Pub Date : 2022-10-03DOI: 10.1080/09720510.2022.2130578
T. Durga Prasad, Astha Sharma, Manish Mittal, M. Govardhan Reddy, B. Giri Babu
Abstract This paper examines and portrays the impact of direct and indirect tax revenue on economic growth in India. Numerous studies were conducted earlier to study taxation from various perspectives, but very few have discussed the impact of taxation on economic growth. Various tax revenue reports from 1990 to 2021 were collected to conduct the present research. An ARDL inbound and outbound tests were applied to continue the investigation further. The study identifies tax variables that have a positive relationship with economic performance. Finally, an in-depth analysis was conducted between variables with the help of ARDL long-run trend analysis. The outcome reveals that direct and indirect tax growth rates positively impact GDP growth. It was suggested that government should focus on corporate tax policies, and more incentives for upcoming and existing entrepreneurs must be provided. The present study is limited only to tax and GDP revenue; it does not focus on economic or corporate policies. However, this research forms a base for further study in taxation, which will support designing policies for economic growth. It is known that the Indian government has introduced nation-one tax policy and implemented GST. Hence, further study can be conducted on the dynamics of GST and its impact on GDP or revenue growth rates.
{"title":"Economic growth of India and the impact of direct and indirect tax : An ARDL approach","authors":"T. Durga Prasad, Astha Sharma, Manish Mittal, M. Govardhan Reddy, B. Giri Babu","doi":"10.1080/09720510.2022.2130578","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130578","url":null,"abstract":"Abstract This paper examines and portrays the impact of direct and indirect tax revenue on economic growth in India. Numerous studies were conducted earlier to study taxation from various perspectives, but very few have discussed the impact of taxation on economic growth. Various tax revenue reports from 1990 to 2021 were collected to conduct the present research. An ARDL inbound and outbound tests were applied to continue the investigation further. The study identifies tax variables that have a positive relationship with economic performance. Finally, an in-depth analysis was conducted between variables with the help of ARDL long-run trend analysis. The outcome reveals that direct and indirect tax growth rates positively impact GDP growth. It was suggested that government should focus on corporate tax policies, and more incentives for upcoming and existing entrepreneurs must be provided. The present study is limited only to tax and GDP revenue; it does not focus on economic or corporate policies. However, this research forms a base for further study in taxation, which will support designing policies for economic growth. It is known that the Indian government has introduced nation-one tax policy and implemented GST. Hence, further study can be conducted on the dynamics of GST and its impact on GDP or revenue growth rates.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531039","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}
Pub Date : 2022-10-03DOI: 10.1080/09720510.2022.2130571
Divya Jyot Kaur, N. Saraswat
Abstract In recent times learning ubiquity and autonomy have emerged as prominent e-learning models as they equip learners with the access of resources at any place and any time which implies taking charge of one’s learning as per the need and purpose. The present study takes stock of ubiquitous (UL) and autonomous learning (LA) as influencing factors for assessing students’ willingness to adopt (WA) LMS for enhancing L2 proficiency. The target population for this study consisted 302 professional students. Data is analyzed using SPSS ver.21. Regression model indicates that both LA (p=.000, β=.289) and UL (p=.000, β=.461) significantly contribute to students’ adoption of LMS. Independent sample t-test results indicate that no significant variation is found in test performance between male and female students. The findings of the proposed model will prove beneficial to assess the reception of LMS among students and will enable the educational institutions to concentrate on LMS’s effective implementation so that they can invest in e-learning technology wisely.
