Pub Date : 2021-10-02DOI: 10.1080/23737484.2021.1979434
V. A. Molaris, K. Triantafyllopoulos, G. Papadakis, P. Economou, S. Bersimis
ABSTRACT Supply chain is a crucial part of the world economy and everyday life. During the outbreak of COVID-19 in 2020, governments across the globe have tried to maintain the supply chain operations in most of the essential goods and services. Dry bulk shipping, a sector characterized by trading versatility and a multitude of cargoes carried, has an important role to play towards that aim. Port restrictions, placed to limit the spread of COVID-19, have created the impression that the market of dry bulk shipping has, to some degree, been affected and become less predictable, as many other transporting services. In this article, this belief is investigated using both state space modeling and neural networks. In particular, using these methods, one-month ahead predictions (during 2020) of the overall freight cost are obtained for three types of vessels (representing the majority of the minor bulks industry). A good forecast performance is observed and this validates the models and the estimation methods proposed. Our results suggest that there is no significant effect of COVID-19 in these types of vessels and their operations. This can provide useful information to shipping managers and policy makers.
{"title":"The Effect of COVID-19 on minor dry bulk shipping: A Bayesian time series and a neural networks approach","authors":"V. A. Molaris, K. Triantafyllopoulos, G. Papadakis, P. Economou, S. Bersimis","doi":"10.1080/23737484.2021.1979434","DOIUrl":"https://doi.org/10.1080/23737484.2021.1979434","url":null,"abstract":"ABSTRACT Supply chain is a crucial part of the world economy and everyday life. During the outbreak of COVID-19 in 2020, governments across the globe have tried to maintain the supply chain operations in most of the essential goods and services. Dry bulk shipping, a sector characterized by trading versatility and a multitude of cargoes carried, has an important role to play towards that aim. Port restrictions, placed to limit the spread of COVID-19, have created the impression that the market of dry bulk shipping has, to some degree, been affected and become less predictable, as many other transporting services. In this article, this belief is investigated using both state space modeling and neural networks. In particular, using these methods, one-month ahead predictions (during 2020) of the overall freight cost are obtained for three types of vessels (representing the majority of the minor bulks industry). A good forecast performance is observed and this validates the models and the estimation methods proposed. Our results suggest that there is no significant effect of COVID-19 in these types of vessels and their operations. This can provide useful information to shipping managers and policy makers.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"4 1","pages":"624 - 638"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88925985","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 : 2021-10-02DOI: 10.1080/23737484.2021.2010622
A. Malyarenko, Hossein Nohrouzian
ABSTRACT The multi-curve extension of the Heath–Jarrow–Morton framework is a popular method for pricing interest rate derivatives and overnight indexed swaps in the post-crisis financial market. That is, the set of forward curves is represented as a solution to an initial boundary value problem for an infinite-dimensional stochastic differential equation. In this paper, we review the post-crisis market proxies for interest rate models. Then, we consider a simple model that belongs to the above framework. This model is driven by a single Wiener process, and we discretize the space of trajectories of its driver by cubature method on Wiener space. After that, we discuss possible methods for numerical solution of the resulting deterministic boundary value problem in the finite-dimensional case. Finally, we compare the obtained numerical solutions of cubature method with the classical Monte Carlo simulation.
{"title":"Evolution of forward curves in the Heath–Jarrow–Morton framework by cubature method on Wiener space","authors":"A. Malyarenko, Hossein Nohrouzian","doi":"10.1080/23737484.2021.2010622","DOIUrl":"https://doi.org/10.1080/23737484.2021.2010622","url":null,"abstract":"ABSTRACT The multi-curve extension of the Heath–Jarrow–Morton framework is a popular method for pricing interest rate derivatives and overnight indexed swaps in the post-crisis financial market. That is, the set of forward curves is represented as a solution to an initial boundary value problem for an infinite-dimensional stochastic differential equation. In this paper, we review the post-crisis market proxies for interest rate models. Then, we consider a simple model that belongs to the above framework. This model is driven by a single Wiener process, and we discretize the space of trajectories of its driver by cubature method on Wiener space. After that, we discuss possible methods for numerical solution of the resulting deterministic boundary value problem in the finite-dimensional case. Finally, we compare the obtained numerical solutions of cubature method with the classical Monte Carlo simulation.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"103 1","pages":"717 - 735"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76660277","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 : 2021-09-24DOI: 10.1080/23737484.2021.1973927
K. Zafeiris, A. Kostaki, Georgios Kontogiannis, Aspasia Tsoni
Abstract Greece is currently characterized by its low fertility levels and a postponement of childbearing toward older ages. Greek Thrace, being a part of Greek territory since the second decade of the 20th century and the study area of this analysis, follows the same national trends, without forgetting the effects of local factors that cause some unique features in its fertility characteristics. Christian and Muslim populations inhabit this area; thus, a multicultural regime prevails. Besides that, Thrace’s ecological and environmental diversity, cultural and social variability, low socioeconomic development, high unemployment, and significant social and economic disparities affect the fertility decisions of the couples solely. This study explores the influence of several factors such as education, occupation, and religion on the fertility outcomes of the population by applying bivariate and multiple regression methods. The results confirm the complex nature of the couples’ fertility decisions under the agency of the cultural and socioeconomic environment. Such findings can outline policy guidelines in addressing population issues in this area. Additionally, they may serve in the interpretation of demographic phenomena of the whole country.
