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.

Biomass estimation of Shorea robusta with principal component analysis of satellite data

Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal （Shorea robusta C. F. Gaertn）. Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components （PCs） of the bands, vegetation indices （VI）, and the PCs of the VIs respectively and multiple linear regression （MLR） analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs.

Urban Metabolism Based on Emergy and Slack Based Model: A Case Study of Beijing, China

The key to studying urban sustainable development depends on quantifying stores, efficiencies of urban metabolisms and capturing urban metabolisms′ mechanisms. This paper builds up the metabolic emergy account and quantifies some important concepts of emergy stores. Emphasis is placed on the urban metabolic model based on the slack based model(SBM) method to measure urban metabolic efficiencies. Urban metabolic mechanisms are discussed by using the regression method. By integrating these models, this paper analyzes the urban metabolic development in Beijing from 2001 to 2010. We conclude that the metabolic emergy stores of Beijing increased significantly from 2001 to 2010, with the emergy imported accounting for most of the increase. The metabolic efficiencies in Beijing have improved since the 2008 Olympic Games. The population, economic growth, industrial structures, and environmental governance positively affect the overall urban metabolism, while the land expansion, urbanization and environmentally technical levels hinder the improving of urban metabolic efficiencies. The SBM metabolic method and the regression model based on the emergy analysis provide insights into the urban metabolic efficiencies and the mechanism. They can promote to integrate such concepts into their sustainability analyses and policy decisions.

The Establishment of Mathematical Models for the Composition Analysis and Identification of Ancient Glass Products

Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.

A Recursive Binary Tree Model for the Analysis of the Response to Antiretroviral Therapy of HIV Infected Adults in Burkina Faso

In this paper we aim to analyse temporal variation of CD4 cell counts for HIV-infected individuals under antiretroviral therapy by using statistical methods. This is achieved by resorting to recursive binary regression tree approach [1]?[2]. This approach has made it possible to highlight the existence of several segments of the population of interest described by the interactions between the predictive covariates of the response to the treatment regimen.

Functional Analysis of Chemometric Data

The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.

Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model

Traumatic brain injury(TBI) at a young age can lead to the development of long-term functional impairments. Severity of injury is well demonstrated to have a strong influence on the extent of functional impairments;however, identification of specific magnetic resonance imaging(MRI) biomarkers that are most reflective of injury severity and functional prognosis remain elusive. Therefore, the objective of this study was to utilize advanced statistical approaches to identify clinically relevant MRI biomarkers and predict functional outcomes using MRI metrics in a translational large animal piglet TBI model. TBI was induced via controlled cortical impact and multiparametric MRI was performed at 24 hours and 12 weeks post-TBI using T1-weighted, T2-weighted, T2-weighted fluid attenuated inversion recovery, diffusion-weighted imaging, and diffusion tensor imaging. Changes in spatiotemporal gait parameters were also assessed using an automated gait mat at 24 hours and 12 weeks post-TBI. Principal component analysis was performed to determine the MRI metrics and spatiotemporal gait parameters that explain the largest sources of variation within the datasets. We found that linear combinations of lesion size and midline shift acquired using T2-weighted imaging explained most of the variability of the data at both 24 hours and 12 weeks post-TBI. In addition, linear combinations of velocity, cadence, and stride length were found to explain most of the gait data variability at 24 hours and 12 weeks post-TBI. Linear regression analysis was performed to determine if MRI metrics are predictive of changes in gait. We found that both lesion size and midline shift are significantly correlated with decreases in stride and step length. These results from this study provide an important first step at identifying relevant MRI and functional biomarkers that are predictive of functional outcomes in a clinically relevant piglet TBI model. This study was approved by the University of Georgia Institutional Animal Care and Use Committee(AUP: A2015 11-001) on December 22, 2015.

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.