{"title":"Statistical analysis of learners’ ubiquity and autonomy: Sustaining the development of learning management system (LMS) for L2 proficiency","authors":"Divya Jyot Kaur, N. Saraswat","doi":"10.1080/09720510.2022.2130571","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130571","url":null,"abstract":"Abstract In recent times learning ubiquity and autonomy have emerged as prominent e-learning models as they equip learners with the access of resources at any place and any time which implies taking charge of one’s learning as per the need and purpose. The present study takes stock of ubiquitous (UL) and autonomous learning (LA) as influencing factors for assessing students’ willingness to adopt (WA) LMS for enhancing L2 proficiency. The target population for this study consisted 302 professional students. Data is analyzed using SPSS ver.21. Regression model indicates that both LA (p=.000, β=.289) and UL (p=.000, β=.461) significantly contribute to students’ adoption of LMS. Independent sample t-test results indicate that no significant variation is found in test performance between male and female students. The findings of the proposed model will prove beneficial to assess the reception of LMS among students and will enable the educational institutions to concentrate on LMS’s effective implementation so that they can invest in e-learning technology wisely.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133178283","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}
Pub Date : 2022-10-03DOI: 10.1080/09720510.2022.2130563
R. Maheshwari, V. Kapoor
Abstract With the advent of technology and advancements in storage capacities, one can get the historical data for any of the major stock markets of the world. In the last few decades, the Indian stock market grew very fast. The investment capacity and frequency for Indian stock markets increased drastically recently. More and more people are investing in stock markets and mutual funds nowadays and as a result of that there have been numerous attempts to forecast the stock market index so as to gain maximum profit. The proposed work forecasts the opening value of the National Stock Exchange index using the ARIMA model.
{"title":"Predicting the NSE stock index trends considering global financial variables and ARIMA model","authors":"R. Maheshwari, V. Kapoor","doi":"10.1080/09720510.2022.2130563","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130563","url":null,"abstract":"Abstract With the advent of technology and advancements in storage capacities, one can get the historical data for any of the major stock markets of the world. In the last few decades, the Indian stock market grew very fast. The investment capacity and frequency for Indian stock markets increased drastically recently. More and more people are investing in stock markets and mutual funds nowadays and as a result of that there have been numerous attempts to forecast the stock market index so as to gain maximum profit. The proposed work forecasts the opening value of the National Stock Exchange index using the ARIMA model.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115943625","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}
Pub Date : 2022-10-03DOI: 10.1080/09720510.2022.2130576
R. Nanda, Amita Sharma, Pooja Choraria, A. Pareek, N. Tiwari, Anubha Jain
Abstract With the proliferation of data in cloud-based systems, the performance of the data retrieval process from the database management system is becoming indispensable. Caching is one of the techniques to retrieve the data faster. It reduces the number of database accesses for similar queries, which in turn, reduces the processing time. It also facilitates in reducing the load on database servers, which results in the reduction of the overall response time. This paper is based on our caching framework for NOSQL datastores, which seeks to speed up the processing of many requests. The frequently used queries, which are expensive to reevaluate, are cached by this framework on top of a column-based datastore. In order to speed up query processing, certain queries are cached. In this study, query processing times without the influence of the database or system cache are used to evaluate the framework’s performance.
{"title":"Statistical analysis of query processing time in cache-based cloud database systems","authors":"R. Nanda, Amita Sharma, Pooja Choraria, A. Pareek, N. Tiwari, Anubha Jain","doi":"10.1080/09720510.2022.2130576","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130576","url":null,"abstract":"Abstract With the proliferation of data in cloud-based systems, the performance of the data retrieval process from the database management system is becoming indispensable. Caching is one of the techniques to retrieve the data faster. It reduces the number of database accesses for similar queries, which in turn, reduces the processing time. It also facilitates in reducing the load on database servers, which results in the reduction of the overall response time. This paper is based on our caching framework for NOSQL datastores, which seeks to speed up the processing of many requests. The frequently used queries, which are expensive to reevaluate, are cached by this framework on top of a column-based datastore. In order to speed up query processing, certain queries are cached. In this study, query processing times without the influence of the database or system cache are used to evaluate the framework’s performance.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129494106","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}
Pub Date : 2022-10-03DOI: 10.1080/09720510.2022.2130569
Nishu Gupta, Ity Patni, S. Choubey, Arpita Sharma
Abstract The study shows the effect of Macro-Economic Factors such as Interest Rate (INT) and Gross Domestic Product (GDP), Inflation (INF), Financial Ratios such as EPS, NPM, ROC, DE, ATR, CR, ITR, EV on Power and Energy stock prices. The period of the study is from 2011-2021. S&P BSE Energy Index consists of 26 companies, but the study has been conducted on 21 companies due to the unavailability of data in the continuous form. While in S & P BSE Power Index, 14 companies are listed, out of which data of 4 companies was not available in continuous form, thus the final power index sample is locked with 10 companies. The results are important for investors to select the stocks of Power and Energy sectors by considering the resultant effective factors of the study. The Random Effect (RE) Model of Panel Regression was applied which shows that Inflation, Interest Rate, GDP, EPS, ATR and CR have significant effect on Stock Prices. The Research Paper is based on the stocks of Power and energy sector which empowers an investor in analyzing the stock by considering these ratios and accordingly they may include the stock in their portfolio and diversify the risk. The study hasn’t covered other economic factors and financial ratios. Other factors and ratios may be considered to know the association with stock return.
