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.

Multinomial Logistic模型在区域高等教育资源结构优化中的应用

在区域确定的前提下,将各高等教育资源数据进行整理,建立Multinomial Logistic模型,分析各结构的相对发生比率以及各结构的最佳分布。分析该回归分析的多维发生比率,由此确定各种离散等级状态之间的调整方向及调整程度。对黑龙江省的各高等教育区域进行实证分析,结果表明,黑龙江省高等教育资源结构以教学型、教学研究型、研究教学型、研究型的比例来判断,高等教育效用有待于进一步挖掘。多维发生比率以及自变量对多维发生比率的变化影响这两个参数对区域高等教育资源结构优化起着关键作用。

基于Multinomial Logit模型的图书促销效果研究

图书作为一种传统的信息传递方式,在现如今的网络时代面临着前所未有的压力和考验。各大出版社在提高整套图书系统质量的同时,也运用各种促销手段来提升销量。选择何种促销策略,达到最大的投资回报是出版商非常关注的一个问题。本文基于Multinomial Logit模型,评估了某出版社在当当和亚马逊两个网站上的销售促销行为对销量提升的作用和影响,为出版商未来的销售促销活动提出科学合理的建议。

消费者选择的Multinomial Logit模型及其应用——以我国轿车市场为例

本文运用Multinomial logit(MNL)模型研究消费者在选择产品过程中其个人特征对消费者选择某个特定因素作为首选因素的影响以及相应的边际影响效果.通过对我国轿车消费者购买选择的实证研究结果表明MNL模型能够很好的反映轿车消费者个人特征和消费者购车时选择特定的因素作为首选因素的关系;实证结果发现消费者选择的首选因素会随着消费者个人特征的不同而发生变化.

基于Multinomial Logit模型的美国北卡罗莱纳州慢行交通事故严重程度分析

为分析慢行(自行车与行人)交通事故严重程度的显著性影响因素,将交通事故分为仅财产损失事故、潜在伤害事故、非伤残事故、伤残事故、死亡事故5类,从人、车、路、环境4个层次选出11个自变量,以交通事故严重程度为因变量,基于Multinomial Logit模型计算每个变量对慢行交通事故严重程度的边际效应。结果表明,驾驶员性别、驾驶员饮酒状态、行人年龄、道路特征、路面状态、一天内发生时间、光线条件、年平均日交通量、地形等9个变量均与道路交通事故严重程度显著相关。

SOME ASYMPTOTIC INFERENCE IN MULTINOMIAL NONLINEAR MODELS (A GEOMERIC APPROACH)

A geometric framework is proposed for multinomial nonlinear modelsbased on a modified version of the geometric structure presented by Bates & Watts[4]. We use this geometric framework to study some asymptotic inference in terms ofcurvatures for multinomial nonlinear models. Our previous results [15] for ordinarynonlinear regression models are extended to multinomial nonlinear models.

DISPERSION COMPARISONS OF TWO PROBABILITY VECTORS UNDER MULTINOMIAL SAMPLING

We consider testing hypotheses concerning comparing dispersions between two parameter vectors of multinomial distributions in both one-sample and two-sample cases. The comparison criterion is the concept of Schur majorization. A new dispersion index is proposed for testing the hypotheses. The corresponding test for the one-sample problem is an exact test. For the two-sample problem, the bootstrap is used to approximate the null distribution of the test statistic and the p-value. We prove that the bootstrap test is asymptotically correct and consistent. Simulation studies for the bootstrap test are reported and a real life example is presented.

TRUST MODEL BASED ON THE MULTINOMIAL SUBJECTIVE LOGIC AND RISK MECHANISM FOR P2P NETWORK OF FILE SHARING

In order to deal with the problems in P2P systems of file sharing such as unreliability of the service,security risk and attacks caused by malicious peers,a novel Trust Model based on Multinomial subjective logic and Risk mechanism(MR-TM) is proposed.According to the multinomial subjective logic theory,the model introduces the risk mechanism.It assesses and quantifies the peers' risk,through computing the resource value,vulnerability,threat level,and finally gets the trust value by the risk value and the reputation value.The introduction of the risk value can reflect the recent behaviors of the peers better and make the system more sensitive to malicious acts.Finally,the effectiveness and feasibility of the model is illustrated by the simulation experiment designed with Peersim.

