发布求助
文献互助 智能选刊 最新文献

Statistics, Optimization & Information Computing最新文献

筛选
英文 中文
In-depth Analysis of von Mises Distribution Models: Understanding Theory, Applications, and Future Directions 深入分析冯-米塞斯分布模型：理解理论、应用和未来方向
Statistics, Optimization & Information Computing Pub Date : 2024-06-06 DOI: 10.19139/soic-2310-5070-1919
Said Benlakhdar, Mohammed Rziza, Rachid Oulad Haj Thami
{"title":"In-depth Analysis of von Mises Distribution Models: Understanding Theory, Applications, and Future Directions","authors":"Said Benlakhdar, Mohammed Rziza, Rachid Oulad Haj Thami","doi":"10.19139/soic-2310-5070-1919","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1919","url":null,"abstract":"Multimodal and asymmetric circular data manifest in diverse disciplines, underscoring the significance of fitting suitable distributions for the analysis of such data. This study undertakes a comprehensive comparative assessment, encompassing diverse extensions of the von Mises distribution and the associated statistical methodologies, spanning from Richard von Mises' seminal work in 1918 to contemporary applications, with a particular focus on the field of wind energy. The primary objective is to discern the strengths and limitations inherent in each method. To illustrate the practical implications, three authentic datasets and a simulation study are incorporated to showcase the performance of the proposed models. Furthermore, this paper provides an exhaustive list of references pertinent to von Mises distribution models.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"337 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380899","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
Bayesian and Non-Bayesian Estimation for The Parameter of Inverted Topp-Leone Distribution Based on Progressive Type I Censoring 基于渐进 I 型删减的倒 Topp-Leone 分布参数的贝叶斯和非贝叶斯估计
Statistics, Optimization & Information Computing Pub Date : 2024-06-04 DOI: 10.19139/soic-2310-5070-1768
H. Muhammed, E. Muhammed
{"title":"Bayesian and Non-Bayesian Estimation for The Parameter of Inverted Topp-Leone Distribution Based on Progressive Type I Censoring","authors":"H. Muhammed, E. Muhammed","doi":"10.19139/soic-2310-5070-1768","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1768","url":null,"abstract":"In this paper, Bayesian and non-Bayesian estimations of the shape parameter of the Inverted Topp-Leone distribution are studied under a progressive Type I censoring scheme. The maximum likelihood estimator (MLE) and Bayes estimator (BE) of the unknown parameter under the squared error loss (SEL) function are obtained. Three types of confidence intervals are discussed for the unknown parameter. A simulation study is performed to compare the performances of the proposed methods, and two numerical examples have been analyzed for illustrative purposes. ","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387272","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
Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets 解决不平衡数据集分类问题的不平衡数据管理技术比较评估
Statistics, Optimization & Information Computing Pub Date : 2024-02-18 DOI: 10.19139/soic-2310-5070-1890
Tanawan Watthaisong, K. Sunat, Nipotepat Muangkote
{"title":"Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets","authors":"Tanawan Watthaisong, K. Sunat, Nipotepat Muangkote","doi":"10.19139/soic-2310-5070-1890","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1890","url":null,"abstract":"Dealing with imbalanced data is crucial and challenging when developing effective machine-learning models for data classification purposes. It significantly impacts the classification model's performance without proper data management, leading to suboptimal results. Many methods for managing imbalanced data have been studied and developed to improve data balance. In this paper, we conduct a comparative study to assess the influence of a ranking technique on the evaluation of the effectiveness of 66 traditional methods for addressing imbalanced data. The three classification models, i.e., Decision Tree, Random Forest, and XGBoost, act as classification models. The experimental settings have been divided into two segments. The first part evaluates the performance of various imbalanced dataset handling methods, while the second part compares the performance of the top 4 oversampling methods. The study encompasses 50 separate datasets: 20 retrieved from the UCI repository and 30 sourced from the OpenML repository. The evaluation is based on F-Measure and statistical methods, including the Kruskal-Wallis test and Borda Count, to rank the data imbalance handling capabilities of the 66 methods. The SMOTE technique is the benchmark for comparison due to its popularity in handling imbalanced data. Based on the experimental results, the MCT, Polynom-fit-SMOTE, and CBSO methods were identified as the top three performers, demonstrating superior effectiveness in managing imbalanced datasets. This research could be beneficial and serve as a practical guide for practitioners to apply suitable techniques for data management.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"158 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452528","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
An Effective Randomized Algorithm for Hyperspectral Image Feature Extraction 高光谱图像特征提取的有效随机算法
