Factors affecting farmers'choice to adopt risk management strategies:The application of multivariate and multinomial probit models

This study investigates the factors that impact farmers'adoption of risk management strategies(RMS)in Pakistan during times of uncertainty.The study examines farmers'adoption of RMS using both multinomial probit(MNP)and multivariate probit(MVP).Data were collected from 382 farmers sampled from four districts in KhyberPakhtunkhwa(KP)province of Pakistan via a multistage sampling technique.This study utilizes the MNP model,considering the assumption of Independence of Irrelevant Alternatives(IIA)and incorporating correlated error terms.The objective is to understand farmers'behavior in risky situations and determine if there is heterogeneity.Results are compared with the MVP model to assess robustness and gain deeper understanding of farmers'decisionmaking processes.The research findings reveal that our results are robust,and farmers behave homogeneously in various RMS scenarios.Farmers adopt RMS individually or in combination to mitigate the adverse effects of natural calamities on their livelihood.The risk-averse farmers,who perceive weather-related risks as a threat,access credits and information,and have farms close to a river are more likely to adopt RMS,irrespective of the format of the strategies available.Moreover,the predicted probabilities and correlation of the RMS and RM categories have strengthened our model estimation.These findings provide insights into the behavior of farmers in adopting RMS which are helpful for policymakers and stakeholders in developing strategies to mitigate the impacts of natural calamities on farmers.

基于Ordered Probit模型的人车冲突安全影响因素研究

为探究和定量分析人车冲突严重程度的影响因素,通过采集3个信号交叉口的人车冲突视频数据,利用T-Analyst标定人车轨迹并计算冲突指标(PET),采用85%位累计频率曲线法划分人车冲突等级;从人、车、路及环境特征中选取10个因素作为变量,构建Ordered Probit模型,以确立人车冲突严重程度的显著影响因素,并通过边际效应定量分析不同显著因素的影响程度。研究结果表明:行人闯红灯情况、年龄、行人交通量、人行道占用情况、人行道起点终点、车辆速度变化及车流量是人车冲突严重程度的显著因素,相较于各自参考量,行人闯红灯、车辆加速通过冲突点、人行道起点及老年人造成严重冲突的概率分别增加8.2%,5.9%,5.1%,4.4%;相较于低流量的交通流,较高流量的车流和行人交通流使得严重冲突的概率分别增加3.7%,2.5%,但人行道占用使得严重冲突的概率下降6.8%。研究结果可为信号交叉口行人过街交通安全设施的设计和实施提供理论依据。

数字乡村参与促进了农村居民创业选择吗?——基于内生转换Probit模型的实证分析

创业是解决农村居民就业、实现共同富裕的有力举措。文章基于数字乡村参与微观视角,运用SOR模型构建“情境刺激—内部动机—行为反应”过程机制,理论分析数字乡村参与与农村居民创业选择的关系,并进一步通过CFPS微观调查数据、内生转换Probit模型,实证检验经济、治理、生活三种不同应用场景下数字乡村参与对农村居民创业选择的影响。研究发现,农村居民数字乡村及各维度参与均显著促进农村居民创业选择。异质性分析表明,数字乡村参与对高教育、高收入水平的农村群体的创业效应更大,且在粮食主产区和东部、中部地区更为突出。因此,要积极鼓励农村居民参与数字乡村实践,同时也要重视数字乡村各领域发展的包容性和公平性。

ENHANCING GROUND RESOLUTION OF TM6 BASED ON MULTI-VARIATE REGRESSION MODEL AND SEMI-VARIOGRAM FUNCTION

It is well known that Landsat TM images are the most widely used remote sensing data in various fields.Usually,it has 7 different electromagnetic spectrum bands,among which the sixth one has much lower ground resolution compared with the other six bands.Nevertheless,it is useful in the study of rock spectrum reflection,geothermal resources exploration,etc.To improve the ground resolution of TM6 to the level as that of the other six bands is a problem .This paper presents an algorithm based on the combination of multivariate regression model with semivariogram function which can improve the ground resolution of TM6 by "fusing" the data of other six bands.It includes the following main steps: （1） testing the correlation between TM6 and one of TM15,7.If the correlation coefficient between TM6 and another one is greater than a given threshold value,then select the band to the regression analysis as an argument.（2） calculating the size of the template window within which some parameters needed by the regression model will be calculated; （3） replacing the original pixel values of TM6 by those obtained by regression analysis; （4） using image entropy as a measurement to evaluate the quality of the fused image of TM6.The basic mechanism of the algorithm is discussed and the V C ++ program for implementing this algorithm is also presented.A simple application example is given in the last part of this paper,showing the effectiveness of the algorithm.

