ERN: Model Construction & Estimation (Topic)最新文献

Nonparametric Tests of Conditional Independence for Time Series 时间序列条件独立性的非参数检验

ERN: Model Construction & Estimation (Topic) Pub Date : 2021-10-10 DOI: 10.2139/ssrn.3939952

Xiaojun Song, Haoyu Wei

引用次数: 0

Estimating Demand with Multi-Homing in Two-Sided Markets 双边市场中多重归巢的需求估计

ERN: Model Construction & Estimation (Topic) Pub Date : 2021-08-01 DOI: 10.2139/ssrn.3905859

Pauline Affeldt, Elena Argentesi, L. Filistrucchi

引用次数: 1

Does Court Type, Size and Employee Satisfaction Affect Court Speed?. Hierarchical Linear Modelling With Evidence from Kenya 法院类型、规模和员工满意度是否影响庭审速度?基于肯尼亚证据的层次线性模型

ERN: Model Construction & Estimation (Topic) Pub Date : 2021-06-01 DOI: 10.7176/jlpg/110-02

Moses Marang’a

{"title":"Does Court Type, Size and Employee Satisfaction Affect Court Speed?. Hierarchical Linear Modelling With Evidence from Kenya","authors":"Moses Marang’a","doi":"10.7176/jlpg/110-02","DOIUrl":"https://doi.org/10.7176/jlpg/110-02","url":null,"abstract":"In most judicial institutions, well-functioning courts are usually expected to process a large volume of work within demanding timelines. For courts to have played their role of enhancing access to justice, the yardstick of success is often viewed through the lens of the speed attained in rendering justice. In Kenya, despite the desirable timeline for finalizing most of the cases being ‘within 360 days’ from the date of case filing in courts, by the end of June 2020, 58 per cent of the unresolved cases had surpassed this timeline and subsequently classified as backlog. In the period 2018/19, the percentage of civil cases that were resolved within the set timeline by High Court and Magistrate Court, the two largest court types by volume of work, was 37 and 42 per cent respectively. Over the same period, the percentage of criminal cases that were resolved within the set timeline was 42 and 84 per cent for the two court types respectively. Evidently therefore, the Kenyan courts had not managed to resolve cases within the desirable timeline. To unearth the reasons that could be occasioning the delay, this study investigated the factors that were potentially affecting court speed. Specifically, the study set out to determine the variation in court speed attributable to court type, and further analyze the effect of court size and employee satisfaction on court speed. This was achieved through the use of Hierarchical Linear Modelling, cross sectional data for the period 2018/19 and estimation using Restricted Maximum Likelihood technique. The results revealed the existence of relatively high variation in court speed that is attributable to court type, and that the smaller the court size, the higher the court speed. Further, high level of employee satisfaction was found to increase timely resolution of cases. Consequently, diverse strategies and policy actions for enhancing court speed have been suggested.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126308064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of Estimation and Forecasting Method in Intelligent Decision Support Systems 智能决策支持系统中估计与预测方法的发展

ERN: Model Construction & Estimation (Topic) Pub Date : 2021-04-30 DOI: 10.15587/1729-4061.2021.229160

Іgor Romanenko, A. Golovanov, V. Khoma, A. Shyshatskyi, Y. Demchenko, L. Shabanova-Kushnarenko, Tetiana Ivakhnenko, O. Prokopenko, Oleh Havaliukh, Dmitrо Stupak

{"title":"Development of Estimation and Forecasting Method in Intelligent Decision Support Systems","authors":"Іgor Romanenko, A. Golovanov, V. Khoma, A. Shyshatskyi, Y. Demchenko, L. Shabanova-Kushnarenko, Tetiana Ivakhnenko, O. Prokopenko, Oleh Havaliukh, Dmitrо Stupak","doi":"10.15587/1729-4061.2021.229160","DOIUrl":"https://doi.org/10.15587/1729-4061.2021.229160","url":null,"abstract":"The method of estimation and forecasting in intelligent decision support systems is developed. The essence of the proposed method is the ability to analyze the current state of the object under analysis and the possibility of short-term forecasting of the object state. The possibility of objective and complete analysis is achieved through the use of improved fuzzy temporal models of the object state, an improved procedure for forecasting the object state and an improved procedure for training evolving artificial neural networks. The concepts of a fuzzy cognitive model, in contrast to the known fuzzy cognitive models, are connected by subsets of fuzzy influence degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. This method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of this method is the ability to take into account the type of a priori uncertainty about the state of the analyzed object (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The ability to clarify information about the state of the monitored object is achieved through the use of an advanced training procedure. It consists in training the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The object state forecasting procedure allows conducting multidimensional analysis, consideration and indirect influence of all components of a multidimensional time series with different time shifts relative to each other under uncertainty.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128023623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Estimating Financial Networks by Realized Interdependencies: A Restricted Autoregressive Approach 利用已实现的相互依赖性估计金融网络:一种限制性自回归方法

ERN: Model Construction & Estimation (Topic) Pub Date : 2021-04-07 DOI: 10.2139/ssrn.3821566

M. Caporin, Deniz Erdemlioglu, Stefano Nasini

引用次数: 1

When Good Balance Goes Bad: a Discussion of Common Pitfalls When Using Entropy Balancing 当好的平衡变坏:讨论使用熵平衡时的常见陷阱

ERN: Model Construction & Estimation (Topic) Pub Date : 2021-02-15 DOI: 10.2139/ssrn.3786224

Jeff L. McMullin, B. Schonberger

引用次数: 14

Addressing Endogeneity Without Strong Instruments: A Practical Guide to Heteroskedasticity-Based Instrumental Variables (HBIV) 在没有强大工具的情况下解决内生性:基于异方差的工具变量(HBIV)的实用指南

ERN: Model Construction & Estimation (Topic) Pub Date : 2021-02-05 DOI: 10.2139/ssrn.3293789

Bernardo F. Quiroga

引用次数: 3

Employment Prediction Using Logistic Regression Algorithm 基于Logistic回归算法的就业预测

ERN: Model Construction & Estimation (Topic) Pub Date : 2020-12-27 DOI: 10.2139/ssrn.3755922

A. Dasgupta, D. Ghosh, Jatin Vyas

引用次数: 0

Nonparametric Time-Varying Panel Data Models with Heterogeneity 异质性的非参数时变面板数据模型

ERN: Model Construction & Estimation (Topic) Pub Date : 2020-12-06 DOI: 10.2139/ssrn.3743529

Fei Liu

引用次数: 0

Simplified Stochastic Calculus via Semimartingale Representations 基于半鞅表示的简化随机微积分

ERN: Model Construction & Estimation (Topic) Pub Date : 2020-06-21 DOI: 10.2139/ssrn.3633638

A. Černý, J. Ruf

引用次数: 5