{"title":"Factors affecting fertility in the multicultural environment of Thrace in Northern Greece","authors":"K. Zafeiris, A. Kostaki, Georgios Kontogiannis, Aspasia Tsoni","doi":"10.1080/23737484.2021.1973927","DOIUrl":"https://doi.org/10.1080/23737484.2021.1973927","url":null,"abstract":"Abstract Greece is currently characterized by its low fertility levels and a postponement of childbearing toward older ages. Greek Thrace, being a part of Greek territory since the second decade of the 20th century and the study area of this analysis, follows the same national trends, without forgetting the effects of local factors that cause some unique features in its fertility characteristics. Christian and Muslim populations inhabit this area; thus, a multicultural regime prevails. Besides that, Thrace’s ecological and environmental diversity, cultural and social variability, low socioeconomic development, high unemployment, and significant social and economic disparities affect the fertility decisions of the couples solely. This study explores the influence of several factors such as education, occupation, and religion on the fertility outcomes of the population by applying bivariate and multiple regression methods. The results confirm the complex nature of the couples’ fertility decisions under the agency of the cultural and socioeconomic environment. Such findings can outline policy guidelines in addressing population issues in this area. Additionally, they may serve in the interpretation of demographic phenomena of the whole country.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"10 1 1","pages":"639 - 669"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81241957","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 : 2021-09-17DOI: 10.1080/23737484.2023.2169210
Narayanaswamy Balakrishnan, S. Barui
Abstract In survival analysis, cure models have gained much importance due to rapid advancements in medical sciences. More recently, a subset of cure models, called destructive cure models, have been studied extensively under competing risks scenario wherein initial competing risks undergo a destructive process. In this article, we study destructive cure models by assuming a flexible weighted Poisson distribution (exponentially weighted Poisson, length biased Poisson and negative binomial distributions) for the initial number of competing causes and lifetimes of the susceptible individuals being defined by proportional hazards. The expectation-maximization (EM) algorithm and profile likelihood approach are made use of to estimate the model parameters. An extensive simulation study is carried out under various parameter settings to examine the properties of the models, and accuracy and the robustness of the proposed estimation technique. Effects of model mis-specification on the parameter estimates are also discussed in detail. For further illustration of the proposed methodology, a real-life cutaneous melanoma data set is analyzed.
{"title":"Destructive cure models with proportional hazards lifetimes and associated likelihood inference","authors":"Narayanaswamy Balakrishnan, S. Barui","doi":"10.1080/23737484.2023.2169210","DOIUrl":"https://doi.org/10.1080/23737484.2023.2169210","url":null,"abstract":"Abstract In survival analysis, cure models have gained much importance due to rapid advancements in medical sciences. More recently, a subset of cure models, called destructive cure models, have been studied extensively under competing risks scenario wherein initial competing risks undergo a destructive process. In this article, we study destructive cure models by assuming a flexible weighted Poisson distribution (exponentially weighted Poisson, length biased Poisson and negative binomial distributions) for the initial number of competing causes and lifetimes of the susceptible individuals being defined by proportional hazards. The expectation-maximization (EM) algorithm and profile likelihood approach are made use of to estimate the model parameters. An extensive simulation study is carried out under various parameter settings to examine the properties of the models, and accuracy and the robustness of the proposed estimation technique. Effects of model mis-specification on the parameter estimates are also discussed in detail. For further illustration of the proposed methodology, a real-life cutaneous melanoma data set is analyzed.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"1 1","pages":"16 - 50"},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85377622","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 : 2021-09-15DOI: 10.1080/23737484.2021.1972876
Wing Ki Liu, Mike K. P. So, Amanda M. Y. Chu
Abstract This article proposes a novel multivariate generalized autoregressive conditionally heteroscedastic (GARCH) model that incorporates the modified Cholesky decomposition for a covariance matrix in order to reduce the number of covariance parameters and increase the interpretation power of the model. The modified Cholesky decomposition for covariance matrix reduces the number of covariance parameters to , where p is the dimension of the stocks in the data, and enables us to obtain a regression equation. To account for the nonlinearity in the GARCH model, the parameters in our model are modeled using long short-term memory. The proposed model is compared with DCC model with respect to portfolio optimization and the distances between the actual covariance matrices and predicted covariance matrices. It is found that although the distances may or may not be reduced by our proposed model in different cases presented in this article, our proposed model outperforms the DCC model in terms of mean portfolio returns.