Statistics, Optimization & Information Computing Pub Date : 2024-02-18 DOI: 10.19139/soic-2310-5070-1980
Jinhong Feng, Rui Yan, Gaohang Yu, Zhongming Chen
{"title":"An Effective Randomized Algorithm for Hyperspectral Image Feature Extraction","authors":"Jinhong Feng, Rui Yan, Gaohang Yu, Zhongming Chen","doi":"10.19139/soic-2310-5070-1980","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1980","url":null,"abstract":"Analyzing the spectral and spatial characteristics of Hyperspectral Imaging (HSI) in a three-dimensional space is a challenging task. Recently, there have been developments in 3D feature extraction methods based on tensor decomposition, which allow for the effective utilization of both global and local information in HSI. These methods also explore the inherent low-rank properties of HSI through tensor decomposition. In this paper, we propose a new approach called variable randomized T-product decomposition (Vrt-SVD), which is a variation of Tensor Singular Spectral Analysis. The goal of this approach is to improve the efficiency of tensor methods for feature extraction and reduce artifacts of image processing. By using a randomized algorithm based on the variable t-SVD, we are able to capture both global and local spatial and spectral information in HSI efficiently, which enables us to explore its low-rank characteristics. To evaluate the effectiveness of the extracted features, we use a Support Vector Machine (SVM) classifier to assess the accuracy of image classification. By conducting numerous numerical experiments, we provide strong evidence to show that the proposed method outperforms several advanced feature extraction techniques.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"42 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452988","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
An Algorithm for Solving Quadratic Programming Problems with an M-matrix 用 M 矩阵求解二次编程问题的算法
Statistics, Optimization & Information Computing Pub Date : 2024-02-18 DOI: 10.19139/soic-2310-5070-1399
Katia Hassaini, Mohand Ouamer Bibi
{"title":"An Algorithm for Solving Quadratic Programming Problems with an M-matrix","authors":"Katia Hassaini, Mohand Ouamer Bibi","doi":"10.19139/soic-2310-5070-1399","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1399","url":null,"abstract":"In this study, we propose an approach for solving a quadraticprogramming problem with an M-matrix and simple constraints (QPs). It isbased on the algorithms of Luk-Pagano and Stachurski. These methods usethe fact that an M-matrix possesses a nonnegative inverse which allows tohave a sequence of feasible points monotonically increasing. Introducing theconcept of support for an objective function developed by Gabasov et al., ourapproach leads to a more general condition which allows to have an initialfeasible solution, related to a coordinator support and close to the optimalsolution. The programming under MATLAB of our method and that of Lukand Pagano has allowed us to make a comparison between them, with anillustration on two numerical examples.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"205 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452673","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
The Type II Exponentiated Half Logistic-Gompertz-G Power Series Class of Distributions: Properties and Applications 第二类指数化半对数-Gompertz-G 幂级数分布：性质与应用
Statistics, Optimization & Information Computing Pub Date : 2024-02-17 DOI: 10.19139/soic-2310-5070-1721
Simbarashe Chamunorwa, B. Oluyede, Thatayone Moakofi, Fastel Chipepa
{"title":"The Type II Exponentiated Half Logistic-Gompertz-G Power Series Class of Distributions: Properties and Applications","authors":"Simbarashe Chamunorwa, B. Oluyede, Thatayone Moakofi, Fastel Chipepa","doi":"10.19139/soic-2310-5070-1721","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1721","url":null,"abstract":"We propose and study a new generalized class of distributions called the Type II Exponentiated Half Logistic-Gompertz-G Power Series (TIIEHL-Gom-GPS) distribution. Some structural properties including expansion of density,ordinary and conditional moments, generating function, order statistics and entropy are derived. We present some specialcases of the proposed distribution. The maximum likelihood method is used for estimating the model parameters. Theimportance of the new class of distributions are illustrated by means of two applications to real data sets.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"134 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453087","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
Using transfer adaptation method for dynamic features expansion in multi-label deep neural network for recommender systems 在用于推荐系统的多标签深度神经网络中使用转移适应法进行动态特征扩展
Statistics, Optimization & Information Computing Pub Date : 2024-02-17 DOI: 10.19139/soic-2310-5070-1836
F. Abdullayeva, Suleyman Suleymanzade