Ethnobotanical profiling of the medicinal flora of Kotli,Azad Jammu and Kashmir,Pakistan:Empirical reflections on multinomial logit specifications

Objective:To report the indigenous people's uses of plants from a multidimensional perspective in a remote area where strong ethnobotanical cultural practices prevail. Methods:An ethnobotanical survey was conducted in the field during 2014-2016. Ethnobotanical data were gathered from 182 informants through oral interviews and semi-structured questionnaires. The distribution of plants was explored using both descriptive and graphical methods. Further,a Multinomial Logit Specification was applied to find out the probability of the occurrence of diverse utilization of plants in multipurpose domains. Results:The study identified 202 plant species distributed among 71 families and 156 genera. Ethnobotanical data indicate that there are more medicinal(36.96%) uses of plants as compared to all other use categories. The output from the Multinomial Logit Specifications(MLS) model reveals that perennial and non-woody plants are exploited more for medicinal and food uses than annual and woody plants. In the context of ethnomedicinal uses,aerial plant parts particularly leaves are more extensively used for the preparation of herbal recipes as compared to underground parts. Conclusions:The results of the study emphasize the need to create awareness among the local communities about the conservation status of plant species in order to maintain a sustainable resource of plant-derived materials into the future. The novel econometric approach employed in this study adds a new insightful methodology to the existing body of literature in the field of ethnobotany. We strongly recommend conservation measures,alongside phytochemical and pharmacological studies on the useful plant species identified in this study in order to ensure their sustainable and effective utilization.

A Novel Negative Multinomial Distribution Based Energy-Efficient Reputation Modeling for Wireless Sensor Networks(WSNs)

In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.

A Trust Model Based on the Multinomial Subjective Logic for P2P Network

In order to deal with the problems in P2P systems such as unreliability of the Service, security risk and attacks caused by malicious peers, a novel trust model MSL-TM based on the Multinomial Subjective Logic is proposed. The model uses multinomial ratings and Dirichlet distribution to compute the expectation of the subjective opinion and accordingly draws the peer’s reputation value and risk value, and finally gets the trust value. The decay of time, rating credibility and the risk value are introduced to reflect the recent behaviors of the peers and make the system more sensitive to malicious acts. Finally, the effectiveness and feasibility of the model is illustrated by the simulation experiment designed with peersim.

Incorporating the Multinomial Logistic Regression in Vehicle Crash Severity Modeling: A Detailed Overview

Multinomial logistic regression (MNL) is an attractive statistical approach in modeling the vehicle crash severity as it does not require the assumption of normality, linearity, or homoscedasticity compared to other approaches, such as the discriminant analysis which requires these assumptions to be met. Moreover, it produces sound estimates by changing the probability range between 0.0 and 1.0 to log odds ranging from negative infinity to positive infinity, as it applies transformation of the dependent variable to a continuous variable. The estimates are asymptotically consistent with the requirements of the nonlinear regression process. The results of MNL can be interpreted by both the regression coefficient estimates and/or the odd ratios (the exponentiated coefficients) as well. In addition, the MNL can be used to improve the fitted model by comparing the full model that includes all predictors to a chosen restricted model by excluding the non-significant predictors. As such, this paper presents a detailed step by step overview of incorporating the MNL in crash severity modeling, using vehicle crash data of the Interstate I70 in the State of Missouri, USA for the years (2013-2015).

Customer Retention: Behaviour Perspective Model of Ghanaian Telecommunication Industry Using Multinomial Regression Analysis

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.

Multinomial Logistic Regression Model for Predicting Driver＇s Drowsiness Using Only Behavioral Measures

The aim of this study was to explore the effectiveness of behavioral evaluation measures for predicting drivers＇ subjective drowsiness. Behavioral measures included neck bending angle, back pressure, foot pressure, COP （center of pressure） movement on sitting surface and tracking error in driving simulator task. Drowsy states were predicted by means of the multinomial logistic regression model where behavioral measures and subjective evaluation of drowsiness corresponded to independent variables and a dependent variable, respectively. First, we compared the effectiveness of two methods （correlation coefficient-based method and odds ratio-based method） for determining the order of entering behavioral measures into the prediction model. It was found that the prediction accuracy did not differ between both methods. Second, the prediction accuracy was compared among the numbers of behavioral measures. The prediction accuracy did not differ among four, five and six behavioral measures and it was concluded that entering at least four behavioral measures into the prediction model is enough to achieve higher prediction accuracy. Third, the prediction accuracy was compared between the strongly drowsy and the weakly drowsy groups. The prediction accuracy differed between the two groups and the proposed method was effective under the condition where drowsiness was induced to a larger extent.

