P. M. Jaslam, M. Kumar, N. Bhardwaj, Salinder, Vikash Kumar , Sumit
Crop statistics for a small area, such as the community development block, are an increasingly important topic in agricultural statistics. Under normality assumptions, the classic Empirical Best Linear Unbiased Prediction (EBLUP) technique is effective for predicting small area means, however the Small Area Estimation (SAE) model can be heavily affected by the incidence of outliers or deviations from the expected distribution. The purpose of this study was to estimate variance, predict block-level wheat crop yield in the Hisar and Sirsa district of Haryana by classical SAE method and a robust random-effect predictor using a slight generalization of Huber’s Proposal 2. In the case of Sirsa district, the results of classical and robust unit level SAE were very close, but not in the case of Hisar district. This could be due to the influential observation found in the Hisar data set. More accurate EBLUP wheat yield estimates are obtained when the Huber-type M-estimation method is initialized by the least square regression estimator.
{"title":"Analysis of unit level models for small area estimation in crop statistics assisted with satellite auxiliary information","authors":"P. M. Jaslam, M. Kumar, N. Bhardwaj, Salinder, Vikash Kumar , Sumit","doi":"10.3233/mas-221416","DOIUrl":"https://doi.org/10.3233/mas-221416","url":null,"abstract":"Crop statistics for a small area, such as the community development block, are an increasingly important topic in agricultural statistics. Under normality assumptions, the classic Empirical Best Linear Unbiased Prediction (EBLUP) technique is effective for predicting small area means, however the Small Area Estimation (SAE) model can be heavily affected by the incidence of outliers or deviations from the expected distribution. The purpose of this study was to estimate variance, predict block-level wheat crop yield in the Hisar and Sirsa district of Haryana by classical SAE method and a robust random-effect predictor using a slight generalization of Huber’s Proposal 2. In the case of Sirsa district, the results of classical and robust unit level SAE were very close, but not in the case of Hisar district. This could be due to the influential observation found in the Hisar data set. More accurate EBLUP wheat yield estimates are obtained when the Huber-type M-estimation method is initialized by the least square regression estimator.","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":"45530153","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}
Frailty models are used in the survival analysis to account for the unobserved heterogeneity in individual risks to disease and death. To analyze the bivariate data on related survival times (e.g. matched pairs experiments, twin or family data), the shared frailty models were suggested. Shared frailty models are used despite their limitations. To overcome their disadvantages correlated frailty models may be used. In this paper, we introduce the correlated compound geometric frailty models based on reversed hazard rate with three different baseline distributions namely, the generalized log-logistic type I, the generalized log-logistic type II and the modified inverse Weibull. We introduce the Bayesian estimation procedure using Markov Chain Monte Carlo (MCMC) technique to estimate the parameters involved in these models. We present a simulation study to compare the true values of the parameters with the estimated values. We also apply the proposed models to the Australian twin data set and a better model is suggested.
{"title":"Correlated compound geometric frailty models based on reversed hazard rate","authors":"David D. Hanagal","doi":"10.3233/mas-221414","DOIUrl":"https://doi.org/10.3233/mas-221414","url":null,"abstract":"Frailty models are used in the survival analysis to account for the unobserved heterogeneity in individual risks to disease and death. To analyze the bivariate data on related survival times (e.g. matched pairs experiments, twin or family data), the shared frailty models were suggested. Shared frailty models are used despite their limitations. To overcome their disadvantages correlated frailty models may be used. In this paper, we introduce the correlated compound geometric frailty models based on reversed hazard rate with three different baseline distributions namely, the generalized log-logistic type I, the generalized log-logistic type II and the modified inverse Weibull. We introduce the Bayesian estimation procedure using Markov Chain Monte Carlo (MCMC) technique to estimate the parameters involved in these models. We present a simulation study to compare the true values of the parameters with the estimated values. We also apply the proposed models to the Australian twin data set and a better model is suggested.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41862128","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}
The Sars-Cov-2 coronavirus outbreak significantly impacted Ghana’s educational system, driving schools to close campuses and swiftly deploy online instruction. This study evaluated e-teaching in higher education amidst the Sars-Cov-2 coronavirus by using the University of Ghana as a case study. Specifically, the study investigated university instructors’ preferences for online instructional strategies to enable higher educational institutions to transit smoothly into online teaching and learning. With the help of a face-to-face questionnaire administration, this cross-sectional study used a discrete choice experiment design to capture the responses of 230-course instructors. The analysis of the survey data obtained was possible using the multinomial logit model. Our results revealed that a recorded lecture video had the highest preference among the course instructors, breakdown of teaching content for approximately 30 to 45 minutes, providing online tutorials, and online support/video tutoring from teaching assistants were the important instructional attributes to help higher educational institutions transition into online teaching and learning.
