{"title":"Solving a Typical Small Sample Size MRSM Dataset Problem Using a Flexible Hybrid Ensemble Approach for Credibility","authors":"D. Chikobvu, Domingo Pavolo","doi":"10.19139/soic-2310-5070-1111","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1111","url":null,"abstract":"Multiresponse surface methodology often involves small data analytics which, statistically, have regression modelling credibility problems. This is worsened by dataset, model selection and solution methodology uncertainties. It is difficult for solution methodologies which select and use single best models per response at simultaneous optimisation to effectively deal with these problems. This paper exploited the fact that model selection criteria choose differently, in a flexible hybrid ensemble system, to generate several solutions for integration and comparison. Mean square prediction error, with bias-variance-covariance decomposition values, was computed and analysed at simultaneous optimisation. Results suggest that the credibility of the final solution is enhanced when working with multiple models, solution methodologies and results. However, the results do not show any significance of small sample size correction to model selection criteria and analysis of bias-variance-covariance decompositions at simultaneous optimisation does not encourage dependence on theoretical optimality for best results.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140513741","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}
Suryadi, M. Romadona, Sigit Setiawan, Fachrizal, Andi Budiansyah, Syahrizal Maulana, Rahmi Lestari Helmi, Silmi Tsurayya, RY Kun Haribowo, Yuni Andari, Bagaskara, Ratna Sri Harjanti
{"title":"Unemployment Rates in Vocational Education in Indonesia Using Economic and Statistical Analysis","authors":"Suryadi, M. Romadona, Sigit Setiawan, Fachrizal, Andi Budiansyah, Syahrizal Maulana, Rahmi Lestari Helmi, Silmi Tsurayya, RY Kun Haribowo, Yuni Andari, Bagaskara, Ratna Sri Harjanti","doi":"10.19139/soic-2310-5070-1887","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1887","url":null,"abstract":"The linear regression model is used in this research to study the influence of the independent variable on the dependent variable. The dependent variable Y is the unemployment rate in vocational education, while the independent variables are X1 in the form of Job Opportunities, X2 in the form of Policy and X3 in the form of Area. To estimate model parameters, the Ordinary Least Square method is used. The research results show that the three independent variables have a significant effect on the dependent variable. Variable X1 has a significant positive effect on the unemployment rate, variables X2 and X3 have a significant negative effect on the unemployment rate in vocational higher education in Indonesia. From the results of this research, there has been an oversupply of labor in vocational higher education in Indonesia.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"31 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140513528","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}
{"title":"Risk assessment in cryptocurrency portfolios: a composite hidden Markov factor analysis framework","authors":"Mohamed Saidane","doi":"10.19139/soic-2310-5070-1837","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1837","url":null,"abstract":"In this paper, we deal with the estimation of two widely used risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES) in a cryptocurrency context. To face the presence of regime switching in the cryptocurrency volatilities and the dynamic interconnection between them, we propose a Monte Carlo-based approach using heteroskedastic factor analysis and hidden Markov models (HMM) combined with a structured variational Expectation-Maximization (EM) learning approach. This composite approach allows the construction of a diversified portfolio and determines an optimal allocation strategy making it possible to minimize the conditional risk of the portfolio and maximize the return. The out-of-sample prediction experiments show that the composite factorial HMM approach performs better, in terms of prediction accuracy, than some other baseline methods presented in the literature. Moreover, our results show that the proposed methodology provides the best performing crypto-asset allocation strategies and it is also clearly superior to the existing methods in VaR and ES predictions.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"3 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140513747","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}
Huiying Huang, Shaoting Peng, Gaohang Yu, Jinhong Huang, Wenyu Hu
{"title":"Hyperspectral image restoration based on color superpixel segmentation","authors":"Huiying Huang, Shaoting Peng, Gaohang Yu, Jinhong Huang, Wenyu Hu","doi":"10.19139/soic-2310-5070-1912","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1912","url":null,"abstract":"Hyperspectral images (HSI) are often degraded by various types of noise during the acquisition process, such as Gaussian noise, impulse noise, dead lines and stripes, etc. Recently, there exists a growing attenrion on low-rank matrix/tensor-based methods for HSI data restoration, assuming that the overall data is low-rank. However, the assumption of overall low-rankness often proves inaccurate due to the spatially heterogeneous local similarity characteristics of HSI. Traditional cube-based methods involve dividing the HSI into fixed-size cubes. However, using fixed-size cubes does not provide flexible coverage of locally similar regions at varying scales. Inspired by superpixel segmentation, this paper proposes the Shrink Low-rank Super-tensor (SLRST) approach for HSI recovery. Instead of using fixed-size cubes, SLRST employs a size-adaptive super-tensor. The proposed approach is effectively solved using the Alternating Direction Method of Multipliers (ADMM). Numerical experiments on HSI data verify that the proposed method outperforms other competing methods.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"52 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139154506","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}
M. Eliwa, Abhishek Tyagi, Morad Alizadeh, M. El-Morshedy
{"title":"Failure rate, vitality, and residual lifetime measures: Characterizations based on stress-strength bivariate model with application to an automated life test data","authors":"M. Eliwa, Abhishek Tyagi, Morad Alizadeh, M. El-Morshedy","doi":"10.19139/soic-2310-5070-1321","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1321","url":null,"abstract":"In this article, we introduce some reliability concepts for the bivariate Pareto Type II distribution including joint hazard rate function, CDF for parallel and series systems, joint mean residual lifetime, and joint vitality function. The maximum likelihood and Bayesian estimation methods are utilized to estimate the model parameters. Simulation is carried out to assess the performance of the maximum likelihood and Bayesian estimators, and it is found that the two approaches work quite well in estimation process. Finally, a real lifetime data is analyzed to show the flexibility and the importance of the introduced bivariate mode.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139265043","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}
