Model Assisted Statistics and Applications最新文献_第4页

Analysis of unit level models for small area estimation in crop statistics assisted with satellite auxiliary information 卫星辅助信息辅助下作物统计小区域估计的单位级模型分析

Model Assisted Statistics and Applications Pub Date : 2023-06-15 DOI: 10.3233/mas-221416

P. M. Jaslam, M. Kumar, N. Bhardwaj, Salinder, Vikash Kumar , Sumit

引用次数: 0

Model Assisted Statistics and Applications Pub Date : 2023-06-15 DOI: 10.3233/mas-221414

David D. Hanagal

引用次数: 0

COVID-19 and online teaching in higher education: A discrete choice experiment COVID-19与高等教育在线教学:一个离散选择实验

Model Assisted Statistics and Applications Pub Date : 2023-06-15 DOI: 10.3233/mas-221403

E. Nyarko, Eddie Agyemang, D. Arku

引用次数: 0

Heuristics approach for the Holt-Winters multiplicative method with new initial values 具有新初始值的Holt-Winters乘法方法的启发式方法

Model Assisted Statistics and Applications Pub Date : 2023-06-15 DOI: 10.3233/mas-221415

Victor Anthonysamy, Khadar Babu, C. Chesneau

引用次数: 0

The exponential power-G family of distributions: Properties, simulations, regression modeling and applications 指数幂- g族分布:性质、模拟、回归建模和应用

Model Assisted Statistics and Applications Pub Date : 2023-06-15 DOI: 10.3233/mas-221400

Alexsandro A. Ferreira, G. Cordeiro

引用次数: 0

Non-compliance to Benford distribution as the portfolio default rate determinant in online retail lending 不遵守Benford分布作为在线零售贷款组合违约率的决定因素

Model Assisted Statistics and Applications Pub Date : 2023-06-15 DOI: 10.3233/mas-221404

H. Penikas

引用次数: 0

The unfolding mystery of the numbers: First and second digits based comparative tests and its application to Ghana’s elections 数字之谜：基于第一和第二位数的比较测试及其在加纳选举中的应用

Model Assisted Statistics and Applications Pub Date : 2023-06-15 DOI: 10.3233/mas-221418

Eddie Agyemang, E. N. Nortey, R. Minkah, K. Asah-Asante

{"title":"The unfolding mystery of the numbers: First and second digits based comparative tests and its application to Ghana’s elections","authors":"Eddie Agyemang, E. N. Nortey, R. Minkah, K. Asah-Asante","doi":"10.3233/mas-221418","DOIUrl":"https://doi.org/10.3233/mas-221418","url":null,"abstract":"This study focuses on the use of digits-based test in anomaly detection in presidential elections in Ghana. Even though Ghana has conducted several successful elections to elect presidents, the outcomes of the elections have been challenged in courts on allegations of vote rigging and fraud. It has been established in the literature that for an election to be anomaly free, the following should be satisfied: the distribution of voters turn-out, the winners’ share and total valid votes cast in the election should be uni-modal. Therefore, we assess the applicability of both first and second digits-based tests to aid in the detection of possible anomaly in the 2016 and 2020 presidential election results data in Ghana. The Benford frequency distribution and Spearman rank correlation coefficient tests were used for the analysis of data obtained from the Electoral Commission of Ghana. The results show that the observed first digits distributions of valid vote counts for both New Patriotic Party (NPP) and National Democratic Congress (NDC), and the total valid votes cast (TVVC), in 2016 and 2020 are consistent with the distributional pattern of first digits postulated by Benford’s Law. However, the findings of the distribution of second digits of the valid vote counts for NPP and total valid vote cast in both 2016 and 2020 elections do not satisfy the probability distributional pattern of second digits according to the Benford’s Law. In view of these, we recommend using the first two digits-based tests to check for consistency of possible election anomaly between the first and second digits since it conveys more information.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49508632","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: 1