基于改进萤火虫算法的Probit模型在数字金融风险预测中的性能分析

为应对数字金融的风险预警,需对其背后的规律进行深度挖掘,从而实现对其风险精准、高效地预测.在数据不平衡的情况下,采用常规单一类别模型进行数据分析时存在性能不稳定的问题,研究面向数字金融欺诈的浅层特征选择方法.为了解决数字化金融风险数据中存在的冗余和无关特征,采用了一种基于二元萤火虫算法的Probit模型,以更有效的方式来筛选数据中的初始特征.实验结果显示,融合烟花协同进化的二元萤火虫算法收敛速度和精度均显著优于其他算法.在初步特征子集中最佳分类精率为93.5358%,平均分类精度为86.2543%,运行时间为5.4883 s;运行时间得到了优化,分类精度同时也得到了一定的提高.研究提出的改进算法模型可以用于指导金融风险管理实践,帮助金融机构更好地识别和预防金融欺诈风险.

价值认知、环境规制对蕉农绿色防控行为的影响——基于多变量Probit模型的证据

推动农户采纳农业绿色防控技术是实现农业可持续发展的关键,深入剖析农户采纳绿色防控技术的关键因素有助于更好地推广绿色防控技术。本文利用海南省456户香蕉种植户调研数据,构建多变量Probit模型,探讨价值认知和环境规制对农户绿色防控行为的影响及代际差异。结果表明:价值认知和环境规制可以显著促进农户采纳绿色防控技术,且两者对农户绿色防控行为的影响存在交互效应,其中,价值认知和引导型环境规制、约束型环境规制的交互项对农户绿色防控行为具有显著的正向影响,价值认知和激励型环境规制的交互项对农户施用生物农药行为具有显著促进作用;价值认知和环境规制对不同代际农户绿色防控行为的影响具有显著差异,新生代农户的绿色防控行为主要受生态价值认知、社会价值认知和环境规制的影响,而老一代农户的绿色防控行为主要受价值认知和引导型环境规制的影响。研究丰富和完善了农户绿色防控行为的影响路径,对缓解农业病虫害频繁发生、践行农业可持续发展战略具有重要的启示作用。

基于空间广义有序Probit模型的高速公路事故严重程度分析

为深入了解影响高速公路交通事故严重程度的显著因素,以广深沿江高速东莞段2014—2019年事故数据为研究对象,将事故严重程度分为3类(即无伤亡事故、轻伤事故、重伤亡事故),并以事故严重程度为因变量、13个潜在影响因素为自变量,通过条件自回归先验解析相邻事故间的空间相关性,建立不同关联距离阈值的空间广义有序Probit模型。结果表明:事故数据间存在显著的空间关联;空间广义有序Probit模型优于广义有序Probit模型和多项Logit模型,基于250 m关联距离阈值的空间广义有序Probit模型表现最优。该模型参数估计显示:车辆类型和归属地、事故发生时间、事故地点曲率、桥梁路段和事故类型对高速公路事故严重程度均有显著影响。边际效应结果表明:相对于小汽车间的交通事故,涉及客车、货车和其他类型车辆的交通事故导致人员重伤亡的概率分别增加3.27%、1.53%和4.11%;外省车使得重伤亡事故的发生概率增加1.02%;相对于周末、春季和桥梁路段,工作日、夏季和非桥梁路段发生的交通事故导致人员重伤亡的概率分别提高0.87%、2.38%和0.08%;单车事故导致重伤亡的概率比多车事故低1.64%;事故地点曲率每增加1 km-1,重伤亡事故的概率将降低1.54%。

基于有序Probit模型的铁路险兆事件影响因素识别研究

为识别铁路险兆事件的影响因素,以宜春车务段2017年9月—2017年10月共1870条数据为样本,以铁路险兆事件等级为因变量,事件原因、环境特征和事件特征为自变量建立有序Probit模型,探究人、设备、环境和管理因素对铁路险兆事件严重影响程度。研究结果表明:设备设施未按规定防护是影响严重性险兆事件发生的主要因素;与其他人为因素相比,未执行相关作业规定对增加险兆事件严重程度有显著作用。研究结果可为铁路管理部门实现事故预控提供依据。

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

Complementary parametric probit regression and nonparametric classi?cation tree modeling approaches to analyze factors affecting severity of work zone weather-related crashes

Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weatherrelated crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions.

