT前ADC、NCRT后ADC及△ADC%为LARC患者NCRT后pCR的独立影响因素。其中NCRT前ADC、NCRT后ADC、△ADC%可作为LARC患者NCRT后pCR的预测因素,且三组参数联合预测价值更为理想。

异方差混合地理加权回归模型的正交投影估计

文章针对误差项存在空间异方差的混合地理加权回归模型,提出了一种新的估计方法。该方法将正交投影、局部线性估计和广义最小二乘估计的思想相结合,能够单独对模型中的常数系数、系数函数和方差函数进行估计。通过数值模拟对所提方法的性能进行验证。模拟结果表明,所提方法比现有的再加权估计方法更具优势。最后,基于城镇居民文化消费水平及其影响因素的实证分析验证了所提方法的实用性。

基于局部对齐单目视频深度的三维场景重建

单目深度估计方法在各种场景下已经取得了较强的鲁棒性,该类方法通常预测尺度偏移量未知的不变仿射深度而非度量深度,因为收集大规模的不变仿射深度训练数据比收集度量深度训练数据容易得多。然而,在某些基于视频的应用场景中,例如视频深度估计和三维场景重建,每帧预测的深度中存在的未知比例和偏移量值可能会导致预测的深度不一致。为了解决该问题,我们提出了一种基于局部加权线性回归的方法,通过利用稀疏锚点恢复深度的尺度图和偏移量图,以保证连续帧之间的一致性。大量的实验表明,我们的方法可以在几个零样本基准上显著降低现有技术方法的Rel误差(相对误差)。此外,我们收集了630万张RGBD图像对来训练鲁棒的深度模型。通过局部恢复尺度和偏移量,我们的ResNet50-backbone模型性能甚至超过了最先进的DPT ViT-Large模型。与基于几何的重建方法相结合,我们提出了一种新的稠密三维场景重建流程,既能受益于稀疏点的尺度一致性,又能受益于单目深度估计方法的鲁棒性。通过对视频的每一帧依次预测深度图,我们可以重建出准确的三维场景几何信息。

基于DA多重插补法和电力物联网的电能数据缺失修复方法

针对电力物联网中电能数据量过多,缺失电能数据修复难度较大的问题,研究基于DA多重插补法和电力物联网的电能数据缺失修复方法。电力物联网利用感知层的电能数据采集终端采集电能数据,所采集电能数据利用通信层传送至应用层,应用层的电能数据缺失修复模块,利用EM插补算法计算电能数据缺失值的初始插补值;将所获取的电能数据插补值作为DA多重插补法的初始值,DA多重插补法利用局部加权回归模型,通过调整电能数据缺失值的预测误差,获取最终电能数据缺失修复结果。实验结果表明,该方法修复电力物联网电能数据的观测误差方差低于0.2,对于短期电能数据与长期电能数据,均具有良好的修复结果。

改进的稳健Lowess标准化算法在基因芯片中的应用

标准化是基因芯片预处理的一个重要的步骤。通过对基因芯片的标准化方法的研究,本文提出了一种算法与稳健Lowess标准化法的效果相当、效率有较大提高的算法。本算法首先利用局部加权线性回归对数据点进行平滑估计,然后再对得到的误差项运用核估计法进一步减小误差,最后对每个格子里的数据点进行缩放处理。实验证明了本算法的高效性。

Local attribute-similarity weighting regression algorithm for interpolating soil property values

Existing spatial interpolation methods estimate the property values of an unmeasured point with observations of its closest points based on spatial distance(SD).However,considering that properties of the neighbors spatially close to the unmeasured point may not be similar,the estimation of properties at the unmeasured one may not be accurate.The present study proposed a local attribute-similarity weighted regression(LASWR)algorithm,which characterized the similarity among spatial points based on non-spatial attributes(NSA)better than on SD.The real soil datasets were used in the validation.Mean absolute error(MAE)and root mean square error(RMSE)were used to compare the performance of LASWR with inverse distance weighting(IDW),ordinary kriging(OK)and geographically weighted regression(GWR).Cross-validation showed that LASWR generally resulted in more accurate predictions than IDW and OK and produced a finer-grained characterization of the spatial relationships between SOC and environmental variables relative to GWR.The present research results suggest that LASWR can play a vital role in improving prediction accuracy and characterizing the influence patterns of environmental variables on response variable.

Concentration Prediction of Total Flavonoids in Aurantii Fructus Extraction Process:Locally Weighted Regression versus Kinetic Model Equation Based on Fick's Law

Objective To predict the total flavonoids concentration of Aurantii Fructus fried with bran in its extraction process. Methods Ultraviolet spectrophotometry was used to determine the concentration of total flavonoids in different extraction time （t） and solvent load （M）. Then the predicted procedure was carried out using the following data： 1 ） based on Ficks second law, the parameters of the kinetic model could be deduced and the equation was established; 2） Locally weighted regression （LWR） code was developed in the WEKA software environment to predict the concentration. And then we used both methods to predict the concentration of total flavonoids in new experiments. Results After comparing the predicted results with the experimental data, the LWR model had better accuracy and performance in the prediction. Conclusion LWR is applied to analyze the extraction process of Chinese herb for the first time, and it is totally fit for the extraction. LWR-based system is a more simple and accurate way to predict than the established equation. It is a good choice especially for a process which exists no clear rules, and can be used in the real-time control during the process.

Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification

We develop an efficient,adaptive locally weighted projection regression(ALWPR)framework for uncertainty quantification(UQ)of systems governed by ordinary and partial differential equations.The algorithm adaptively selects the new input points with the largest predictive variance and decides when and where to add new localmodels.It effectively learns the local features and accurately quantifies the uncertainty in the prediction of the statistics.The developed methodology provides predictions and confidence intervals at any query input and can dealwithmulti-output cases.Numerical examples are presented to show the accuracy and efficiency of the ALWPR framework including problems with non-smooth local features such as discontinuities in the stochastic space.

