The work presents various techniques of the logistic and multinomial-logit modeling with their modifications. These methods are useful for regression modeling with a binary or categorical outcome, structuring in regression and clustering, singular value decomposition and principal component analysis with positive loadings, and numerous other applications. Particularly, these models are employed in the discrete choice modeling and the best-worst scaling known in applied psychology and socio-economics studies.
{"title":"Logistic and multinomial-logit models: A brief review on their modifications and extensions","authors":"S. Lipovetsky","doi":"10.3233/mas-210543","DOIUrl":"https://doi.org/10.3233/mas-210543","url":null,"abstract":"The work presents various techniques of the logistic and multinomial-logit modeling with their modifications. These methods are useful for regression modeling with a binary or categorical outcome, structuring in regression and clustering, singular value decomposition and principal component analysis with positive loadings, and numerous other applications. Particularly, these models are employed in the discrete choice modeling and the best-worst scaling known in applied psychology and socio-economics studies.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48427838","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}
Marcos Vinicius de Oliveira Peres, R. P. de Oliveira, E. Martinez, J. Achcar
In this paper, we order to evaluate via Monte Carlo simulations the performance of sample properties of the estimates of the estimates for Sushila distribution, introduced by Shanker et al. (2013). We consider estimates obtained by six estimation methods, the known approaches of maximum likelihood, moments and Bayesian method, and other less traditional methods: L-moments, ordinary least-squares and weighted least-squares. As a comparison criterion, the biases and the roots of mean-squared errors were used through nine scenarios with samples ranging from 30 to 300 (every 30rd). In addition, we also considered a simulation and a real data application to illustrate the applicability of the proposed estimators as well as the computation time to get the estimates. In this case, the Bayesian method was also considered. The aim of the study was to find an estimation method to be considered as a better alternative or at least interchangeable with the traditional maximum likelihood method considering small or large sample sizes and with low computational cost.
{"title":"Different inference approaches for the estimators of the sushila distribution","authors":"Marcos Vinicius de Oliveira Peres, R. P. de Oliveira, E. Martinez, J. Achcar","doi":"10.3233/mas-210539","DOIUrl":"https://doi.org/10.3233/mas-210539","url":null,"abstract":"In this paper, we order to evaluate via Monte Carlo simulations the performance of sample properties of the estimates of the estimates for Sushila distribution, introduced by Shanker et al. (2013). We consider estimates obtained by six estimation methods, the known approaches of maximum likelihood, moments and Bayesian method, and other less traditional methods: L-moments, ordinary least-squares and weighted least-squares. As a comparison criterion, the biases and the roots of mean-squared errors were used through nine scenarios with samples ranging from 30 to 300 (every 30rd). In addition, we also considered a simulation and a real data application to illustrate the applicability of the proposed estimators as well as the computation time to get the estimates. In this case, the Bayesian method was also considered. The aim of the study was to find an estimation method to be considered as a better alternative or at least interchangeable with the traditional maximum likelihood method considering small or large sample sizes and with low computational cost.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48241202","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}
COVID-19 pandemic challenges the sustainability of the modern financial system. International central bankers claim that banks are solid. They have accumulated significant capital buffers. Those buffers should be further more augmented by 2027 in line with Basel III reforms. However, disregarding such a consecutive rise in the banking capital adequacy requirements, the US financial authorities undertook an unprecedented step. First time in the country history they lowered the reserve requirement to zero at the end of March 2020. Friedrich von Hayek demonstrated the fragility of the modern fractional reserve banking systems. Together with Ludwig von Mises (von Mises, 1978) he was thus able to predict the Great Depression of 1929 and explain its mechanics much in advance. Thus, we wish to utilize the agent-based modeling technique to extend von Hayek’s rationale to the previously unstudied interaction of capital adequacy and reserve requirement regulation. We find that the full reserve requirement regime even without capital adequacy regulation provides more stable financial environment than the existing one. Rise in capital adequacy adds to modern banking sustainability, but it still preserves the system remarkably fragile compared to the full reserve requirement. We also prove that capital adequacy regulation is redundant when the latter environment is in place. We discuss our findings application to the potential Central Bank Digital Currencies regulation.