量纲统一在滑坡易发性评价中的影响分析

以往的区域性滑坡易发性评价研究多以对比不同评价模型结果和改进模型为主,而忽视了所选致灾因子的信息保留以及因子量纲如何统一的问题。为探究致灾因子的相关性和量纲对易发性评价结果的影响,以甘肃省靖远县北部地区作为研究区,选取高程、坡度、坡向和地形起伏度等12个因子,利用主成分分析提取的新因子参与易发性评价,并采用数据标准化、滑坡密度和信息量值替代法统一致灾因子的量纲,最后基于GIS平台绘制研究区滑坡易发性分区图。通过ROC曲线评估各模型的易发性评价结果精度。结果表明:在信息量模型、逻辑回归模型和感知机模型中,经主成分分析处理的因子得到的模型评价结果精度更高,采用信息量值替代法统一因子的量纲能够进一步提升逻辑回归和感知机模型的评价结果精度;同时,3种评价模型中感知机模型的结果精度最高(AUC=0.9367),优于信息量模型(AUC=0.9173)和逻辑回归模型(AUC=0.9272),是该研究区滑坡易发性评价的理想模型,应优先考虑。研究结果可为类似地区的防灾减灾工作提供基础数据和理论参考。

新疆喀什地区疏附县肺结核与气象因素和大气污染物关联研究

目的探讨新疆喀什地区疏附县气象因素和大气污染物对肺结核发病的影响。方法收集喀什地区疏附县2019年1月1日至2021年12月31日的肺结核每周发病、气象因素和大气污染物等数据。利用主成分分析和分位数回归模型分析不同气象因素和大气污染物对肺结核发病的影响。结果Spearman相关性分析结果显示除了自变量AQI、PM_(2.5)与RH和CO、WS、NO_(2)与PM_(10)相互之间没有相关关系外,其余自变量与因变量和各自变量之间均有较显著的相关关系。主成分分析提取C1、C2、C3和C4四个主成分并得出表达式。分位数回归模型结果显示,截距在不同分位点均有统计学意义。主成分C1在0.3和0.6分位点以外的其他分位点P值均<0.05,有统计学意义,表明在0.1、0.2、0.4、0.5、0.7、0.8和0.9分位点对肺结核发病有影响;主成分C2在0.1至0.6分位点P<0.05,表明在0.1~0.6分位点对肺结核发病有显著影响;主成分C3也在0.1至0.6分位点P值<0.05,有统计学意义,表明在0.1~0.6位点的自变量对肺结核发病有明显效应;主成分C4在0.1~0.9分位P值均>0.05,没有统计学意义,表明主成分C4包含的因素对肺结核发病无明显效应。C1、C2、C3和C4四个主成分参数估计结果显示主成分C1系数随着分位点的增大而下降;主成分C2、C3系数随着分位点的增大而呈现逐渐上升的趋势;主成分C4随着分位点的增大而呈现先下降后上升的趋势;表明各主成分在不同分位点对肺结核发病产生不同的效应。结论本文用主成分分位数回归模型分析了气象因素和大气污染物对肺结核发病的影响,研究表明温度、湿度、CO、NO_(2)、O_(3)、PM_(2.5)、PM_(10)和SO_(2)等对肺结核发病均有影响。

基于Logistic回归模型和PCA模型的急性缺血性脑卒中发作影响因素分析

目的应用Logistic回归模型和主成分分析(PCA)模型分析急性缺血性脑卒中(AIS)发作的影响因素。方法从大连医科大学附属第二医院医渡云科研大数据服务器系统提取2001年1月1日至2021年12月31日的数据,将医院病历系统收录的55620例AIS患者纳入病例组,将筛选后的64134例在医院体检中心体检的人群纳入对照组。收集两组的临床资料,分析AIS发作的影响因素。结果多因素分析结果显示,年龄、性别、肌酐、白细胞计数、血红蛋白、红细胞计数、血小板计数、甘油三酯(TG)、低密度脂蛋白胆固醇(LDL-C)、高密度脂蛋白胆固醇(HDL-C)、丙氨酸氨基转移酶(ALT)、总胆固醇(TC)、γ-谷氨酰转移酶(γ-GGT)水平及吸烟史、饮酒史、高血压、糖尿病、心梗、冠心病、动脉粥样硬化为AIS的影响因素(P<0.05);受试者工作特征(ROC)曲线下面积(AUC)为0.927。PCA提取8个主成分,既往病史、血脂水平风险比较大。结论既往病史及血脂水平是AIS发作的主要影响因素,有效控制原发疾病及血脂水平能够更好控制AIS发作。