{"title":"Economic and financial ratios: Relevant for stock selection in power and energy sector?","authors":"Nishu Gupta, Ity Patni, S. Choubey, Arpita Sharma","doi":"10.1080/09720510.2022.2130569","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130569","url":null,"abstract":"Abstract The study shows the effect of Macro-Economic Factors such as Interest Rate (INT) and Gross Domestic Product (GDP), Inflation (INF), Financial Ratios such as EPS, NPM, ROC, DE, ATR, CR, ITR, EV on Power and Energy stock prices. The period of the study is from 2011-2021. S&P BSE Energy Index consists of 26 companies, but the study has been conducted on 21 companies due to the unavailability of data in the continuous form. While in S & P BSE Power Index, 14 companies are listed, out of which data of 4 companies was not available in continuous form, thus the final power index sample is locked with 10 companies. The results are important for investors to select the stocks of Power and Energy sectors by considering the resultant effective factors of the study. The Random Effect (RE) Model of Panel Regression was applied which shows that Inflation, Interest Rate, GDP, EPS, ATR and CR have significant effect on Stock Prices. The Research Paper is based on the stocks of Power and energy sector which empowers an investor in analyzing the stock by considering these ratios and accordingly they may include the stock in their portfolio and diversify the risk. The study hasn’t covered other economic factors and financial ratios. Other factors and ratios may be considered to know the association with stock return.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125829510","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}
Pub Date : 2022-10-03DOI: 10.1080/09720510.2022.2130567
A. K. Rout, A. Sethy, Soumyabrata Nayak
Abstract The stock market makes a mention of public markets that contains buying, issuing, and selling shares which trade on a stock exchange. The aim of stock market is to confer capital to companies that they can utilize for funding and spreading their businesses also to serve investors. But it is elusive to prepare right decision for the companies in particular trading of stocks because of dynamic and intermediate nature of the share price. The charge of funding and commercial enterprise possibilities within the inventory market can boom if an efficient algorithm could be developed to predict the price of an individual stock. There are many deep learning algorithms available in which Extreme learning machine (ELM) is one of the most efficient technique for training single layer feed-forward neural networks (SLFNs). Integrating ELM with auto encoder has gotten another viewpoint for extracting features using unlabeled data. This paper attempts to focus on predicting stock market five days ahead by using a new variant of deep neural network i.e multilayer extreme learning machine with auto encoder (MLELMAE). This model is applied on YES, SBI, and BOI datasets there by the performance of the proposed model is measured and compared with other Deep Learning (DL) techniques like Radial Basis Function Neural Network (RBF), Back Propagation Neural Network (BPNN), and ELM in terms of Mean Absolute error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results also show that the proposed model outperforms best over other DL techniques.
{"title":"Adaptive MLELM-AE model for efficient prediction of stock market data","authors":"A. K. Rout, A. Sethy, Soumyabrata Nayak","doi":"10.1080/09720510.2022.2130567","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130567","url":null,"abstract":"Abstract The stock market makes a mention of public markets that contains buying, issuing, and selling shares which trade on a stock exchange. The aim of stock market is to confer capital to companies that they can utilize for funding and spreading their businesses also to serve investors. But it is elusive to prepare right decision for the companies in particular trading of stocks because of dynamic and intermediate nature of the share price. The charge of funding and commercial enterprise possibilities within the inventory market can boom if an efficient algorithm could be developed to predict the price of an individual stock. There are many deep learning algorithms available in which Extreme learning machine (ELM) is one of the most efficient technique for training single layer feed-forward neural networks (SLFNs). Integrating ELM with auto encoder has gotten another viewpoint for extracting features using unlabeled data. This paper attempts to focus on predicting stock market five days ahead by using a new variant of deep neural network i.e multilayer extreme learning machine with auto encoder (MLELMAE). This model is applied on YES, SBI, and BOI datasets there by the performance of the proposed model is measured and compared with other Deep Learning (DL) techniques like Radial Basis Function Neural Network (RBF), Back Propagation Neural Network (BPNN), and ELM in terms of Mean Absolute error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results also show that the proposed model outperforms best over other DL techniques.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134033079","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}
Pub Date : 2022-10-03DOI: 10.1080/09720510.2022.2130579
Rajesh Sannegadu, Bhoomika Batra, Thanika Devi Juwaheer, S. Pudaruth
Abstract This study aims at assessing the impact of perceived value on the adoption intention of mobile payment in Mauritius. The study empirically investigates 255 Mauritius, mobile payment users, using the survey. Structural equation modeling (SEM) using Smart PLS software has been used to validate the variables and their relationships. Results reveal that social value and utilitarian value positively and significantly influence behavioural value. Conversely, enjoyment value is inversely proportionate and insignificantly influences the behavioural value. Overall, this study projects consumer’s perceptions of value as an influential factor in the adoption of mobile payment in the context of small island economies. This study contributes to the ongoing debate on mobile payment usage and its acceptance in small island states. This research is pertinent and timely as the Mauritian government is working towards the transformation of the island into a digital economy. Further, the findings are important to digital marketers and other professionals who are developing effective marketing tactics to reinforce value to encourage mobile payment.