{"title":"Agent-based modeling for benchmarking banking regulation regimes: Application for the CBDC","authors":"V. Nechitailo, H. Penikas","doi":"10.3233/mas-210540","DOIUrl":"https://doi.org/10.3233/mas-210540","url":null,"abstract":"COVID-19 pandemic challenges the sustainability of the modern financial system. International central bankers claim that banks are solid. They have accumulated significant capital buffers. Those buffers should be further more augmented by 2027 in line with Basel III reforms. However, disregarding such a consecutive rise in the banking capital adequacy requirements, the US financial authorities undertook an unprecedented step. First time in the country history they lowered the reserve requirement to zero at the end of March 2020. Friedrich von Hayek demonstrated the fragility of the modern fractional reserve banking systems. Together with Ludwig von Mises (von Mises, 1978) he was thus able to predict the Great Depression of 1929 and explain its mechanics much in advance. Thus, we wish to utilize the agent-based modeling technique to extend von Hayek’s rationale to the previously unstudied interaction of capital adequacy and reserve requirement regulation. We find that the full reserve requirement regime even without capital adequacy regulation provides more stable financial environment than the existing one. Rise in capital adequacy adds to modern banking sustainability, but it still preserves the system remarkably fragile compared to the full reserve requirement. We also prove that capital adequacy regulation is redundant when the latter environment is in place. We discuss our findings application to the potential Central Bank Digital Currencies regulation.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41898488","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}
Simple estimators were given in (Kachiashvili & Topchishvili, 2016) for the lower and upper limits of an irregular right-angled triangular distribution together with convenient formulas for removing their bias. We argue here that the smallest observation is not a maximum likelihood estimator (MLE) of the lower limit and we present a procedure for computing an MLE of this parameter. We show that the MLE is strictly smaller than the smallest observation and we give some bounds that are useful in a numerical solution procedure. We also present simulation results to assess the bias and variance of the MLE.
{"title":"Note on “Parameters estimators of irregular right-angled triangular distribution”","authors":"B. Lamond, Luckny Zéphyr","doi":"10.3233/mas-210541","DOIUrl":"https://doi.org/10.3233/mas-210541","url":null,"abstract":"Simple estimators were given in (Kachiashvili & Topchishvili, 2016) for the lower and upper limits of an irregular right-angled triangular distribution together with convenient formulas for removing their bias. We argue here that the smallest observation is not a maximum likelihood estimator (MLE) of the lower limit and we present a procedure for computing an MLE of this parameter. We show that the MLE is strictly smaller than the smallest observation and we give some bounds that are useful in a numerical solution procedure. We also present simulation results to assess the bias and variance of the MLE.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44024702","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}
F. Yanuar, Atika Defita Sari, D. Devianto, A. Zetra
Data on the number of health insurance participants at the subdistrict level is crucial since it is strongly correlated with the availability of health service centers in the areas. This study’s primary purpose is to predict the proportion of health and social security participants of a state-owned company named Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS) in eleven subdistricts in Padang, Indonesia. The direct, ordinary least square, and hierarchical Bayesian for small area estimation (HB-SAE) methods were employed in obtaining the best estimator for the BPJS participants in these small areas. This study found that the HB-SAE method resulted in better estimation than two other methods since it has the smallest standard deviation value. The auxiliary variable age (percentage of individuals more than 50 years old) and the percentage of health complaints have a significant effect on the proportion of the number of BPJS participants based on the HB-SAE method.
