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Comparison of multinomial logistic regression and logistic regression： which is more efficient in allocating land use？

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Frontiers of Earth Science 2014年第4期8卷 512-523页

作者： Yingzhi LIN Xiangzheng DENG Xing LI Enjun MA School of Mathematics and Physics China University of Geosciences Wuhan 430074 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China Center for Chinese Agricultural Policy Chinese Academy of Sciences Beijing 100101 China

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/ allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

关键词： multinomial logistic regression land usechange logistic regression land use suitability land useallocation

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Optimal Poisson Subsampling for Softmax regression

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Journal of Systems Science & Complexity 2023年第4期36卷 1609-1625页

作者： YAO Yaqiong ZOU Jiahui WANG Haiying Department of Statistics University of ConnecticutStorrsCT06269USA School of Statistics Capital University of Economics and BusinessBeijing 100070China

Softmax regression,which is also called multinomial logistic regression,is widely used in various fields for modeling the relationship between covariates and categorical responses with multiple *** increasing volumes of data bring new challenges for parameter estimation in softmax regression,and the optimal subsampling method is an effective way to solve ***,optimal subsampling with replacement requires to access all the sampling probabilities simultaneously to draw a subsample,and the resultant subsample could contain duplicate *** this paper,the authors consider Poisson subsampling for its higher estimation accuracy and applicability in the scenario that the data exceed the memory *** authors derive the asymptotic properties of the general Poisson subsampling estimator and obtain optimal subsampling probabilities by minimizing the asymptotic variance-covariance matrix under both A-and L-optimality *** optimal subsampling probabilities contain unknown quantities from the full dataset,so the authors suggest an approximately optimal Poisson subsampling algorithm which contains two sampling steps,with the first step as a pilot *** authors demonstrate the performance of our optimal Poisson subsampling algorithm through numerical simulations and real data examples.

关键词： multinomial logistic regression optimality criterion optimal subsampling

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The Perception of Flood Risks: A Case Study of Babessi in Rural Cameroon

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International Journal of Disaster Risk Science 2021年第4期12卷 458-478页

作者： Gertrud Buchenrieder Julian Brandl Azibo Roland Balgah Research Center RISK Institute of Sociology and EconomicsUniversität der Bundeswehr München85577NeubibergGermany Leibniz Institute of Agricultural Development in Transition Economies(IAMO) 06120Halle(Saale)Germany TUM School of Life Sciences Weihenstephan Technical University Munich(TUM)85354MunichGermany College of Technology The University of Bamenda(UBa)BamendaNorth West RegionCameroon

Although risk perception of natural hazards has been identified as an important determinant for sound policy design,there is limited empirical research on it in developing *** article narrows the empirical literature *** draws from Babessi,a rural town in the Northwest Region of *** was hit by a severe flash flood in *** cross-disciplinary lens applied here deciphers the complexity arising from flood hazards,often embedded in contexts characterized by poverty,a state that is constrained in disaster relief,and market-based solutions being *** data were collected via snowball *** logistic regression analysis suggests that individuals with leadership functions,for example,heads of households,perceive flood risk higher,probably due to their role as household *** found that risk perception is linked to location,which in turn is associated with religious *** perceive floods riskier than Muslims because the former traditionally reside at the foot of hills and the latter uphill;rendering Muslims less exposed and eventually less affected by ***,public disaster relief appears to have built up trust and subsequently reduced risk perception,even if some victims remained skeptical of state disaster *** indicates strong potential benefits of public transfers for flood risk management in developing countries.

关键词： Flood disaster multinomial logistic regression Risk perception Rural Cameroon

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Characterizing diseases using genetic and clinical variables:A data analytics approach

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Quantitative Biology 2024年第3期12卷 271-285页

作者： Madhuri Gollapalli Harsh Anand Satish Mahadevan Srinivasan Engineering Department Penn State Great ValleyMalvernPennsylvaniaUSA Department of Systems and Information Engineering School of Engineering and Applied ScienceUniversity of VirginiaCharlottesvilleVirginiaUSA

Predictive analytics is crucial in precision medicine for personalized patient *** aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease *** achieve this,experiments were performed on diseased tissues obtained from the L1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical *** this study,the k-means technique was used for clustering the diseased tissue types,and the multinomial logistic regression(MLR)technique was applied for classifying the diseased tissue *** reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical *** results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 non-landmark genes,and the difference is statistically ***,it was evident that both clinical and genetic variables were important in predicting the diseased tissue *** top three clinical predictors for predicting diseased tissue types were identified as morphology,gender,and age of ***,this study explored the possibility of using the latent representations of the clusters of landmark and non-landmark genes as predictors for an MLR *** classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical *** study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine,enhancing diagnostic accuracy.

关键词： clustering k-means L1000 dataset analysis landmark genes multinomial logistic regression non-landmark genes principal component analysis tissue classification

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Identifying the Factors Affecting the Injury Severity of Pedestrians and E-bike Crashes:a Data-mining and Statistics Combined Method

Identifying the Factors Affecting the Injury Severity of Ped...

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作者： Denisa Sulai 长沙理工大学

学位级别：硕士

随着电动自行车用户及行人数量的增加,行人和电动自行车用户的安全受到广泛关注。由于该类出行者与机动车共用道路,容易诱发交通事故,导致出行者遭受严重伤害,此类伤害程度远高于机动车与机动车之间的碰撞。因此有必要针对影响行人交通... 详细信息

随着电动自行车用户及行人数量的增加,行人和电动自行车用户的安全受到广泛关注。由于该类出行者与机动车共用道路,容易诱发交通事故,导致出行者遭受严重伤害,此类伤害程度远高于机动车与机动车之间的碰撞。因此有必要针对影响行人交通事故的因素开展深入研究,以降低或消除该类交通事故的发生,保障出行安全。本文基于湖南省2014年至2016年发生的8172交通的事故数据,筛选影响事故的主要因素,包括车辆违规驾驶行为(闯红灯,超度、疲劳、逆向和酒驾等;行人违规(行人在机动车道上行走)和环境因素(能见度,照明条件和事故发生的时间等),研究分析影响骑行者的伤害严重性和撞车事故中行人受伤的核心因素。主要研究内容及结论主要包括以下两个方面:(1)结合K-Means聚类分析法和多项式logistic回归模型(MNLR)方法,解析事故因素对死亡,重伤和轻伤三种不同行人碰撞伤害严重程度的影响。研究结果表明:驾驶员的疲劳、酒驾、闯红灯、逆向违规驾驶行为、行人在车道上行走,能见度,行人年龄(65岁以上),天气条件和道路类型对行人的伤害严重程度具有显著影响。(2)提出了分类树和逻辑回归模型的组合方法,以获取不同群体的电动自行车驾手遭受严重伤害的潜在风险因素。回归树分析研究表明,城市地区55岁以上的非职业驾驶员遭受严重伤害的可能性更高;回归分析结果表明,高度道路等危险因素对不同组的伤害具有显著影响,而主要因素仅对特定组具有显著影响。

关键词：行人事故道路安全伤害程度安全影响因素电动自行车 K-Means multinomial logistic regression Tree classification CHAID logistic regression analysis

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Investigating the factors affecting traffic violations based on electronic enforcement data:A case study in Shangyu,China

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Journal of Southeast University(English Edition) 2021年第2期37卷 227-236页

作者： Fan Haoxuan Ren Gang Li Haojie Ma Jingfeng School of Transportation Southeast University Nanjing 211189 China

To study the influencing factors of traffic violations,this study investigated the effects of vehicle attribution,day of week,time of day,location of traffic violations,and weather on traffic violations based on the electronic enforcement data and historical weather data obtained in Shangyu,*** categories of traffic violations were determined from the raw ***,chi-square tests were used to analyze the relationship between traffic violations and the potential risk *** logistic regression analyses were conducted to further estimate the effects of different risk factors on the likelihood of the occurrence of traffic *** analyzing the results of chi-square tests via SPSS,the five factors above were all determined as significant factors associated with traffic *** results of the multinomial logistic regression revealed the significant effects of the five factors on the likelihood of the occurrence of corresponding traffic *** conclusions are of great significance for the development of effective traffic intervention measures to reduce traffic violations and the improvement of road traffic safety.

关键词： traffic violations road traffic safety electronic enforcement data multinomial logistic regression influencing factors

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Farmer's Perception on Supply-Demand Matching of New Variety and Its Influence Factors

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Asian Agricultural Research 2016年第8期8卷 53-59页

作者： Qingjie HUANG College of Economics and Management Huazhong Agricultural University

Using disordered multinomial logistic regression and multiple linear regression method,385 copies of questionnaires on farmer are analyzed to explore the relationship between peasant's psychological traits,peasant's cognition on seed technology and perception on supplydemand matching of new *** results show that the vast majority of farmers think that current new variety is at high-level supplydemand balance and the oversupply status,and updating speed of new variety on the market is faster;the farmers preferring risk,seeking innovation and having strong learning and cognition ability may select high-level supply-demand matching state,and the farmers understanding the importance and difference of seed technology tend to choose high-level supply-demand matching situation;the farmers with strong learning and cognition ability can acknowledge the importance and difference of seed technology,while the farmers preferring risk can perceive the difference of seed technology;psychology seeking the innovation and learning and cognition ability affect the farmer's perception on supplydemand matching status of new variety via affecting the farmer's cognition on technical difference.

关键词： Crop seed Perception of supply-demand matching status Seed technology cognition multinomial logistic regression

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Costa Rican Poverty Level Prediction

Costa Rican Poverty Level Prediction

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2020城市建设与展望——第三届粤港澳大湾区研究生论坛

作者： Sheng WANG Yu ZHAO Yiwei ZHAO College of Professional Study Northeastern University Institute of Data Science City University of Macau Shanxi University of Finance and Economics

With the increasing gap between the rich and the poor in the world,how to identify the poor people and provide effective help for them is a problem concerned by the *** the process of quantifying poverty from qualitative to quantitative is complex,the Costa Rican government has provided data to help identify truly poor families We have used a dataset of Inter-American Development Bank,which consists of socio-economic conditions of Costa Rican Households such as education background,family details,home,electricity details,***-American Development Bank gives this data set on the Kaggle *** data set contains 143 variables like home rent,house conditions,education details,household information,*** it contains 9558 rows of *** need to predict the poverty level（Target variable） of householdbased *** target variable is an ordinal variable indicating groups of income level,i.e.,1= extreme poverty,2=moderate poverty,3= vulnerable households,4=non-vulnerable *** this project,we have used statistical analysis tool R for analysis and visualizing *** to the industry standard method of data mining,we carried out the following six stages:data requirements,data acquisition,data preparation,data cleansing,exploratory data analysis,modeling and ***,an effective model is *** analyzing socio-economic conditions of household’s characteristics,governments can identify which households have the highest need for social welfare *** accurately predicting the household poverty level,we can help Guangdong-Hong Kong-Macau Greater Bay Area to assess the social need.

关键词： Costa Rican Poverty Gradient Boosting Model Decision Tree multinomial logistic regression

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