{"title":"Impact of perceived value on the adoption of contactless mobile payments in small island developing states (SIDS): A study on emerging payments systems from Mauritius","authors":"Rajesh Sannegadu, Bhoomika Batra, Thanika Devi Juwaheer, S. Pudaruth","doi":"10.1080/09720510.2022.2130579","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130579","url":null,"abstract":"Abstract This study aims at assessing the impact of perceived value on the adoption intention of mobile payment in Mauritius. The study empirically investigates 255 Mauritius, mobile payment users, using the survey. Structural equation modeling (SEM) using Smart PLS software has been used to validate the variables and their relationships. Results reveal that social value and utilitarian value positively and significantly influence behavioural value. Conversely, enjoyment value is inversely proportionate and insignificantly influences the behavioural value. Overall, this study projects consumer’s perceptions of value as an influential factor in the adoption of mobile payment in the context of small island economies. This study contributes to the ongoing debate on mobile payment usage and its acceptance in small island states. This research is pertinent and timely as the Mauritian government is working towards the transformation of the island into a digital economy. Further, the findings are important to digital marketers and other professionals who are developing effective marketing tactics to reinforce value to encourage mobile payment.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134018436","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}
Pub Date : 2022-10-03DOI: 10.1080/09720510.2022.2130572
Archana Sasi, Thiruselvan Subramanian
Abstract One of the most challenging issues during the pandemic is managing uncertainties in demand, customer behavior, and market trends. Such instability and unpredictability resulted in numerous cases of excess stock when demand declined or a shortage of commodities when demand for certain goods increased significantly. The research presented in this paper contributes to modelling and forecasting rice sales demand in a Fair Price Shop (FPS) in Kerala, India by employing a time series technique. Our research shows how past demand data can be used to estimate future demand and how these forecasts impact the Public Distribution System (PDS). Our study employs Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES) techniques to develop future prediction models that significantly increase the efficiency and accuracy of demand and inventory forecasting. The forecast models generated from past data are verified and validated in the real case application using the Mean Absolute Percentage Error (MAPE) that helps to forecast the demand of inventory required in FPS. The proposed ARIMA and DES outperform the forecasts made by the empirical model, with ARIMA doing better in terms of future forecasts.