基于有序PROBIT模型的历史文化街区风貌满意度研究

以大召历史文化街区为研究对象,在构建风貌要素指标体系的基础上,采用民众风貌感知数据,运用有序Probit模型、边际效用分析与I-S评分法,研究风貌要素满意度对总体风貌满意度的影响机理,以此对风貌改造工作提出精准建议。结果表明:街道服务设施特点、灯光照明、景观特点与历史文化之“风”四个风貌要素满意度对总体风貌满意度具有显著影响;相比于男性,女性对街区风貌的要求更高,更不容易被满足;风貌要素优先提升权从高到低依次为街道服务设施特点、灯光照明、景观特点与历史文化之“风”。

Using MCMC Probit Model to Value Coastal Beach Quality Improvement

Dichotomous choice elicitation technique of contingent valuation method is broadly used in the research fields of environmental resource and recreational activity management. The binary choice type of questions are generally analyzed by using Logit or Probit probability distribution models in which a common analysis procedure is to apply MLE for estimating variable parameters before calculating the respondents’ willingness to pay. In this paper, a MCMC Gibbs sampling Probit model is adopted to maintain the three advantages it has in dealing with heteroscedasticity, high dimension numerical integral and sample size restriction problems. The results revealed that the MCMC model and MLE Probit model are strikingly consistent, which suggests that the former is much simple and reliable estimation method. At the same time, the empirically based existence value estimation of coastal beach quality improvement in Dalian, China is RMB?168 per person.

Probit Normal Correlated Topic Model

The logistic normal distribution has recently been adapted via the transformation of multivariate Gaussian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. Our use of the probit model in the context of topic discovery is novel, as many authors have so far concentrated solely of the logistic model partly due to the formidable inefficiency of the multinomial probit model even in the case of very small topical spaces. We herein circumvent the inefficiency of multinomial probit estimation by using an adaptation of the diagonal orthant multinomial probit in the topic models context, resulting in the ability of our topic modeling scheme to handle corpuses with a large number of latent topics. An additional and very important benefit of our method lies in the fact that unlike with the logistic normal model whose non-conjugacy leads to the need for sophisticated sampling schemes, our approach exploits the natural conjugacy inherent in the auxiliary formulation of the probit model to achieve greater simplicity. The application of our proposed scheme to a well-known Associated Press corpus not only helps discover a large number of meaningful topics but also reveals the capturing of compellingly intuitive correlations among certain topics. Besides, our proposed approach lends itself to even further scalability thanks to various existing high performance algorithms and architectures capable of handling millions of documents.

金融能力、正规信贷约束与农户创业行为

基于金融能力和正规信贷约束视角,利用2017年和2019年CHFS微观数据,通过构建Probit模型实证分析金融能力对农户创业行为的影响,探讨正规信贷约束在金融能力和农户创业行为关系中发挥的中介效应。结果表明:金融能力对农户创业行为具有显著正向影响,且在考虑内生性的情况下,该结论仍然成立;中介机制检验表明,金融能力能够通过缓解正规信贷约束来促进农户创业行为;异质性分析表明,中、西部地区和三线及以下城市的农户金融能力对其创业行为的影响更为显著。据此,建议建立完善的金融能力培养体系,加大对欠发达地区的政策扶持,且提高正规信贷支持力度。

基于随机参数Ordered Probit模型的山区公路摩托车事故严重程度研究

为准确识别山区双车道公路摩托车事故严重程度影响因素及其异质性效应,降低摩托车事故发生率及其严重度,综合考虑事故严重度有序性和影响因素异质性,以2012—2017年云南省楚雄州Y公路摩托车交通事故为研究对象,从驾驶员、车辆、道路、环境和事故特性五要素中选取16个潜在影响因素作为自变量,以摩托车事故严重程度作为因变量,分别构建了山区双车道公路摩托车事故严重程度固定参数有序probit模型(OP模型)和随机参数有序probit模型(RP-OP模型),并结合边际效应分析了各影响因素对摩托车事故严重程度的作用强度。结果表明:考虑个体异质性的RP-OP模型拟合优度高于OP模型,能准确刻画数据中未观测到的异质性和事故严重程度影响因素之间的交互作用,更适用于事故严重度影响机理的精细化研究;OP和RP-OP模型均识别出摩托车驾驶员性别、操作行为、是否有责、事故发生时间、光线条件、事故发生地点、路表情况、涉事车型显著影响摩托车事故严重程度的8个变量,其中女性摩托车驾驶员、驾驶员超车不当、涉及货车3个变量是分别服从(0.721,0.987^(2)),(0.432,0.344^(2)),(0.723,0.562^(2))正态分布的随机参数,对摩托车事故严重度具有显著的异质性影响效应。研究成果可为山区双车道公路摩托车事故预防提供决策参考,并针对性制定安全改善措施。

