Many cardiac disorders were diagnosed by analyzing an electrocardiogram signal, in particular, atrial fibrillation. We join the SDCST method with the Detrended Fluctuation Analysis (DFA) and the backpropagation net to identify atrial fibrillation in one hundred ECG signals obtained from Physionet Challenge 2017 database. The accuracy of the proposed classifier parameter is 97% for the training set and 95% for the test set.
{"title":"Atrial fibrillation detection by DFA and SDCST methods","authors":"R. N. Vargas, Antônio C. P. Veiga, R. Linhares","doi":"10.3233/mas-210532","DOIUrl":"https://doi.org/10.3233/mas-210532","url":null,"abstract":"Many cardiac disorders were diagnosed by analyzing an electrocardiogram signal, in particular, atrial fibrillation. We join the SDCST method with the Detrended Fluctuation Analysis (DFA) and the backpropagation net to identify atrial fibrillation in one hundred ECG signals obtained from Physionet Challenge 2017 database. The accuracy of the proposed classifier parameter is 97% for the training set and 95% for the test set.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46741883","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}
This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.
{"title":"The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia","authors":"U. Mukhaiyar, Devina Widyanti, Sandy Vantika","doi":"10.3233/mas-210533","DOIUrl":"https://doi.org/10.3233/mas-210533","url":null,"abstract":"This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46384653","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 section in MASA: Guide Handbook of Statistical Techniques (GHOST)","authors":"","doi":"10.3233/mas-210535","DOIUrl":"https://doi.org/10.3233/mas-210535","url":null,"abstract":"","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43222288","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":"15-Year Anniversary of Model Assisted Statistics and Applications (MASA)","authors":"S. Lipovetsky","doi":"10.3233/mas-210520","DOIUrl":"https://doi.org/10.3233/mas-210520","url":null,"abstract":"","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/mas-210520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45264015","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}
This special MASA issue is intended for the problems of statistical modeling of pandemics in general, and the Coronavirus COVID-19 one particularly. A recent analysis in Nature1 shows that the number of papers on coronavirus skyrocketed in the first 4 months of 2020 and then stabilized, more or less in accordance with behavior of the pandemic itself. The statistical modeling of the coronavirus pandemic is also flattened – yet the number of monthly publications is huge, exceeding the “normal pre-pandemic” level in 25–30 times. The goal of “modeling” is to create multiple scenarios, including the pessimistic and optimistic, to be immediately available when circumstances require it. Perhaps, the reasons for the pandemic been so devastating are that the science was not ready, the WHO recommendations were not in the place, effective government plans did not exist, and so on. The period of the preliminary preparations was just lost, which is especially sorrowful, because comparatively recent pandemics, like SARS in 2002–4 and others, gave all the reasons to be timely prepared. It seems, just Taiwan2 took all previous cases seriously and made a strategic plan, which was brazenly ignored by other countries and WHO; the difference between Taiwan and other countries outcomes is now startling. In light of that all, what could be the purpose for the special issue of the statistical journal on pandemic problems? It obviously will not help to reach the ear of decision makers in the struggle with the current wave, which seems starts to calm down. However, the different approaches presented in this issue will help in future preparation for the yet unknown pandemics or epidemics. A wide geography of the authors’ countries and variety of the topics cover somewhat different aspects of statistical modeling of pandemics. A reader should also know that all the papers were in preparation for several months earlier to this issue, while the pandemics was evolving very fast. Some of the quantitative results may look obsolete (although, the authors tried to get maximum in their data collection), but the methodological value of the proposed approaches stays to be useful. Fighting the Coronavirus COVID-19 pandemic required quick developing tests and vaccines, continuing trials and research (Mandel & Lipovetsky, 2020). As a reflection of these efforts, multiple journal articles have been published on the related topics. The coronavirus pandemic covers the most populated areas on Earth, and the spread of infection has been going fast with the global transportation and connectivity of travelers and commerce. With COVID-19 highly infectious features, high transmissivity, often asymptomatic appearance, it spreads with huge consequences in areas of dense populations and poor public health systems. In conditions of the lack of a vaccine, only the forced isolation of the infected serves to decreasing the infection rates. However, within months of the virus
{"title":"Statistical modeling of pandemics and coronavirus","authors":"I. Mandel, B. Zaslavsky, S. Lipovetsky","doi":"10.3233/MAS-210509","DOIUrl":"https://doi.org/10.3233/MAS-210509","url":null,"abstract":"This special MASA issue is intended for the problems of statistical modeling of pandemics in general, and the Coronavirus COVID-19 one particularly. A recent analysis in Nature1 shows that the number of papers on coronavirus skyrocketed in the first 4 months of 2020 and then stabilized, more or less in accordance with behavior of the pandemic itself. The statistical modeling of the coronavirus pandemic is also flattened – yet the number of monthly publications is huge, exceeding the “normal pre-pandemic” level in 25–30 times. The goal of “modeling” is to create multiple scenarios, including the pessimistic and optimistic, to be immediately available when circumstances require it. Perhaps, the reasons for the pandemic been so devastating are that the science was not ready, the WHO recommendations were not in the place, effective government plans did not exist, and so on. The period of the preliminary preparations was just lost, which is especially sorrowful, because comparatively recent pandemics, like SARS in 2002–4 and others, gave all the reasons to be timely prepared. It seems, just Taiwan2 took all previous cases seriously and made a strategic plan, which was brazenly ignored by other countries and WHO; the difference between Taiwan and other countries outcomes is now startling. In light of that all, what could be the purpose for