{"title":"Dynamic covariance modeling with artificial neural networks","authors":"Wing Ki Liu, Mike K. P. So, Amanda M. Y. Chu","doi":"10.1080/23737484.2021.1972876","DOIUrl":"https://doi.org/10.1080/23737484.2021.1972876","url":null,"abstract":"Abstract This article proposes a novel multivariate generalized autoregressive conditionally heteroscedastic (GARCH) model that incorporates the modified Cholesky decomposition for a covariance matrix in order to reduce the number of covariance parameters and increase the interpretation power of the model. The modified Cholesky decomposition for covariance matrix reduces the number of covariance parameters to , where p is the dimension of the stocks in the data, and enables us to obtain a regression equation. To account for the nonlinearity in the GARCH model, the parameters in our model are modeled using long short-term memory. The proposed model is compared with DCC model with respect to portfolio optimization and the distances between the actual covariance matrices and predicted covariance matrices. It is found that although the distances may or may not be reduced by our proposed model in different cases presented in this article, our proposed model outperforms the DCC model in terms of mean portfolio returns.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"94 1","pages":"15 - 42"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81243392","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 : 2021-09-14DOI: 10.1080/23737484.2021.1972877
Marilena Furno
Abstract Commodity price stationarity grants good predictability and effective policy intervention. The analysis at the quantiles shows that prices are stationary at the lower but not at the higher quantiles. The non-stationarity of commodity prices is often related to the behavior of the US exchange rate series. Quantile regression estimates show changing coefficients across quantiles. This signals the presence of heteroscedasticity in the series. Stationarity holds throughout once the series are corrected for heteroscedasticity, exchange rate included. This finding weakens the claim that exchange rates cause persistence in commodity prices. The price-exchange rate correlation mirrors their common heteroscedasticity. The correlation declines in the cleaned/homoscedastic series.
{"title":"Commodity prices at the quantiles","authors":"Marilena Furno","doi":"10.1080/23737484.2021.1972877","DOIUrl":"https://doi.org/10.1080/23737484.2021.1972877","url":null,"abstract":"Abstract Commodity price stationarity grants good predictability and effective policy intervention. The analysis at the quantiles shows that prices are stationary at the lower but not at the higher quantiles. The non-stationarity of commodity prices is often related to the behavior of the US exchange rate series. Quantile regression estimates show changing coefficients across quantiles. This signals the presence of heteroscedasticity in the series. Stationarity holds throughout once the series are corrected for heteroscedasticity, exchange rate included. This finding weakens the claim that exchange rates cause persistence in commodity prices. The price-exchange rate correlation mirrors their common heteroscedasticity. The correlation declines in the cleaned/homoscedastic series.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"55 1","pages":"43 - 67"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90897003","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 : 2021-09-14DOI: 10.1080/23737484.2021.1972875
D. Desbois
Abstract The decision to adopt one or another of the sustainable land management alternatives should not be based solely on their respective benefits in terms of climate change mitigation but also based on the performances of the productive systems used by farm holdings, assessing their environmental impacts through the cost of fertilizer resources used. This communication uses the symbolic clustering tools in order to analyze the conditional quantile estimates of the fertilizer costs of yearly crop productions in agriculture, as a replacement proxy for internal soil erosion costs. After recalling the conceptual framework of the estimation of agricultural production costs, we present the empirical data model, the quantile regression approach and the interval principal component analysis clustering tools used to obtain typologies of European countries on the basis of the conditional quantile distributions of fertilizer cost empirical estimates. The comparative analysis of econometric results for yearly crops between European countries illustrates the relevance of the typologies obtained for international comparisons to assess land management alternatives based on their impact on agricultural carbon sequestration in soils.