个人素质、家庭背景、社区状况与青年农民就业选择——基于Multinomial Logit模型的实证分析

青年农民就业选择对城乡经济的即期和中长期发展具有双重的决定性影响。本文利用经济较发达、中等发达和欠发达地区3组样本,分析在总体和不同组内青年农民就业选择的基本特征,并构建计量模型考察个人素质、家庭背景、社区状况等对其就业选择的影响。分析结果表明,青年农民就业选择的非农倾向突出、经济较发达组>总体>经济欠发达组>经济中等组,就业层次均较低且结构不合理、普通务工者最多;青年农民就业选择受个人素质中的文化程度和非农职业经历、家庭背景中的父母文化程度和非农职业经历及社区状况中的地形和经济发展水平的综合影响,但不同组内、不同类型就业选择所受影响的程度各异。

农村独生子女生育选择影响因素及测度研究--基于Multinomial Logistic模型实证分析

本文基于北京市昌平区的农村独生子女调查数据,利用效用最大化离散选择模型,对农村独生子女生育选择模式及影响因素进行了分析。文章不仅考察了个体因素、经济与社会政策因素对生育选择模式的影响,更重要的是从生育选择的预测概率、离散变化以及Odds Ratios等多角度对影响因素的重要程度、影响大小等进行了定量测度分析。并从中推断起决定作用的因素发生变化时,可能导致的生育意愿、计划与行为的改变,探讨独生子女生育选择对中国未来人口变动趋势产生的影响和政策意义。

考虑顾客选择行为的BOPS自提门店个性化推荐

针对全渠道零售中的BOPS(在线购买门店提货)模式,给出了考虑顾客选择行为的自提门店推荐方法。此方法构建了结合商圈、距离以及预计送达时长的不同收货方式下顾客的效用函数,通过MNL选择模型来刻画顾客的选择行为,建立考虑顾客选择行为的以零售商利润最大化为目标的混合整数规划模型。其次,将模型转换成易于求解器求解的混合整数二阶锥规划模型。数值实验表明本文提出的自提门店的推荐方法可提升零售商的利润。最后,探讨了预计送达时长、顾客的距离敏感度与收益的关系。数值实验结果表明,零售商的总利润与预计送达时长呈正相关关系,零售商的总利润与顾客对距离的敏感度呈负相关关系。

Navigating Market Choices: Understanding Carrot Market Outlet Selection among Smallholder Farmers in Kenya

This research delves into the hurdles and strategies aimed at augmenting the market involvement of smallholder carrot farmers in Nakuru County, Kenya. Employing a Multinomial Logit (MNL) model, it scrutinizes the factors influencing the selection of marketing outlets among carrot farmers. The findings unveil that a significant majority (81%) of surveyed farmers actively participate in diverse market outlets, encompassing the farm gate, cleaning point, local market, external market, and export market. Notably, pivotal buyers include aggregators, brokers, wholesalers, retailers, and consumers, with transactions predominantly occurring at the farm level. Additionally, the analysis discerns substantial influences of socio-economic characteristics, experiential factors, and geographical proximity on farmers’ choices of market outlets. Specifically, gender, age, land size, farming experience, and distance to markets emerge as critical determinants. Moreover, the study delves into the examination of market margins along the carrot value chain, shedding light on the potential profitability of carrot farming in the region. Remarkably, higher average gross margins are identified in export and external markets, signaling lucrative prospects for farmers targeting these segments. However, disparities in profit distribution between farmers and traders underscore the necessity for interventions to ensure equitable value distribution throughout the value chain. These findings underscore the imperative for tailored interventions to tackle challenges and foster inclusive agricultural development. Strategies such as farmer organizations, contracting, and vertical integration are advocated to enhance market access and profitability for smallholder carrot farmers. Thus, this study enriches our comprehension of the dynamics within carrot value chains and provides valuable insights for policymakers and development practitioners aiming to uplift rural livelihoods and bolster food security.

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

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.

Testing for the maximum cell probabilities in multinomial distributions

This paper investigates one-sided hypotheses testing for p, the largest cell probability of multinomial distribution. A small sample test of Ethier (1982) is extended to the general cases. Based on an estimator of p, a kind of large sample tests is proposed. The asymptotic power of the above tests under local alternatives is derived. An example is presented at the end of this paper.