{"title":"Comparative analysis of ARIMA and double exponential smoothing for forecasting rice sales in fair price shop","authors":"Archana Sasi, Thiruselvan Subramanian","doi":"10.1080/09720510.2022.2130572","DOIUrl":"https://doi.org/10.1080/09720510.2022.2130572","url":null,"abstract":"Abstract One of the most challenging issues during the pandemic is managing uncertainties in demand, customer behavior, and market trends. Such instability and unpredictability resulted in numerous cases of excess stock when demand declined or a shortage of commodities when demand for certain goods increased significantly. The research presented in this paper contributes to modelling and forecasting rice sales demand in a Fair Price Shop (FPS) in Kerala, India by employing a time series technique. Our research shows how past demand data can be used to estimate future demand and how these forecasts impact the Public Distribution System (PDS). Our study employs Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES) techniques to develop future prediction models that significantly increase the efficiency and accuracy of demand and inventory forecasting. The forecast models generated from past data are verified and validated in the real case application using the Mean Absolute Percentage Error (MAPE) that helps to forecast the demand of inventory required in FPS. The proposed ARIMA and DES outperform the forecasts made by the empirical model, with ARIMA doing better in terms of future forecasts.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126107448","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}
Pub Date : 2022-09-25DOI: 10.1080/09720510.2022.2060915
Behzad Mansouri, Sami Atiyah Sayyid Al-Farttosi, H. Mombeni, R. Chinipardaz
Abstract COVID-19 disease has aggressively affected all aspects of human life since late 2019. Hospital staff have been under unprecedented pressure from a large number of patients, and in some countries, the lack of space for patients at the height of the epidemic has reached a point where hospitals do not have the capacity to accept new patients. Therefore, studying the duration of treatment of COVID-19 patients is very important in managing the ability of treatment staff and hospital facilities. In this paper, the length of hospitalization of all COVID-19 patients in Al-Sadr General Hospital in Al-Amarah, Iraq, is statistically studied from March 2020 to April 2021. The cumulative distribution function (cdf) of the patients’ treatment duration is estimated using Birnbaum-Saunders (B-S) kernel estimator. This estimate allows us to estimate the probability of a patient’s stay in the hospital for a specified period of time. In this paper, we obtain an asymptotic confidence interval for the B-S kernel estimator. However, due to the dependence of the obtained confidence interval on the unknown cdf and its derivatives, we propose a bootstrap algorithm to calculate the confidence interval and use it for the length of hospital stay of COVID-19 patients.
{"title":"Statistical analysis and estimation of the cumulative distribution function of COVID-19 cure duration in Iraq","authors":"Behzad Mansouri, Sami Atiyah Sayyid Al-Farttosi, H. Mombeni, R. Chinipardaz","doi":"10.1080/09720510.2022.2060915","DOIUrl":"https://doi.org/10.1080/09720510.2022.2060915","url":null,"abstract":"Abstract COVID-19 disease has aggressively affected all aspects of human life since late 2019. Hospital staff have been under unprecedented pressure from a large number of patients, and in some countries, the lack of space for patients at the height of the epidemic has reached a point where hospitals do not have the capacity to accept new patients. Therefore, studying the duration of treatment of COVID-19 patients is very important in managing the ability of treatment staff and hospital facilities. In this paper, the length of hospitalization of all COVID-19 patients in Al-Sadr General Hospital in Al-Amarah, Iraq, is statistically studied from March 2020 to April 2021. The cumulative distribution function (cdf) of the patients’ treatment duration is estimated using Birnbaum-Saunders (B-S) kernel estimator. This estimate allows us to estimate the probability of a patient’s stay in the hospital for a specified period of time. In this paper, we obtain an asymptotic confidence interval for the B-S kernel estimator. However, due to the dependence of the obtained confidence interval on the unknown cdf and its derivatives, we propose a bootstrap algorithm to calculate the confidence interval and use it for the length of hospital stay of COVID-19 patients.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115251447","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}
Pub Date : 2022-09-12DOI: 10.1080/09720510.2022.2060615
M. I. Khan
Abstract The basic concept of ordered random variables is how the ordering is being done for a model. Dual generalized order statistics (dgos) is one of them. The powered inverse Rayleigh distribution is introduced in this research investigation to analyze the moments’ properties based on dgos. This introduced distribution was coined by Nashaat [1]. The characterization findings based on the recurrence relations and conditional moments are also derived. In addition, some statistical calculations are performed.
{"title":"Dual generalized order statistics with moments properties using powered inverse Rayleigh distribution","authors":"M. I. Khan","doi":"10.1080/09720510.2022.2060615","DOIUrl":"https://doi.org/10.1080/09720510.2022.2060615","url":null,"abstract":"Abstract The basic concept of ordered random variables is how the ordering is being done for a model. Dual generalized order statistics (dgos) is one of them. The powered inverse Rayleigh distribution is introduced in this research investigation to analyze the moments’ properties based on dgos. This introduced distribution was coined by Nashaat [1]. The characterization findings based on the recurrence relations and conditional moments are also derived. In addition, some statistical calculations are performed.","PeriodicalId":270059,"journal":{"name":"Journal of Statistics and Management Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124243663","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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