{"title":"Applying interval PCA and clustering to quantile estimates: empirical distributions of fertilizer cost estimates for yearly crops in European Countries","authors":"D. Desbois","doi":"10.1080/23737484.2021.1972875","DOIUrl":"https://doi.org/10.1080/23737484.2021.1972875","url":null,"abstract":"Abstract The decision to adopt one or another of the sustainable land management alternatives should not be based solely on their respective benefits in terms of climate change mitigation but also based on the performances of the productive systems used by farm holdings, assessing their environmental impacts through the cost of fertilizer resources used. This communication uses the symbolic clustering tools in order to analyze the conditional quantile estimates of the fertilizer costs of yearly crop productions in agriculture, as a replacement proxy for internal soil erosion costs. After recalling the conceptual framework of the estimation of agricultural production costs, we present the empirical data model, the quantile regression approach and the interval principal component analysis clustering tools used to obtain typologies of European countries on the basis of the conditional quantile distributions of fertilizer cost empirical estimates. The comparative analysis of econometric results for yearly crops between European countries illustrates the relevance of the typologies obtained for international comparisons to assess land management alternatives based on their impact on agricultural carbon sequestration in soils.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"25 1","pages":"695 - 716"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88254239","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 : 2021-09-09DOI: 10.1080/23737484.2021.1973926
Rizwan Suliankatchi Abdulkader, V. Deneshkumar, K. Senthamarai Kannan, Vijayakumar Koyilil, Â. Paes, T. Sebastian
Abstract Longitudinal studies are best suited to describe the evolution of particular health conditions over time. In this study, data on the occurrence and transition of cough among post-operative cardiac surgery patients was analyzed using semi-parametric regression models for recurrent events. Cough severity was recorded as no cough, mild cough and severe cough. Transition probability matrix was calculated for the various transitions and across different covariate categories. Also, mean first passage time (MFPT) was calculated using Markov principles and Monte-Carlo simulation. The Andersen-Gill (AG) and Prentice, Williams and Peterson (PWP) semi-parametric regression models were used to test the effect of covariates on the cough transition. Ninety percent of the patients developed cough on the first post-operative day. The probability of transitioning from no cough to severe cough was 8% but the probability of resolution was just 3%. The mean first passage time from no cough to severe cough was about 7.2 (95% CI 6.8–7.5) days and the resolution time was 13.7 (13.0–14.5) days. The MFPT varied widely across the covariate categories. The regression models did not reveal any major significant influences by the measured covariates and the models without covariates were not significantly different from the covariate models. Applying these statistical techniques can serve as effective tools to help medical decision makers to provide better, consistent, efficient and evidence-based healthcare services.
纵向研究最适合描述特定健康状况随时间的演变。在本研究中，使用复发事件的半参数回归模型分析心脏手术后患者咳嗽发生和转移的数据。咳嗽严重程度分为无咳嗽、轻微咳嗽和严重咳嗽。计算了不同协变量类别的各种转移的转移概率矩阵。利用马尔可夫原理和蒙特卡罗模拟计算平均首次通过时间(MFPT)。采用Andersen-Gill (AG)半参数回归模型和Prentice, Williams和Peterson (PWP)半参数回归模型检验协变量对咳嗽转变的影响。90%的患者在术后第一天出现咳嗽。从无咳嗽过渡到严重咳嗽的概率为8%，但缓解的概率仅为3%。从无咳嗽到严重咳嗽的平均首次通过时间约为7.2 (95% CI 6.8-7.5)天，缓解时间为13.7(13.0-14.5)天。MFPT在协变量类别中差异很大。回归模型未发现测量协变量对回归模型有显著影响，无协变量模型与协变量模型无显著差异。应用这些统计技术可以作为有效的工具，帮助医疗决策者提供更好、一致、高效和基于证据的医疗保健服务。
{"title":"An application of Markov chain modeling and semi-parametric regression for recurrent events in health data","authors":"Rizwan Suliankatchi Abdulkader, V. Deneshkumar, K. Senthamarai Kannan, Vijayakumar Koyilil, Â. Paes, T. Sebastian","doi":"10.1080/23737484.2021.1973926","DOIUrl":"https://doi.org/10.1080/23737484.2021.1973926","url":null,"abstract":"Abstract Longitudinal studies are best suited to describe the evolution of particular health conditions over time. In this study, data on the occurrence and transition of cough among post-operative cardiac surgery patients was analyzed using semi-parametric regression models for recurrent events. Cough severity was recorded as no cough, mild cough and severe cough. Transition probability matrix was calculated for the various transitions and across different covariate categories. Also, mean first passage time (MFPT) was calculated using Markov principles and Monte-Carlo simulation. The Andersen-Gill (AG) and Prentice, Williams and Peterson (PWP) semi-parametric regression models were used to test the effect of covariates on the cough transition. Ninety percent of the patients developed cough on the first post-operative day. The probability of transitioning from no cough to severe cough was 8% but the probability of resolution was just 3%. The mean first passage time from no cough to severe cough was about 7.2 (95% CI 6.8–7.5) days and the resolution time was 13.7 (13.0–14.5) days. The MFPT varied widely across the covariate categories. The regression models did not reveal any major significant influences by the measured covariates and the models without covariates were not significantly different from the covariate models. Applying these statistical techniques can serve as effective tools to help medical decision makers to provide better, consistent, efficient and evidence-based healthcare services.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"5 1","pages":"68 - 80"},"PeriodicalIF":0.0,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81121030","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 : 2021-09-08DOI: 10.1080/23737484.2020.1827465
V. Tennekoon, S. Caudill
ABSTRACT Despite the great interest in models of self-selection and models with misclassification, there have been few studies combining the two. Notable exceptions are given by McCarthy, Millimet, and Roy and Shiu. None of these models have been developed in a contingent valuation setting that we are interested in. The goal of this note is to add to this literature by presenting a model for estimating willingness to pay using data collected through a contingent valuation survey. We examine the case of a selectivity model in which the outcome equation is interval censored but the decision indicator is not observed.