基于主成分分析的回归模型对重度创伤性脑损伤去骨瓣术后脑积水的评估价值

目的探讨基于主成分分析的回归模型对重度创伤性脑损伤去骨瓣术后脑积水的评估价值。方法选取邯郸市中心医院2022-01—2023-10收治的92例重度创伤性脑损伤患者,均接受去骨瓣术治疗。根据患者术后是否发生脑积水分为发生组(20例)及未发生组(72例),比较2组临床资料,并对其进行主成分分析,然后提取主成分作为自变量,采用主成分分析-Logistic回归分析重度创伤性脑损伤去骨瓣术后脑积水的危险因素,采用受试者工作特征曲线评估模型对患者发生脑积水的预测效能。结果单因素分析显示,发生组脑震荡、脑内血肿、术后颅内感染、术后大面积脑梗死、手术前后脑室系统出血占比、受伤距离手术时间、手术前后CGS评分、白细胞计数均高于未发生组(P<0.05)。主成分分析-Logistic回归分析显示,合并脑内血肿、颅内感染、术后大面积脑梗死、手术前后脑室系统出血、手术前后GCS评分低是重度创伤性脑损伤去骨瓣术后发生脑积水的独立危险因素(P<0.05)。主成分分析-Logistic回归模型评估重度创伤性脑损伤去骨瓣术后发生脑积水的灵敏度为90.00%,特异度为94.44%,AUC为0.958。结论主成分分析-Logistic回归模型能够有效评估重度创伤性脑损伤去骨瓣术患者的预后情况,脑内血肿、颅内感染、术后大面积脑梗死、脑室系统出血、GCS评分等是影响患者发生脑积水的重要因素。

河南荥阳市耕地土壤重金属分布特征及来源解析

耕地质量关系着人民生活,而重金属是影响耕地质量的重要因素之一。根据全国土壤污染状况调查显示,中国耕地环境状况不容乐观,对耕地的重金属调查分析迫在眉睫。但仅简单地对重金属含量水平及来源类型进行判断已不足以为区域土壤重金属污染治理提供支持,而通过对各类污染源贡献率的定量计算,不仅可以明确农田土壤重金属分布特征,同时可判别污染源类别及来源,从而识别优先控制的污染元素,为重金属污染精准管控提供关键信息。本文采集河南荥阳市耕地表层土壤样品(0~20cm),应用电感耦合等离子体质谱和发射光谱法(ICP-MS/OES)、原子荧光光谱法(AFS)及离子选择电极法(IES)对As、Cd、Cr、Cu、Hg、Ni、Pb、Zn等8种重金属进行测试和p H分析;利用多元统计、绝对因子分析-多元线性回归(APCS-MLR)受体模型探讨研究区8种重金属污染含量空间分布特征及来源,利用富集因子和地累积指数开展土壤污染评价。结果表明:(1)耕地土壤中重金属含量整体偏高。除Cr外,其他元素为郑州市土壤背景值的1.04~1.40倍,其中Cd的累积效应较明显。(2)研究区重金属高值区主要分布于荥阳市城区周边。(3)基于富集因子法、相关性分析、主成分分析及APCS-MLR源解析结果显示,研究区重金属主要有三个来源:自然源对Ni、As、Cu、Cr的贡献率分别为98%、94%、80%及63%;工业源对Cd的贡献率为78%;其他源则主要是农业化肥源、燃煤源的混合源,对Cr、Pb、Hg的贡献率分别为37%、35%及33%。(4)地累积指数表明,研究区各重金属以无污染为主,而Cd超标率最高,其中度、中-重度污染、重度污染样点数分别为19个、5个及3个,并存在1个极重度污染样点。综上,Cd在研究区耕地中富集较明显,为潜在的主要污染元素;工业源、自然源、农业化肥源及燃煤源是重金属的主要来源,表明人类活动已对研究区耕地产生影响,需采取措施避免该影响进一步加剧。