{"title":"COVID-19 and online teaching in higher education: A discrete choice experiment","authors":"E. Nyarko, Eddie Agyemang, D. Arku","doi":"10.3233/mas-221403","DOIUrl":"https://doi.org/10.3233/mas-221403","url":null,"abstract":"The Sars-Cov-2 coronavirus outbreak significantly impacted Ghana’s educational system, driving schools to close campuses and swiftly deploy online instruction. This study evaluated e-teaching in higher education amidst the Sars-Cov-2 coronavirus by using the University of Ghana as a case study. Specifically, the study investigated university instructors’ preferences for online instructional strategies to enable higher educational institutions to transit smoothly into online teaching and learning. With the help of a face-to-face questionnaire administration, this cross-sectional study used a discrete choice experiment design to capture the responses of 230-course instructors. The analysis of the survey data obtained was possible using the multinomial logit model. Our results revealed that a recorded lecture video had the highest preference among the course instructors, breakdown of teaching content for approximately 30 to 45 minutes, providing online tutorials, and online support/video tutoring from teaching assistants were the important instructional attributes to help higher educational institutions transition into online teaching and learning.","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":"42203977","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}
When trend and seasonality are detected, the Holt-Winters multiplicative approach is one of the most commonly used methods for forecasting time series data. Choosing the proper initial values for level, trend, and seasonality plays a vital role in this method. In this paper, a new and efficient procedure to choose the initial values for the Holt-Winters multiplicative method is developed. A total of 12 types of agricultural satellite backscatter values are used for analysis, estimated, and compared with the existing Hansun and Holt-Winters methods and the proposed initial setting method with the best smoothing constants. According to the analysis of the mean absolute percentage error, symmetric mean absolute percentage error, Theil-U statistics, and root mean squared error, the proposed approaches outperformed the existing methods in this experiment.
{"title":"Heuristics approach for the Holt-Winters multiplicative method with new initial values","authors":"Victor Anthonysamy, Khadar Babu, C. Chesneau","doi":"10.3233/mas-221415","DOIUrl":"https://doi.org/10.3233/mas-221415","url":null,"abstract":"When trend and seasonality are detected, the Holt-Winters multiplicative approach is one of the most commonly used methods for forecasting time series data. Choosing the proper initial values for level, trend, and seasonality plays a vital role in this method. In this paper, a new and efficient procedure to choose the initial values for the Holt-Winters multiplicative method is developed. A total of 12 types of agricultural satellite backscatter values are used for analysis, estimated, and compared with the existing Hansun and Holt-Winters methods and the proposed initial setting method with the best smoothing constants. According to the analysis of the mean absolute percentage error, symmetric mean absolute percentage error, Theil-U statistics, and root mean squared error, the proposed approaches outperformed the existing methods in this experiment.","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":"49392579","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}
The new exponential power-G is introduced following Alzaatreh et al. (2013). Some of its main statistical properties are provided in terms of the exponentiated-G properties. Maximum likelihood estimation and simulations are addressed using the log-logistic for the baseline distribution. The log-exponential power log-logistic regression model is constructed and applied to censored data. The utility of the new models is proved by means of two real data sets.