分区一级医疗保险参保人数的数据至关重要，因为它与该地区医疗服务中心的可用性密切相关。本研究的主要目的是预测一家名为Badan Penyelenggara Jaminan Sosial Kesehatan（BPJS）的国有公司在印度尼西亚巴东11个街道的健康和社会保障参与者比例。采用直接、普通最小二乘和分层贝叶斯小区域估计（HB-SAE）方法来获得这些小区域中BPJS参与者的最佳估计量。这项研究发现，HB-SAE方法比其他两种方法产生了更好的估计，因为它具有最小的标准偏差值。辅助变量年龄（50岁以上个体的百分比）和健康投诉的百分比对基于HB-SAE方法的BPJS参与者人数的比例有显著影响。
{"title":"Assessment of health and social security agency participants proportion using hierarchical bayesian small area estimation","authors":"F. Yanuar, Atika Defita Sari, D. Devianto, A. Zetra","doi":"10.3233/mas-210538","DOIUrl":"https://doi.org/10.3233/mas-210538","url":null,"abstract":"Data on the number of health insurance participants at the subdistrict level is crucial since it is strongly correlated with the availability of health service centers in the areas. This study’s primary purpose is to predict the proportion of health and social security participants of a state-owned company named Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS) in eleven subdistricts in Padang, Indonesia. The direct, ordinary least square, and hierarchical Bayesian for small area estimation (HB-SAE) methods were employed in obtaining the best estimator for the BPJS participants in these small areas. This study found that the HB-SAE method resulted in better estimation than two other methods since it has the smallest standard deviation value. The auxiliary variable age (percentage of individuals more than 50 years old) and the percentage of health complaints have a significant effect on the proportion of the number of BPJS participants based on the HB-SAE method.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49383934","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}
Surveying human behaviors, especially in demographic, social, medical and public health research, often involves sensitive issues. Posing direct inquiries about stigmatizing or threatening topics may lead survey participants to refuse to answer or to give untruthful responses. Nonresponse and misreporting denote measurement errors that are difficult to treat and are likely to yield unreliable analyses of the surveyed topics. This problem can be mitigated by adopting survey methods that enhance anonymity and respondent cooperation. One possibility is to create a trustful and confidential relationship between the interviewer and the survey participants. Alternatively, it is possible to fully protect privacy by adopting indirect questioning procedures that elicit information without posing sensitive questions directly. We consider both above-mentioned possibilities showing the results of a real study which explores the effectiveness of the randomized response crossed model proposed by Lee et al. (2013) to produce prevalence estimates for two sensitive traits, cannabis use and its legalization.
{"title":"Some evidences and applications of the randomized response crossed model in a survey of sensitive population behaviors","authors":"E. Pelle, P. Perri","doi":"10.3233/mas-210537","DOIUrl":"https://doi.org/10.3233/mas-210537","url":null,"abstract":"Surveying human behaviors, especially in demographic, social, medical and public health research, often involves sensitive issues. Posing direct inquiries about stigmatizing or threatening topics may lead survey participants to refuse to answer or to give untruthful responses. Nonresponse and misreporting denote measurement errors that are difficult to treat and are likely to yield unreliable analyses of the surveyed topics. This problem can be mitigated by adopting survey methods that enhance anonymity and respondent cooperation. One possibility is to create a trustful and confidential relationship between the interviewer and the survey participants. Alternatively, it is possible to fully protect privacy by adopting indirect questioning procedures that elicit information without posing sensitive questions directly. We consider both above-mentioned possibilities showing the results of a real study which explores the effectiveness of the randomized response crossed model proposed by Lee et al. (2013) to produce prevalence estimates for two sensitive traits, cannabis use and its legalization.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45076363","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 paper proposes prediction of the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques. It extends the prediction of the bitcoin return direction using exogenous macroeconomic and financial variables which have been investigated as drivers of bitcoin return. We also use google trends as proxy for investors interest on bitcoin. We consider those variables as predictors for bitcoin return direction. We conduct an in-sample and out-of-sample empirical analysis and achieve a misclassification error around 4% for in-sample evaluation and around 41% in out-of-sample empirical analysis. Ensemble learning trees based outperforms the other methods in both in-sample and out-of-sample analyses.
{"title":"Predicting the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques","authors":"Patrick Rakotomarolahy","doi":"10.3233/mas-210530","DOIUrl":"https://doi.org/10.3233/mas-210530","url":null,"abstract":"This paper proposes prediction of the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques. It extends the prediction of the bitcoin return direction using exogenous macroeconomic and financial variables which have been investigated as drivers of bitcoin return. We also use google trends as proxy for investors interest on bitcoin. We consider those variables as predictors for bitcoin return direction. We conduct an in-sample and out-of-sample empirical analysis and achieve a misclassification error around 4% for in-sample evaluation and around 41% in out-of-sample empirical analysis. Ensemble learning trees based outperforms the other methods in both in-sample and out-of-sample analyses.","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":"43572162","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 work describes a series of techniques designed to obtain regression models resistant to multicollinearity and having some other features needed for meaningful results. These models include enhanced ridge-regressions with several regularization parameters, regressions by data segments and by levels of the dependent variable, latent class models, unitary response, models, orthogonal and equidistant regressions, minimization in Lp-metric, and other criteria and models. All the approaches have been practically implemented in various projects and found useful for decision making in economics, management, marketing research, and other fields requiring data modeling and analysis.