乡村旅游提高农村居民幸福感了吗?——基于海南省博纵村微观数据

随着我国进入新发展阶段,尤其在扎实推进共同富裕重要时期,如何提高人民幸福感逐渐成为社会各界日益关注的重要议题。在乡村旅游深刻影响农村社区发展的背景下,以乡村旅游示范村海南省博纵村为研究对象,利用有序Probit模型从微观层面检验乡村旅游对村民幸福感的影响与机理。研究发现,乡村旅游能显著提高农村居民幸福感。相较于未参与乡村旅游的村民,参与到乡村旅游的村民幸福感水平高0.167个单位;进一步分析发现,利益感知、相对剥夺感和旅游资源产权在乡村旅游和农村居民幸福感中发挥中介作用。其中利益感知中介作用最为显著,对幸福感提高具有正向效应;旅游资源产权模糊及相对剥夺感对幸福感的提高具有抑制作用。因此,在乡村旅游发展过程中,应完善乡村旅游利益分配机制,整合乡村旅游资源以及引导村民内归因。

基层政府治理水平对农民奋斗价值观的影响研究

赓续艰苦奋斗精神,激发农民奋斗意志,是当前基层治理中农民思想建设的重要内容。基于2018年与2020年中国家庭追踪调查数据,利用有序Probit模型,实证分析基层政府治理水平对农民奋斗价值观的影响及作用机制。研究发现:第一,基层政府治理水平与农民奋斗价值观呈正相关,即基层政府治理水平越高,农民越倾向于认为“努力工作能有回报”,越发增强“对未来的信心”;第二,基层政府治理水平对农民奋斗价值观的影响,主要通过提高该群体的生活满意度和工作满意度来实现;第三,高水平的基层政府治理正向促进了建档立卡农民和非建档立卡农民奋斗价值观的塑造,且这种促进作用在非建档立卡农民中更显著。

民营粮食收购企业供应链融资模式选择的影响因素研究

论文针对东北三省中小民营粮食收购企业存在融资难的问题,选取东北三省中小民营粮食收购企业收储供应链为研究对象,首先,通过问卷调查得出:一是这类企业选择供应链融资模式频率由小到大依次为“应收账款类融资模式”“信用类融资模式”“存货类融资模式”;二是在下游购销企业主导的粮食收储供应链金融中,以其自身资质向中小民营粮食收购企业提供担保的意愿不高;三是中小民营粮食收购企业认为政商关系总体在合格以上。其次,运用多变量Probit模型,得出供应链融资模式选择影响机理:贷款期限对信用类融资模式选择有显著的正向影响,政府对这类企业的服务水平正向影响着存货类融资模式选择。最后,得出政策启示:政府应提高服务水平精准到选择存货类融资模式的企业,通过优质中小企业梯度培育政策鼓励中小民营粮食收购企业兼并,鼓励银行扩大资产负债率的融资条件到8%—20%。

基于Probit的犯罪嫌疑人判定方法研究

为了在涉案人群中实现计算机识别犯罪嫌疑人,协助办案并提高办案效率,提出了一种基于Probit模型的犯罪嫌疑人判定技术.采用聚类的分离算法、关联算法以及Probit模型的显著性水平参数发现重要属性,通过对重要属性提取后的数据进行训练得到犯罪风险判定模型.实验结果表明,该方法对嫌疑人判定的平均准确率达到90.5%,平均查全率达到92.7%,判定效果较好.

城市特征对城市游客满意度的影响--基于Probit模型的定量分析

游客满意度是衡量城市旅游发展的重要指标之一,掌握和控制游客满意度的影响因素对城市旅游发展和管理越来越关键。国内外研究综述表明,城市旅游的游客满意度影响因素及其关系的定量研究尚不多见,尤其对城市特征因素的研究。城市特征是城市旅游的必要组成,作为客观因素对游客满意度的影响不言而喻。文章运用文献分析法、问卷调查法和二元Probit离散选择模型计量法,以长沙市477名游客的现场调查数据为基础,对城市特征、个人特征和旅游动机三大变量下10个影响因素与游客满意度之间的假设关系进行检验。研究结果显示,修正后的游客满意度影响因素Probit模型具有统计学意义,6个研究假设得到验证;研究结论表明,城市社会发展水平、城市绿化水平、环境保护水平、旅游资源丰富程度、游客月收入、游客是否主动到访等因素对城市旅游的游客满意度产生显著的正面影响,其中,游客主动到访因素对游客满意度的影响作用最大,城市社会发展水平影响作用最小。研究结论一定程度上有利于促使城市管理者更多关注城市发展与旅游质量,最后,从满意度改善系统、城市要素建设、城市旅游资源开发和游客营销引导等方面提出政策建议。