{"title":"The exponential power-G family of distributions: Properties, simulations, regression modeling and applications","authors":"Alexsandro A. Ferreira, G. Cordeiro","doi":"10.3233/mas-221400","DOIUrl":"https://doi.org/10.3233/mas-221400","url":null,"abstract":"The new exponential power-G is introduced following Alzaatreh et al. (2013). Some of its main statistical properties are provided in terms of the exponentiated-G properties. Maximum likelihood estimation and simulations are addressed using the log-logistic for the baseline distribution. The log-exponential power log-logistic regression model is constructed and applied to censored data. The utility of the new models is proved by means of two real data sets.","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":"47921058","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}
There is large online lending growth in volume world-wide. The credit risk concerns point to the fact that most of these loans might be used to redeem earlier borrowed funds. However, the true reasons for online borrowing and lending are unavailable. Benford law is one of the tools used by auditors to monitor how suspicious the transaction is. That is why I wish to study one of the publicly available lending portfolios. Our objective is to trace associativity of compliance to Benford law and reported default rates. I find that MAE is a more statistically significant determinant of the country portfolio default rate, than RMSE. Moreover, the least creditworthy portfolios seem to be those with the MAE around 52–56%, while the closest to Benford and the least adjacent distribution do not demonstrate that large default rates.
{"title":"Non-compliance to Benford distribution as the portfolio default rate determinant in online retail lending","authors":"H. Penikas","doi":"10.3233/mas-221404","DOIUrl":"https://doi.org/10.3233/mas-221404","url":null,"abstract":"There is large online lending growth in volume world-wide. The credit risk concerns point to the fact that most of these loans might be used to redeem earlier borrowed funds. However, the true reasons for online borrowing and lending are unavailable. Benford law is one of the tools used by auditors to monitor how suspicious the transaction is. That is why I wish to study one of the publicly available lending portfolios. Our objective is to trace associativity of compliance to Benford law and reported default rates. I find that MAE is a more statistically significant determinant of the country portfolio default rate, than RMSE. Moreover, the least creditworthy portfolios seem to be those with the MAE around 52–56%, while the closest to Benford and the least adjacent distribution do not demonstrate that large default rates.","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":"49363532","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}
Eddie Agyemang, E. N. Nortey, R. Minkah, K. Asah-Asante
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.
{"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}
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.
{"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}
Seasonality is an inherent part of most of the epidemic data. The fixed coefficient INAR(1) models with seasonal structure have been studied by many authors. The varying immunity and susceptibility affect the chances of catching or escaping an infection. This brings in the randomness in the phenomenon of the spread of the diseases. The fixed coefficient INAR models assume that the chance of infection remains the same for every individual, which is not true practically and hence one needs to study the disease spread phenomenon using random coefficient INAR models. The parameters of the proposed model have been estimated using quasi maximum likelihood estimation. Various probabilistic and inferential properties of the model have been studied. A simulation study has been carried out for parameter estimation. Two data sets having seasonal structures have been analyzed using the model. The model fits well to the data sets compared to the existing models.
{"title":"A random coefficient integer autoregressive model of order one with seasonal structure (RCINAR(1)s)","authors":"Manik Awale, A. Kashikar","doi":"10.3233/mas-211333","DOIUrl":"https://doi.org/10.3233/mas-211333","url":null,"abstract":"Seasonality is an inherent part of most of the epidemic data. The fixed coefficient INAR(1) models with seasonal structure have been studied by many authors. The varying immunity and susceptibility affect the chances of catching or escaping an infection. This brings in the randomness in the phenomenon of the spread of the diseases. The fixed coefficient INAR models assume that the chance of infection remains the same for every individual, which is not true practically and hence one needs to study the disease spread phenomenon using random coefficient INAR models. The parameters of the proposed model have been estimated using quasi maximum likelihood estimation. Various probabilistic and inferential properties of the model have been studied. A simulation study has been carried out for parameter estimation. Two data sets having seasonal structures have been analyzed using the model. The model fits well to the data sets compared to the existing models.","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":"42292352","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}
Tom Britt, Jack Nusbaum, Alexandra Savinkina, A. Shemyakin
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.
{"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}
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