the special issue of the statistical journal on pandemic problems? It obviously will not help to reach the ear of decision makers in the struggle with the current wave, which seems starts to calm down. However, the different approaches presented in this issue will help in future preparation for the yet unknown pandemics or epidemics. A wide geography of the authors’ countries and variety of the topics cover somewhat different aspects of statistical modeling of pandemics. A reader should also know that all the papers were in preparation for several months earlier to this issue, while the pandemics was evolving very fast. Some of the quantitative results may look obsolete (although, the authors tried to get maximum in their data collection), but the methodological value of the proposed approaches stays to be useful. Fighting the Coronavirus COVID-19 pandemic required quick developing tests and vaccines, continuing trials and research (Mandel & Lipovetsky, 2020). As a reflection of these efforts, multiple journal articles have been published on the related topics. The coronavirus pandemic covers the most populated areas on Earth, and the spread of infection has been going fast with the global transportation and connectivity of travelers and commerce. With COVID-19 highly infectious features, high transmissivity, often asymptomatic appearance, it spreads with huge consequences in areas of dense populations and poor public health systems. In conditions of the lack of a vaccine, only the forced isolation of the infected serves to decreasing the infection rates. However, within months of the virus","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/MAS-210509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48387209","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 Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.
{"title":"A hybrid of artificial neural network, exponential smoothing, and ARIMA models for COVID-19 time series forecasting","authors":"S. Safi, O. I. Sanusi","doi":"10.3233/MAS-210512","DOIUrl":"https://doi.org/10.3233/MAS-210512","url":null,"abstract":"The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/MAS-210512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70129847","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}
This article accentuates the estimation of a two-parameter generalized Topp-Leone distribution using dual generalized order statistics (dgos). In the part of estimation, we obtain maximum likelihood (ML) estimates and approximate confidence intervals of the model parameters using dgos, in particular, based on order statistics and lower record values. The Bayes estimate is derived with respect to a squared error loss function using gamma priors. The highest posterior density credible interval is computed based on the MH algorithm. Furthermore, the explicit expressions for single and product moments of dgos from this distribution are also derived. Based on order statistics and lower records, a simulation study is carried out to check the efficiency of these estimators. Two real life data sets, one is for order statistics and another is for lower record values have been analyzed to demonstrate how the proposed methods may work in practice.
{"title":"Inferences for generalized Topp-Leone distribution under dual generalized order statistics with applications to Engineering and COVID-19 data","authors":"D. Kumar, M. Nassar, S. Dey, A. Elshahhat","doi":"10.3233/MAS-210525","DOIUrl":"https://doi.org/10.3233/MAS-210525","url":null,"abstract":"This article accentuates the estimation of a two-parameter generalized Topp-Leone distribution using dual generalized order statistics (dgos). In the part of estimation, we obtain maximum likelihood (ML) estimates and approximate confidence intervals of the model parameters using dgos, in particular, based on order statistics and lower record values. The Bayes estimate is derived with respect to a squared error loss function using gamma priors. The highest posterior density credible interval is computed based on the MH algorithm. Furthermore, the explicit expressions for single and product moments of dgos from this distribution are also derived. Based on order statistics and lower records, a simulation study is carried out to check the efficiency of these estimators. Two real life data sets, one is for order statistics and another is for lower record values have been analyzed to demonstrate how the proposed methods may work in practice.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/MAS-210525","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70130356","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 paper presents a fundamental parametric approach to simultaneous forecasting of a vector of functionally dependent random variables. The motivation behind the proposed method is the following: each random variable at interest is forecasted by its own model and then adjusted in accordance with the functional link. The method incorporates the assumption that models’ errors are independent or weekly dependent. Proposed adjustment is explicit and extremely easy-to-use. Not only does it allow adjusting point forecasts, but also it is possible to adjust the expected variance of errors, that is useful for computation of confidence intervals. Conducted thorough simulation and empirical testing confirms, that proposed method allows to achieve a steady decrease in the mean-squared forecast error for each of predicted variables.
{"title":"Simultaneous prediction of functionally dependent random variables by maximum likelihood estimation","authors":"N. Moiseev","doi":"10.3233/MAS-210526","DOIUrl":"https://doi.org/10.3233/MAS-210526","url":null,"abstract":"The paper presents a fundamental parametric approach to simultaneous forecasting of a vector of functionally dependent random variables. The motivation behind the proposed method is the following: each random variable at interest is forecasted by its own model and then adjusted in accordance with the functional link. The method incorporates the assumption that models’ errors are independent or weekly dependent. Proposed adjustment is explicit and extremely easy-to-use. Not only does it allow adjusting point forecasts, but also it is possible to adjust the expected variance of errors, that is useful for computation of confidence intervals. Conducted thorough simulation and empirical testing confirms, that proposed method allows to achieve a steady decrease in the mean-squared forecast error for each of predicted variables.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/MAS-210526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70130398","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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