{"title":"Using transfer adaptation method for dynamic features expansion in multi-label deep neural network for recommender systems","authors":"F. Abdullayeva, Suleyman Suleymanzade","doi":"10.19139/soic-2310-5070-1836","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1836","url":null,"abstract":"In this paper, we propose to use a convertible deep neural network (DNN) model with a transfer adaptation mechanism to deal with varying input and output numbers of neurons. The flexible DNN model serves as a multi-label classifier for the recommender system as part of the retrieval systems’ push mechanism, which learns the combination of tabular features and proposes the number of discrete offers (targets). Our retrieval system uses the transfer adaptation, mechanism, when the number of features changes, it replaces the input layer of the neural network then freezes all gradients on the following layers, trains only replaced layer, and unfreezes the entire model. The experiments show that using the transfer adaptation technique impacts stable loss decreasing and learning speed during the training process. \u0000  \u0000","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453225","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
Statistical Models to Measure the Impact of Intellectual Property Rights Protection on Foreign Trade in Egypt 衡量知识产权保护对埃及对外贸易影响的统计模型
Statistics, Optimization & Information Computing Pub Date : 2024-01-29 DOI: 10.19139/soic-2310-5070-1870
Hanaa Hussein Ali
{"title":"Statistical Models to Measure the Impact of Intellectual Property Rights Protection on Foreign Trade in Egypt","authors":"Hanaa Hussein Ali","doi":"10.19139/soic-2310-5070-1870","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1870","url":null,"abstract":"This study aims to estimate the relationship between the Protection of intellectual property rights indices and the foreign trade index in Egypt from 1995 to 2022. The comparison has been made between many models such as full modified ordinary least squares (FMOLS) model, dynamic ordinary least squares (DOLS) model , canonical co-integration regression (CCR) model and autoregressive distributed lag (ARDL) model. The results of the study showed that the best model was the ARDL model to increase its interpretive capacity. The study also showed that the most important property rights protection indicators affecting the foreign trade index are the number of applications and registrations of brands, the number of patents registered and granted, the number of applications and registrations of industrial designs, and the proportion of expenditure on research and development as a proportion of gross domestic product (GDP). The estimated model also passed all diagnostic tests and showed that there was no autocorrelation, no Heteroskedasticity. In addition, it was found to follow a normal distribution and to be stable.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"40 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140488944","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
Bayesian Estimation of the Odd Lindley Exponentiated Exponential Distribution : Applications in-Reliability 奇数林德利指数分布的贝叶斯估计：在可靠性方面的应用
Statistics, Optimization & Information Computing Pub Date : 2024-01-29 DOI: 10.19139/soic-2310-5070-1880
Nour El houda Djemoui, A. Chadli, Ilhem Merah
{"title":"Bayesian Estimation of the Odd Lindley Exponentiated Exponential Distribution : Applications in-Reliability","authors":"Nour El houda Djemoui, A. Chadli, Ilhem Merah","doi":"10.19139/soic-2310-5070-1880","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1880","url":null,"abstract":"         In this work, we investigate the estimation of the unknown parameters and the reliability characteristics ofthe Odd Lindley Exponentiated Exponential distribution. The Bayes estimators and corresponding risks are derived usingvarious loss functions with complete data and a gamma prior distribution. A simulation study was carried out to calculate allthe results. We used Pitman’s closeness criterion and the integrated mean squared error to compare the performance of theBayesian and maximum likelihood estimators. Finally, we illustrate our techniques by analysing a real-life data set.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"58 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140486632","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
On Probabilistic Cooperative Search Model to Detect a Lost Target in N-Disjoint Areas 论在 N 个不相连区域检测丢失目标的概率合作搜索模型
Statistics, Optimization & Information Computing Pub Date : 2024-01-17 DOI: 10.19139/soic-2310-5070-1876
Mohamed Abd, Allah El-Hadidy, M. Fakharany
{"title":"On Probabilistic Cooperative Search Model to Detect a Lost Target in N-Disjoint Areas","authors":"Mohamed Abd, Allah El-Hadidy, M. Fakharany","doi":"10.19139/soic-2310-5070-1876","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1876","url":null,"abstract":"This paper presents a new probabilistic coordinated search technique for finding a randomly located target in n-disjoint known regions by using n-searchers. Each region contains one searcher. The searchers use advanced technology to communicate with each other. The purpose of this paper is to obtain the candidate utility function namely the expected value of the time for detecting the target. Additionally, to minimize this expected value given a restricted amount of time. We present a special case when the target has a multinomial distribution. This important for searching about a valuable target missing at sea or lost at wilderness area.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"40 9-10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140505550","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: 2
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信