STATISTICAL INFERENCES FOR VARYING-COEFFICINT MODELS BASED ON LOCALLY WEIGHTED REGRESSION TECHNIQUE

Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fited by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.

胃癌外周血淋巴细胞数关联因素的横断面研究

目的识别胃癌患者外周血淋巴细胞数(PBLC)的关联因素。方法横断面设计,收集行胃癌手术的患者资料,用单因素分析、多元线性回归、变量重要性评价,分析术前PBLC变化的关联因素。局部加权回归和稳健线性模型进一步验证。结果术前PBLC<1.1×10^(9)/L(A组)138例(20.72%),PBLC≥1.1×10^(9)/L(B组)528例(79.28%)。相对于B组,A组的年龄较大[(64.61±10.42)岁vs(62.18±10.41)岁,P<0.05],中性粒细胞较低[(3.21±1.41)×10^(9)/L vs(3.59±1.31)×10^(9)/L,P<0.01]。淋巴细胞减少与较高的胃癌分期有关,P<0.01。多元线性回归分析显示模型残差随机分布,年龄(β=-0.01,t=-3.70,P<0.01)、肿瘤分期[β(ⅡvsⅠ)=-0.16,t=-2.79,P<0.01;β(ⅢvsⅠ)=-0.18,t=-3.87,P<0.01;β(ⅣvsⅠ)=-0.21,t=-2.16,P<0.05]是淋巴细胞减少的关联因素,中性粒细胞增加与PBLC升高有关(β=0.05,t=3.61,P<0.01)。连续自变量的相对重要性分析显示,年龄、中性粒细胞、癌胚抗原(CEA)的LMG指标分别为55.55%、44.14%、0.31%。局部加权回归和稳健线性模型显示,年龄是PBLC的负向关联因素。结论胃癌PBLC与中性粒细胞正向关联。

基于PCA-GRD-LWR模型的海上油田中长期最大电力负荷预测

年最大负荷是合理配置电源、确定系统装机容量的重要理论依据,精确的预测结果可以减少海上油田平台的设备投资和运行成本。年最大负荷与油田产量、开采阶段等影响因素密切相关,分析影响负荷需求的特征量与最大负荷的内在联系及变化趋势,用主成分分析法对特征量进行处理,将相关性强的特征量转化为互不相关的主成分;计算各主成分与最大负荷之间的灰色关联度,根据关联程度确定回归模型的权重;建立基于灰色关联度的局部加权回归预测模型,并用粒子群算法优化局部加权回归模型的参数。以某海上油田的历史数据为例进行校验分析,结果表明,中长期负荷预测误差均小于3%,验证了所提方法的有效性,给出了未来10 a的最大负荷预测结果。

利用近邻因子提高二氧化氮遥感反演浓度的精度-基于随机森林算法

NO_(2)是损害健康和破坏生态的主要大气污染物。本文基于NASA提供的AuraOMI遥感反演NO_(2)浓度,利用采样点8km内的经济、人口、路网和坡度数据,以及气象、植被和高程的点值数据,采用随机森林算法、地理加权回归(GWR)和多尺度GWR方法提高NO_(2)浓度的预测精度。NASA原浓度R2为0.48,以上三种模型把交叉验证R2分别提高到0.74、0.71和0.70,其中随机森林算法的精度最高,该算法的均方根误差(RMSE)和平均绝对误差(MAE)分别只有6.4μg/m^(3)和4.98μg/m^(3),且其速度远快于多尺度GWR,预测精度也高于大部分现有的同等范围研究。在浓度修正方面,局部化经济人口路网因子对预测精度提高的贡献至少为11.24%。此外,基于随机森林算法还给出全国县级城市NO_(2)浓度估计值的分布图。

基于早期余震的地震烈度评估方法研究——以2021年青海玛多Ms7.4地震为例

余震序列与发震断层间的关系密切,在破坏性地震发生后,常通过余震序列空间分布判断发震断层的位置。可以根据震后短期内余震序列的空间分布识别主震破裂面的基本特征。Ozawa等(2021)通过对几何复杂断裂带中的主震和余震序列进行模拟,发现非常早期的余震分布可以很好地描述断层破裂的程度,而且早期的余震多集中在主震破裂的边界附近.

基于局部加权回归及经验模态分解的地心运动降噪方法

针对目前地心运动序列包含复杂噪声、真实信号难以有效提取等问题,提出一种结合局部加权回归(locally weighted regression, LOESS)和经验模态分解(empirical mode decomposition, EMD)的降噪方法LOESS-EMD。该方法首先对地心运动序列进行局部加权回归拟合,得到拟合后的时间序列和残差序列;然后对残差序列进行经验模态分解,从中提取出低频信号;最后将拟合的时间序列和残差中的低频信号进行重构,得到降噪后的时间序列。在仿真实验中,相比于LOESS方法,LOESS-EMD方法降噪结果的均方根误差减小31%,信噪比和剩余能量百分比分别提高16%和0.16百分点。利用该方法对国际GNSS服务IGS第三次重处理(3rd reprocessing campaign, Repro3)提供的地心运动序列进行降噪分析,结果表明,LOESS-EMD方法能够有效减少地心运动序列的噪声。

Approximation by randomly weighting method in censored regression model

Censored regression ("Tobit") models have been in common use, and their linear hypothesis testings have been widely studied. However, the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters. In this paper, we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic. It is shown that, under both the null and local alternative hypotheses, conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic. Therefore, the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters. At the same time, we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model. Simulation studies illustrate that the per-formance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis.