Sonic Hedgehog Medulloblastoma(SHH-MB)is one of the four primary molecular subgroups of Medulloblastoma.It is estimated to be responsible for nearly one-third of allMB cases.Using transcriptomic and DNA methylation pr...Sonic Hedgehog Medulloblastoma(SHH-MB)is one of the four primary molecular subgroups of Medulloblastoma.It is estimated to be responsible for nearly one-third of allMB cases.Using transcriptomic and DNA methylation profiling techniques,new developments in this field determined four molecular subtypes for SHH-MB.SHH-MB subtypes show distinct DNAmethylation patterns that allow their discrimination fromoverlapping subtypes and predict clinical outcomes.Class overlapping occurs when two or more classes share common features,making it difficult to distinguish them as separate.Using the DNA methylation dataset,a novel classification technique is presented to address the issue of overlapping SHH-MBsubtypes.Penalizedmultinomial regression(PMR),Tomek links(TL),and singular value decomposition(SVD)were all smoothly integrated into a single framework.SVD and group lasso improve computational efficiency,address the problem of high-dimensional datasets,and clarify class distinctions by removing redundant or irrelevant features that might lead to class overlap.As a method to eliminate the issues of decision boundary overlap and class imbalance in the classification task,TL enhances dataset balance and increases the clarity of decision boundaries through the elimination of overlapping samples.Using fivefold cross-validation,our proposed method(TL-SVDPMR)achieved a remarkable overall accuracy of almost 95%in the classification of SHH-MB molecular subtypes.The results demonstrate the strong performance of the proposed classification model among the various SHH-MB subtypes given a high average of the area under the curve(AUC)values.Additionally,the statistical significance test indicates that TL-SVDPMR is more accurate than both SVM and random forest algorithms in classifying the overlapping SHH-MB subtypes,highlighting its importance for precision medicine applications.Our findings emphasized the success of combining SVD,TL,and PMRtechniques to improve the classification performance for biomedical applications with many features and overlapping subtypes.展开更多
To stay competitive, the mobile telecommunication companies spend millions of Ghana cedi each year on building long-term relationships with their customers. Marketing managers are constantly challenged with the proble...To stay competitive, the mobile telecommunication companies spend millions of Ghana cedi each year on building long-term relationships with their customers. Marketing managers are constantly challenged with the problem of where to channel the limited resources in order to retain existing customers. This study approaches the customer retention problem in the mobile phone sector from a behavioural perspective, applying the Behavioural Perspective Model as the main analytical framework and further exploits some other factors that influence customer retention. The model includes a set of pre-behaviour and post-behaviour factors to study consumer choice, and explains its relevant drivers in a viable and comprehensive way, grounded in radical behaviourism. Data for the analysis were collected from tertiary students from Accra and Takoradi. Data collected were analysed using the multinomial regression technique. Analysis of the data revealed that the Behaviour setting factor is the only significant element in Behaviour Perspective Model. Further exploitation of behaviour situation revealed that the number of networks a customer uses, previous experience of a customer and customer’s intention are significant factors in determining customer retention in Ghana’s mobile telecommunication industry.展开更多
The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for...The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.展开更多
Rice straw,a by-product of rice cultivation,is commonly disposed of through open-field burning,which contributes to air pollution and environmental degradation.This study aims to identify the key factors influencing f...Rice straw,a by-product of rice cultivation,is commonly disposed of through open-field burning,which contributes to air pollution and environmental degradation.This study aims to identify the key factors influencing farmers’decisions on rice straw management and to develop policy recommendations that encourage the sustainable utilization of rice straw within the supply chain.A mixed-methods approach was adopted,combining qualitative interviews with nine key informants and a quantitative survey of 585 rice farmers across Thailand.Multinomial Logit Regression(MLR)was employed to analyze farmers’preferences among four management options:burning,composting,animal feeding,and selling.The results reveal that membership in farmer groups,ownership of livestock,access to baling machinery,knowledge,and skills related to straw utilization,ease of field access,availability of storage facilities,engagement in integrated farming,and year-round access to baling services significantly increased the likelihood of choosing sustainable alternatives over the burning straw.These findings underscore the importance of both capacity-building and infrastructure in enabling sustainable practices.Based on these insights,the study proposes a multi-level policy framework to enhance the value creation of rice straw.National policies should focus on expanding access to machinery and supporting innovation,while local governments should facilitate farmer training and improve straw logistics.Strengthening farmer organizations and market connections is also crucial for scaling adoption.Overall,structural integration and stakeholder coordination are key to reducing straw burning and promoting sustainable resource use in rice-producing regions.展开更多
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 th...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.展开更多
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 e...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,China.Ten categories of traffic violations were determined from the raw data.Then,chi-square tests were used to analyze the relationship between traffic violations and the potential risk factors.Multinomial logistic regression analyses were conducted to further estimate the effects of different risk factors on the likelihood of the occurrence of traffic violations.By analyzing the results of chi-square tests via SPSS,the five factors above were all determined as significant factors associated with traffic violations.The results of the multinomial logistic regression revealed the significant effects of the five factors on the likelihood of the occurrence of corresponding traffic violations.The conclusions are of great significance for the development of effective traffic intervention measures to reduce traffic violations and the improvement of road traffic safety.展开更多
The subject of this study is the microcredit market in the USA,more specifically in Florida.The justification for choosing this specific state is the massive presence of the Hispanic population.This will facilitate a ...The subject of this study is the microcredit market in the USA,more specifically in Florida.The justification for choosing this specific state is the massive presence of the Hispanic population.This will facilitate a generalization of the obtained results to the microcredit market in Latin American countries.Thus,the objective of this study is to analyze the profile of microcredit holders and their companies from socioeconomic and financial points of view.As our data also consider the degree of repayment of the microloans included in the sample,the clients’profile is related to the punctuality or default of their corresponding loan repayments using the methodology of multi-nomial logit regression.The variables used in this study refer to personal information concerning borrowers(gender,age,education level,and marital status),the economic situation of their respective companies(closeness to the lender,number of workers,and revenues),and the characteristics of granted loans(principal,term,and purpose).However,the results of the regression show that only two variables are significant at the 5%significance level:the borrower’s age,which has a positive effect on repay-ment punctuality,and the loan term,which exhibits a negative effect.The findings of this study have clear implications,as they can help lenders design suitable microloans adjusted to customer profiles.Finally,future research should include other demograph-ics and characteristics of affected companies.展开更多
A follow-up study with 7,826 representative newly married couples for fifteen months after their weddings in Shanghai Municipality showed that among the 3, 412 couples who actually adopted contraceptive method, rhythm...A follow-up study with 7,826 representative newly married couples for fifteen months after their weddings in Shanghai Municipality showed that among the 3, 412 couples who actually adopted contraceptive method, rhythm was the main choice; the proportion for couples taking the contraceptive pill was much higher among sexually active couples before their weddings. The proportions of adopting rhythm or condom or the both, however, increased afterwards.About 86% of couples who had ever planned adopting the rhythm at registration actually used it. In fact, 16% of those who had ever planned to take pills eventually made this choice, because of their worry about any adverse side effects on mother's and fetus' health. Their knowledge about contraception,especially the pills, was incomprehensiue. APProximately 62% of condom users had not been given any instruction regarding its use when they got this contracoptive device one year later. Half of the pill and spermicide users learnt these respective methods from their friends or relatives. The proportion of delivering contraceptiues alter marriage by;F.P.P. was rather low. By fitting the multinomial logistic regression model, it is indicated that couple's evaluation on contraceptiue methods and contraceptiue goal were the main factors determining newlyweds' method of choice. Wife's knowledge on contraception and the accessibility of contraceptives and devices also influenced the method choice to some extent.展开更多
A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial l...A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.展开更多
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...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 variety.Research 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.展开更多
A field survey was carried out to model farmers’ vulnerability to climate variability and extreme events in selected agroecological zones in Kitui County. The indicator approach was used to calculate the overall hous...A field survey was carried out to model farmers’ vulnerability to climate variability and extreme events in selected agroecological zones in Kitui County. The indicator approach was used to calculate the overall household vulnerability index, where Principal Component Analysis (PCA) was used to allocate weights to indicators of exposure, sensitivity and adaptive capacity. Multinomial logistic regression was run in Stata to model the influence of socioeconomic characteristics on farmers’ vulnerability levels. The study established that different socioeconomic characteristics of households had a varying influence on the households’ vulnerability levels. Proximity to the Market and the arid agroecological zone significantly reduced the probability of a household belonging to the low and moderate vulnerability categories. On the other hand, the education level and the semi-humid zone significantly increased the odds of a household belonging to the low vulnerability category. Further, access to credit facilities and the semi-humid agroecological zone significantly increased the odds of a household belonging to the moderate vulnerability category. The study thus recommends that policy interventions should target specific socioeconomic characteristics that influence households’ vulnerability to climate variability and extreme events.展开更多
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 levels.The increasing v...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 levels.The increasing volumes of data bring new challenges for parameter estimation in softmax regression,and the optimal subsampling method is an effective way to solve them.However,optimal subsampling with replacement requires to access all the sampling probabilities simultaneously to draw a subsample,and the resultant subsample could contain duplicate observations.In this paper,the authors consider Poisson subsampling for its higher estimation accuracy and applicability in the scenario that the data exceed the memory limit.The 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 criteria.The 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 phase.The authors demonstrate the performance of our optimal Poisson subsampling algorithm through numerical simulations and real data examples.展开更多
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 countries.This article narrows the empirical ...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 countries.This article narrows the empirical literature gap.It draws from Babessi,a rural town in the Northwest Region of Cameroon.Babessi was hit by a severe flash flood in 2012.The 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 absent.Primary data were collected via snowball sampling.Multinomial 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 providers.We found that risk perception is linked to location,which in turn is associated with religious affiliation.Christians 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 floods.Finally,public disaster relief appears to have built up trust and subsequently reduced risk perception,even if some victims remained skeptical of state disaster relief.This indicates strong potential benefits of public transfers for flood risk management in developing countries.展开更多
Predictive analytics is crucial in precision medicine for personalized patient care.To aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classi...Predictive analytics is crucial in precision medicine for personalized patient care.To aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease types.To 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 variables.In 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 types.Dimensionality reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical variables.The 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 significant.Furthermore,it was evident that both clinical and genetic variables were important in predicting the diseased tissue types.The top three clinical predictors for predicting diseased tissue types were identified as morphology,gender,and age of diagnosis.Additionally,this study explored the possibility of using the latent representations of the clusters of landmark and non-landmark genes as predictors for an MLR classifier.The classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical variables.This study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine,enhancing diagnostic accuracy.展开更多
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01137).
文摘Sonic Hedgehog Medulloblastoma(SHH-MB)is one of the four primary molecular subgroups of Medulloblastoma.It is estimated to be responsible for nearly one-third of allMB cases.Using transcriptomic and DNA methylation profiling techniques,new developments in this field determined four molecular subtypes for SHH-MB.SHH-MB subtypes show distinct DNAmethylation patterns that allow their discrimination fromoverlapping subtypes and predict clinical outcomes.Class overlapping occurs when two or more classes share common features,making it difficult to distinguish them as separate.Using the DNA methylation dataset,a novel classification technique is presented to address the issue of overlapping SHH-MBsubtypes.Penalizedmultinomial regression(PMR),Tomek links(TL),and singular value decomposition(SVD)were all smoothly integrated into a single framework.SVD and group lasso improve computational efficiency,address the problem of high-dimensional datasets,and clarify class distinctions by removing redundant or irrelevant features that might lead to class overlap.As a method to eliminate the issues of decision boundary overlap and class imbalance in the classification task,TL enhances dataset balance and increases the clarity of decision boundaries through the elimination of overlapping samples.Using fivefold cross-validation,our proposed method(TL-SVDPMR)achieved a remarkable overall accuracy of almost 95%in the classification of SHH-MB molecular subtypes.The results demonstrate the strong performance of the proposed classification model among the various SHH-MB subtypes given a high average of the area under the curve(AUC)values.Additionally,the statistical significance test indicates that TL-SVDPMR is more accurate than both SVM and random forest algorithms in classifying the overlapping SHH-MB subtypes,highlighting its importance for precision medicine applications.Our findings emphasized the success of combining SVD,TL,and PMRtechniques to improve the classification performance for biomedical applications with many features and overlapping subtypes.
文摘To stay competitive, the mobile telecommunication companies spend millions of Ghana cedi each year on building long-term relationships with their customers. Marketing managers are constantly challenged with the problem of where to channel the limited resources in order to retain existing customers. This study approaches the customer retention problem in the mobile phone sector from a behavioural perspective, applying the Behavioural Perspective Model as the main analytical framework and further exploits some other factors that influence customer retention. The model includes a set of pre-behaviour and post-behaviour factors to study consumer choice, and explains its relevant drivers in a viable and comprehensive way, grounded in radical behaviourism. Data for the analysis were collected from tertiary students from Accra and Takoradi. Data collected were analysed using the multinomial regression technique. Analysis of the data revealed that the Behaviour setting factor is the only significant element in Behaviour Perspective Model. Further exploitation of behaviour situation revealed that the number of networks a customer uses, previous experience of a customer and customer’s intention are significant factors in determining customer retention in Ghana’s mobile telecommunication industry.
基金funded by the INTER program and cofunded by the Fond National de la Recherche,Luxembourg(FNR)and the Fund for Scientific Research-FNRS,Belgium(F.R.S-FNRS),T.0233.20-‘Sustainable Residential Densification’project(SusDens,2020–2024).
文摘The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.
文摘Rice straw,a by-product of rice cultivation,is commonly disposed of through open-field burning,which contributes to air pollution and environmental degradation.This study aims to identify the key factors influencing farmers’decisions on rice straw management and to develop policy recommendations that encourage the sustainable utilization of rice straw within the supply chain.A mixed-methods approach was adopted,combining qualitative interviews with nine key informants and a quantitative survey of 585 rice farmers across Thailand.Multinomial Logit Regression(MLR)was employed to analyze farmers’preferences among four management options:burning,composting,animal feeding,and selling.The results reveal that membership in farmer groups,ownership of livestock,access to baling machinery,knowledge,and skills related to straw utilization,ease of field access,availability of storage facilities,engagement in integrated farming,and year-round access to baling services significantly increased the likelihood of choosing sustainable alternatives over the burning straw.These findings underscore the importance of both capacity-building and infrastructure in enabling sustainable practices.Based on these insights,the study proposes a multi-level policy framework to enhance the value creation of rice straw.National policies should focus on expanding access to machinery and supporting innovation,while local governments should facilitate farmer training and improve straw logistics.Strengthening farmer organizations and market connections is also crucial for scaling adoption.Overall,structural integration and stakeholder coordination are key to reducing straw burning and promoting sustainable resource use in rice-producing regions.
基金Acknowledgements This research was financially supported by the National Basic Research of China （2010CB950900） and the National Natural Science Foundation of China （Grant Nos. 71225005 and 41071343）. Two anonymous reviewers are sincerely acknowledged for their valuable comments which have significantly improved the manuscript.
文摘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.
基金The National Key Research and Development Program of China(No.2019YFB1600200).
文摘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,China.Ten categories of traffic violations were determined from the raw data.Then,chi-square tests were used to analyze the relationship between traffic violations and the potential risk factors.Multinomial logistic regression analyses were conducted to further estimate the effects of different risk factors on the likelihood of the occurrence of traffic violations.By analyzing the results of chi-square tests via SPSS,the five factors above were all determined as significant factors associated with traffic violations.The results of the multinomial logistic regression revealed the significant effects of the five factors on the likelihood of the occurrence of corresponding traffic violations.The conclusions are of great significance for the development of effective traffic intervention measures to reduce traffic violations and the improvement of road traffic safety.
基金funded by the Spanish Ministry of Economy and Competitiveness,Grant No.DER2016-76053R.
文摘The subject of this study is the microcredit market in the USA,more specifically in Florida.The justification for choosing this specific state is the massive presence of the Hispanic population.This will facilitate a generalization of the obtained results to the microcredit market in Latin American countries.Thus,the objective of this study is to analyze the profile of microcredit holders and their companies from socioeconomic and financial points of view.As our data also consider the degree of repayment of the microloans included in the sample,the clients’profile is related to the punctuality or default of their corresponding loan repayments using the methodology of multi-nomial logit regression.The variables used in this study refer to personal information concerning borrowers(gender,age,education level,and marital status),the economic situation of their respective companies(closeness to the lender,number of workers,and revenues),and the characteristics of granted loans(principal,term,and purpose).However,the results of the regression show that only two variables are significant at the 5%significance level:the borrower’s age,which has a positive effect on repay-ment punctuality,and the loan term,which exhibits a negative effect.The findings of this study have clear implications,as they can help lenders design suitable microloans adjusted to customer profiles.Finally,future research should include other demograph-ics and characteristics of affected companies.
文摘A follow-up study with 7,826 representative newly married couples for fifteen months after their weddings in Shanghai Municipality showed that among the 3, 412 couples who actually adopted contraceptive method, rhythm was the main choice; the proportion for couples taking the contraceptive pill was much higher among sexually active couples before their weddings. The proportions of adopting rhythm or condom or the both, however, increased afterwards.About 86% of couples who had ever planned adopting the rhythm at registration actually used it. In fact, 16% of those who had ever planned to take pills eventually made this choice, because of their worry about any adverse side effects on mother's and fetus' health. Their knowledge about contraception,especially the pills, was incomprehensiue. APProximately 62% of condom users had not been given any instruction regarding its use when they got this contracoptive device one year later. Half of the pill and spermicide users learnt these respective methods from their friends or relatives. The proportion of delivering contraceptiues alter marriage by;F.P.P. was rather low. By fitting the multinomial logistic regression model, it is indicated that couple's evaluation on contraceptiue methods and contraceptiue goal were the main factors determining newlyweds' method of choice. Wife's knowledge on contraception and the accessibility of contraceptives and devices also influenced the method choice to some extent.
基金National Key Research and Development Program of China(No.2016YFF0103604)National Natural Science Foundations of China(Nos.61171165,11431015,61571230)+1 种基金National Scientific Equipment Developing Project of China(No.2012YQ050250)Natural Science Foundation of Jiangsu Province,China(No.BK20161500)
文摘A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.
文摘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 variety.Research 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.
文摘A field survey was carried out to model farmers’ vulnerability to climate variability and extreme events in selected agroecological zones in Kitui County. The indicator approach was used to calculate the overall household vulnerability index, where Principal Component Analysis (PCA) was used to allocate weights to indicators of exposure, sensitivity and adaptive capacity. Multinomial logistic regression was run in Stata to model the influence of socioeconomic characteristics on farmers’ vulnerability levels. The study established that different socioeconomic characteristics of households had a varying influence on the households’ vulnerability levels. Proximity to the Market and the arid agroecological zone significantly reduced the probability of a household belonging to the low and moderate vulnerability categories. On the other hand, the education level and the semi-humid zone significantly increased the odds of a household belonging to the low vulnerability category. Further, access to credit facilities and the semi-humid agroecological zone significantly increased the odds of a household belonging to the moderate vulnerability category. The study thus recommends that policy interventions should target specific socioeconomic characteristics that influence households’ vulnerability to climate variability and extreme events.
基金Wang Haiying’s research was partially supported by the National Science Foundation under Grant No.CCF 2105571.
文摘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 levels.The increasing volumes of data bring new challenges for parameter estimation in softmax regression,and the optimal subsampling method is an effective way to solve them.However,optimal subsampling with replacement requires to access all the sampling probabilities simultaneously to draw a subsample,and the resultant subsample could contain duplicate observations.In this paper,the authors consider Poisson subsampling for its higher estimation accuracy and applicability in the scenario that the data exceed the memory limit.The 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 criteria.The 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 phase.The authors demonstrate the performance of our optimal Poisson subsampling algorithm through numerical simulations and real data examples.
文摘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 countries.This article narrows the empirical literature gap.It draws from Babessi,a rural town in the Northwest Region of Cameroon.Babessi was hit by a severe flash flood in 2012.The 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 absent.Primary data were collected via snowball sampling.Multinomial 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 providers.We found that risk perception is linked to location,which in turn is associated with religious affiliation.Christians 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 floods.Finally,public disaster relief appears to have built up trust and subsequently reduced risk perception,even if some victims remained skeptical of state disaster relief.This indicates strong potential benefits of public transfers for flood risk management in developing countries.
文摘Predictive analytics is crucial in precision medicine for personalized patient care.To aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease types.To 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 variables.In 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 types.Dimensionality reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical variables.The 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 significant.Furthermore,it was evident that both clinical and genetic variables were important in predicting the diseased tissue types.The top three clinical predictors for predicting diseased tissue types were identified as morphology,gender,and age of diagnosis.Additionally,this study explored the possibility of using the latent representations of the clusters of landmark and non-landmark genes as predictors for an MLR classifier.The classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical variables.This study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine,enhancing diagnostic accuracy.