Identifying target customer needs for a Master’s Degree Program in Industrial Engineering by conjoint analysis and Kano model 通过联合分析和卡诺模型确定工业工程硕士学位课程的目标客户需求

Model Assisted Statistics and Applications Pub Date : 2023-06-15 DOI: 10.3233/mas-221409

N. Phumchusri, Mookarin Thongoiam

{"title":"Identifying target customer needs for a Master’s Degree Program in Industrial Engineering by conjoint analysis and Kano model","authors":"N. Phumchusri, Mookarin Thongoiam","doi":"10.3233/mas-221409","DOIUrl":"https://doi.org/10.3233/mas-221409","url":null,"abstract":"Customer satisfaction has become a key factor in strategic work of many institutions towards the increasing competition regarding student recruitment. This paper presents a systematic approach to identify customer needs for a Master’s Degree Program in Industrial Engineering based on target students’ needs in the view of new product development. The approach consists of two methods: Choice-based conjoint analysis and Kano model. Conjoint analysis is used to explore important scores of each attribute of the program, i.e., specialist concentration, class period, research type, teaching language, teaching format, and tuition fee. Also, the popularity of levels in each attribute are identified. Latent class model is used to identify different clusters of target customers. The result indicates two different segments of different preferences. The heterogeneity of needs and preference is characterized mainly in levels of specialist concentration preference as well as other attributes such as tuition fee. Other attributes such as interdisciplinary, cooperate program, work experience requirement and group (with presence/absence option) are analyzed by Kano model to identify their categories, i.e., how important they are. This research contributes in the literature as a pioneer in applying these two methods to gain customer perception insights about new Master’s curriculum development for education industry.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45015802","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

A random coefficient integer autoregressive model of order one with seasonal structure (RCINAR(1)s) 具有季节结构的一阶随机系数整数自回归模型(RCINAR(1)s)

Model Assisted Statistics and Applications Pub Date : 2023-03-24 DOI: 10.3233/mas-211333

Manik Awale, A. Kashikar

引用次数: 0

Short-term forecast of U.S. COVID mortality using excess deaths and vector autoregression 使用超额死亡和向量自回归对美国新冠肺炎死亡率的短期预测

Model Assisted Statistics and Applications Pub Date : 2023-03-24 DOI: 10.3233/mas-221392

Tom Britt, Jack Nusbaum, Alexandra Savinkina, A. Shemyakin

{"title":"Short-term forecast of U.S. COVID mortality using excess deaths and vector autoregression","authors":"Tom Britt, Jack Nusbaum, Alexandra Savinkina, A. Shemyakin","doi":"10.3233/mas-221392","DOIUrl":"https://doi.org/10.3233/mas-221392","url":null,"abstract":"We analyze overall mortality in the U.S. as a whole and several states in particular in order to make conclusions regarding timing and strength of COVID pandemic effect from an actuarial risk analysis perspective. No effort is made to analyze biological or medical characteristics of the pandemic. We use open data provided by CDC, U.S. state governments and Johns Hopkins University. In the first part of the paper, we suggest time series analysis (ARIMA) for weekly excess U.S. mortality in 2020 as compared to several previous years’ experience in order to build a statistical model and provide short-term forecast based exclusively on historical mortality data. In the second half of the paper, we also analyze weekly COVID cases, hospitalizations and deaths in 2020 and 2021. Two midwestern states, Minnesota and Wisconsin, along with geographically diverse Colorado and Georgia, are used to illustrate global and local patterns in the COVID pandemic data. We suggest vector autoregression (VAR) as a method of simultaneous explanatory and predictive analysis of several variables. VAR is a popular tool in econometrics and financial analysis, but it is less common in problems of risk management related to mortality analysis in epidemiology and actuarial practice. Efficiency of short-term forecast is illustrated by observing the effect of vaccination on COVID development in the state of Minnesota in 2021.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49004379","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