{"title":"Implementation of Fuzzy Logic Controller Algorithms with MF optimization on FPGA","authors":"Samet Ahmed, Kourd Yahia","doi":"10.19139/soic-2310-5070-1790","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1790","url":null,"abstract":"In this work, we propose the design and implementation of a parallel-structured fuzzy logic controller with integral action and anti-windup. The Grey Wolf Optimization (GWO) optimization technique is used to optimize fuzzy rules, which allows for the complicated algebraic ideas of type 1 fuzzy logic algorithms to be reduced to straightforward numerical equations for FPGA target implementation. The techniques for operating a geared DC motor are optimized by the membership function structure of our controller's data propagation. Our proposed controller was implemented in Xilinx System Generator (XSG) and co-simulated on hardware and software with VIVADO and XSG tools.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139278843","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}
{"title":"Statistical Analysis of Covid-19 Data using the Odd Log Logistic Kumaraswamy Distribution","authors":"F. Opone, Kadir Karakaya, Ngozi O. Ubaka","doi":"10.19139/soic-2310-5070-1572","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1572","url":null,"abstract":"This paper presents a statistical analysis of Covid-19 data using the Odd log logistic kumaraswamy Kumaraswamy (OLLK) distribution. Some mathematical properties of the proposed OLLK distribution such as the survival and hazard functions, quantile function, ordinary and incomplete moments, moment generating function, probability weighted moment, distribution of order statistic and Renyi entropy were derived. Five estimators are examined for unknown model parameters. The performance of the estimators is compared using an extensive simulation study based on the bias and mean square error criteria. Two Covid-19 data sets representing the percentage of daily recoveries of Covid-19 patients are used to illustrate the applicability of the proposed OLLK distribution. Results revealed that the OLLK distribution is a better alternative to some existing models with bounded support.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"19 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139278356","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}
M. Rasekhi, M. Saber, H. Yousof, Emadeldin I. A. Ali
{"title":"Estimation of the Multicomponent Stress-Strength Reliability Model Under the Topp-Leone Distribution: Applications, Bayesian and Non-Bayesian Assessement","authors":"M. Rasekhi, M. Saber, H. Yousof, Emadeldin I. A. Ali","doi":"10.19139/soic-2310-5070-1685","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1685","url":null,"abstract":"The advantages of applying multicomponent stress-strength models lie in their ability to provide a comprehensive and accurate analysis of system reliability under real-world conditions. By accounting for the interactions between different stress components and identifying critical weaknesses, engineers can make informed decisions, leading to safer and more reliable designs. The primary emphasis of this research is placed on the Bayesian and classical estimations of a multicomponent stress-strength reliability model that is derived from the bounded Topp Leone distribution. It is presumable that both stress and strength follow a Topp Leone distribution, but the shape parameters of each variable differ, and the scale parameters (which determine where the variable is bounded) remain the same. Statisticians utilize approaches such as maximum likelihood paired with parametric and non-parametric bootstrap, as well as Bayesian methods, in order to evaluate the dependability of a system. Bayesian methods are also utilized. Simulation studies are carried out with the intention of establishing the degree of precision that may be achieved by employing the various methods of estimating. For the sake of this example, two genuine data sets are dissected and examined in detail.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139279113","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}
{"title":"The Topp-Leone Odd Burr X-G Family of Distributions: Properties and Applications","authors":"B. Oluyede, B. Tlhaloganyang, Whatmore Sengweni","doi":"10.19139/soic-2310-5070-1673","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1673","url":null,"abstract":"This paper proposes a new generalized family of distributions called the Topp-Leone odd Burr X-G (TLOBX-G) distribution and its special model, Topp-Leone odd Burr X-Weibull (TLOBX-W) is studied in detail. Structural properties are derived, including the hazard rate function, quantile function, density expansion, moments, R'enyi entropy, and order statistics. The maximum likelihood technique is used to estimate the parameters of the new family of distributions and a simulation study was carried out to assess the accuracy and consistency of these estimators. Finally, the applicability, usefulness, and flexibility of TLOBX-W distribution are illustrated using two real-life datasets.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"79 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139279086","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}
{"title":"A new routing method based on ant colony optimization in vehicular ad-hoc network","authors":"Oussama Sbayti, Khalid Housni","doi":"10.19139/soic-2310-5070-1766","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1766","url":null,"abstract":"Vehicular Ad hoc Networks (VANETs) face significant challenges in providing high-quality service. These networks enable vehicles to exchange critical information, such as road obstacles and accidents, and support various communication modes known as Vehicle-to-Everything (V2X). This research paper proposes an intelligent method to improve the quality of service by optimizing path selection between vehicles, aiming to minimize network overhead and enhance routing efficiency. The proposed approach integrates Ant Colony Optimization (ACO) into the Optimized Link State Routing (OLSR) protocol. The effectiveness of this method is validated through implementation and simulation experiments conducted using the Simulation of Urban Mobility (SUMO) and the network simulator (NS3). Simulation results demonstrate that the proposed method outperforms the traditional OLSR algorithm in terms of throughput, average packet delivery rate (PDR), end-to-end delay (E2ED), and average routing overhead.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"32 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139279233","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}