基于太赫兹时域光谱和PCA-SVM算法的甜蜜素含量分析

光谱分析是研究太赫兹(THz)辐射与物质相互作用的重要手段。采用全光纤式THz时域光谱(THz Time-Domain Spectroscopy,THz-TDS)系统测试了不同含量甜蜜素样品的透过率光谱,发现甜蜜素的特征吸收峰位置在1.4 THz和1.7 THz附近;采用主成分分析结合支持向量机(PCA-SVM)的方法建立了甜蜜素含量回归模型,然后将其预测结果与遗传算法结合偏最小二乘(GA-PLS)模型进行分析比较,并引入决定系数(R 2)和预测均方根误差(RMSE)来评价建模效果,对以10%含量梯度制作的样品集进行检测。研究结果表明,采用PCA-SVM、SVM和GA-PLS方法建立的预测模型的RMSE分别为1.885%、1.926%和2.432%。因此,PCA-SVM方法的预测效果最优,且预测数据与实际数据均表现出良好的相关性,获得了效果良好的含量回归预测模型,为甜蜜素含量的检测与分析提供了一种有效手段。

基于改进自适应增强算法的混煤发热量预测方法

为解决传统多元线性回归(Multivariate linear regression,MLR)模型在煤炭发热量预测方面精度不足和适用性有限的问题,提出了一种基于改进自适应增强算法(Adaptive boosting,Adaboost)的煤发热量的预测模型。将随机森林(Random forest,RF)作为Adaboost的基学习器,以提高模型在工业煤质分析中的发热量预测精度和泛化能力。研究基于某电厂1万组入炉煤的工业分析数据,选取水分、挥发分、灰分和固定碳作为模型输入,建立煤炭低位发热量的预测模型。通过与传统的多元线性回归方程及其他非线性模型比较,模型展现出更高的预测精度和更好的泛化能力。大样本测试的实验结果表明,本模型的平均绝对百分比误差为0.5417%,均方根误差为0.1304 MJ/kg,拟合度(R^(2))达到0.9799,其在煤炭发热量预测方面优于其他模型。此外,200组真实的混煤工业分析数据的模拟验证,进一步确认了本模型较优的泛化性能。

消费者预制菜购买意愿及影响因素研究——以西安市为例

近年来,在居民生活水平显著提高、“懒宅经济”盛行、新冠疫情、烹饪便捷性需求逐年提升等多因素影响下,国内预制菜行业迅速发展。本文运用数理统计方法,对西安市居民的预制菜购买意愿及影响因素进行调查分析。结果表明,西安市居民对预制菜的购买意愿较高,虽然,对于产品质量问题存在担忧,但同时也看重预制菜的便捷。对此,本研究提出以下建议:政府要充分发挥主导作用,健全相关法律法规,加快冷链物流体系建设;预制菜生产企业要加强产品的宣传和推广,增强品牌意识,提高产品的质量和安全性,促进预制菜市场的健康发展。

特殊教育普惠发展的统计建模

特殊教育现代化是教育现代化不可或缺的一部分,强化特殊教育普惠发展是特殊教育现代化的努力方向。从强化特殊教育发展的“普”与“惠”两方面入手,运用TF-IDF算法、主成分分析法以及多元线性回归方法,并与国外构建测量特殊教育普惠发展的统计模型,依据该模型分析我国20多年来的特殊教育普惠发展水平,并与国外进行对比发现,我国特殊教育普惠发展态势向上向好,但仍存在地区发展水平不平衡不充分、特殊教育经费投入占地区教育经费投入较低等问题。针对以上问题,建议保障特殊教育办学经费投入,改善特殊教育资源布局,加强专业化特殊教育教师队伍建设。

基于PCA-SBM的轨道交通站点接驳评价体系——以厦门市轨道交通站点为例

以慢行交通规划为指导,搭建基于PCA-SBM(主成分分析-基于松弛值测算)的效率评价模型,从管理者视角对厦门市轨道交通单车接驳现状作出评价。研究发现:厦门市站点接驳效率整体偏低,多呈现高投入、中回报的数据表现,需加强高峰日的引导轮次;电子围栏使用率低,应加强政企联动,做好及时的高峰时期车辆调度与用户前端引导,提升用户对电子围栏的感知,推动落实智能化管理。

四川省长江上游水土保持工程生态经济综合评价

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