{"title":"Modified ridge and other regularization criteria: A brief review on meaningful regression models","authors":"S. Lipovetsky","doi":"10.3233/mas-210536","DOIUrl":"https://doi.org/10.3233/mas-210536","url":null,"abstract":"The work describes a series of techniques designed to obtain regression models resistant to multicollinearity and having some other features needed for meaningful results. These models include enhanced ridge-regressions with several regularization parameters, regressions by data segments and by levels of the dependent variable, latent class models, unitary response, models, orthogonal and equidistant regressions, minimization in Lp-metric, and other criteria and models. All the approaches have been practically implemented in various projects and found useful for decision making in economics, management, marketing research, and other fields requiring data modeling and analysis.","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":"43071383","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}
G. Cordeiro, Dalson Figueiredo, Lucas Silva, E. M. Ortega, F. Prataviera
The beta regression has been received considerable attention in the last decade because of its applications to proportional data in several fields. We study the variability of coronavirus death rates in the first wave of twenty European countries using the beta regression with two systematic components for the mean and dispersion parameters. We prove empirically that the population density, proportion of urban population, hospital beds per 100 thousand and running time explain the variability of the COVID-19 death rates in the first wave of these countries.
{"title":"Explaining COVID-19 mortality rates in the first wave in Europe","authors":"G. Cordeiro, Dalson Figueiredo, Lucas Silva, E. M. Ortega, F. Prataviera","doi":"10.3233/mas-210534","DOIUrl":"https://doi.org/10.3233/mas-210534","url":null,"abstract":"The beta regression has been received considerable attention in the last decade because of its applications to proportional data in several fields. We study the variability of coronavirus death rates in the first wave of twenty European countries using the beta regression with two systematic components for the mean and dispersion parameters. We prove empirically that the population density, proportion of urban population, hospital beds per 100 thousand and running time explain the variability of the COVID-19 death rates in the first wave of these countries.","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":"47443183","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 aim of this study is to compare the performance of robust estimators in the presence of explanatory variables with Generalized Extreme Value (GEV) distributions in the logistic regression model. Existence of extreme values in the logistic regression model negatively affects the bias and effectiveness of classical Maximum Likelihood (ML) estimators. For this reason, robust estimators that are less sensitive to extreme values have been developed. Random variables with extreme values may be fit in one of specific distributions. In study, the GEV distribution family was examined and five robust estimators were compared for the Fréchet, Gumbel and Weibull distributions. To the simulation results, the CUBIF estimator is prominent according to both bias and efficiency criteria for small samples. In medium and large samples, while the MALLOWS estimator has the minimum bias, the CUBIF estimator has the best efficiency. The same results apply for different contamination ratios and different scale parameter values of the distributions. Simulation findings were supported by a meteorological real data application.
{"title":"Comparison of robust logistic regression estimators for variables with generalized extreme value distributions","authors":"Şaban Kızılarslan, Ceren Camkıran","doi":"10.3233/mas-210531","DOIUrl":"https://doi.org/10.3233/mas-210531","url":null,"abstract":"The aim of this study is to compare the performance of robust estimators in the presence of explanatory variables with Generalized Extreme Value (GEV) distributions in the logistic regression model. Existence of extreme values in the logistic regression model negatively affects the bias and effectiveness of classical Maximum Likelihood (ML) estimators. For this reason, robust estimators that are less sensitive to extreme values have been developed. Random variables with extreme values may be fit in one of specific distributions. In study, the GEV distribution family was examined and five robust estimators were compared for the Fréchet, Gumbel and Weibull distributions. To the simulation results, the CUBIF estimator is prominent according to both bias and efficiency criteria for small samples. In medium and large samples, while the MALLOWS estimator has the minimum bias, the CUBIF estimator has the best efficiency. The same results apply for different contamination ratios and different scale parameter values of the distributions. Simulation findings were supported by a meteorological real data application.","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":"45632020","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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