{"title":"Estimation of a selectivity model with misclassified selection","authors":"V. Tennekoon, S. Caudill","doi":"10.1080/23737484.2020.1827465","DOIUrl":"https://doi.org/10.1080/23737484.2020.1827465","url":null,"abstract":"ABSTRACT Despite the great interest in models of self-selection and models with misclassification, there have been few studies combining the two. Notable exceptions are given by McCarthy, Millimet, and Roy and Shiu. None of these models have been developed in a contingent valuation setting that we are interested in. The goal of this note is to add to this literature by presenting a model for estimating willingness to pay using data collected through a contingent valuation survey. We examine the case of a selectivity model in which the outcome equation is interval censored but the decision indicator is not observed.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"54 1","pages":"1 - 14"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86073008","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 : 2021-09-04DOI: 10.1080/23737484.2021.1969297
O. Makinde, Modupe Iyabo Omotosho, O. O. Fadugba, Folashade Adeola Bolarinwa, Peace Kanyinsola Ayeni, O. Omotoso, Oluwaseun Tosin Oki
Abstract Meningitis a communicable disease caused by infection and inflammation of the meninges leading to a substantial morbidity and mortality in the whole world. Meningitis epidemic occurs frequently across the African meningitis belts. This article presents analysis of bacterial meningitis incidence and mortality cases over Nigeria. A negative binomial regression model was fitted for number of bacterial meningitis death cases based on reported cases, laboratory confirmed cases and their interaction. Also, a dynamic regression model was fitted for the fatality rates of bacterial meningitis under the assumption that the residuals of linear regression model are auto-correlated. Results from this study show that there were significantly increasing trends in the number of bacterial meningitis reported cases, laboratory confirmed cases and death cases. The number of death cases is significantly affected by number of reported cases, number of laboratory confirmed cases and interaction between them. The decrease in case fatality rate may depend on number of laboratory confirmed individuals and total reported cases. The Ljung–Box test shows that the residuals of the fitted models are not auto-correlated. Observed data are compared with the fitted data from the optimal models. Results show that the optimal models fit the data well. Fold change is estimated based on crude case-fatality risk to investigate whether there is massive underreporting and under-testing of bacterial meningitis cases in Nigeria. There is an evidence of massive underreporting and under-testing of bacterial meningitis cases in Nigeria.
{"title":"Modeling the bacterial meningitis death cases and case fatality rates over Nigeria","authors":"O. Makinde, Modupe Iyabo Omotosho, O. O. Fadugba, Folashade Adeola Bolarinwa, Peace Kanyinsola Ayeni, O. Omotoso, Oluwaseun Tosin Oki","doi":"10.1080/23737484.2021.1969297","DOIUrl":"https://doi.org/10.1080/23737484.2021.1969297","url":null,"abstract":"Abstract Meningitis a communicable disease caused by infection and inflammation of the meninges leading to a substantial morbidity and mortality in the whole world. Meningitis epidemic occurs frequently across the African meningitis belts. This article presents analysis of bacterial meningitis incidence and mortality cases over Nigeria. A negative binomial regression model was fitted for number of bacterial meningitis death cases based on reported cases, laboratory confirmed cases and their interaction. Also, a dynamic regression model was fitted for the fatality rates of bacterial meningitis under the assumption that the residuals of linear regression model are auto-correlated. Results from this study show that there were significantly increasing trends in the number of bacterial meningitis reported cases, laboratory confirmed cases and death cases. The number of death cases is significantly affected by number of reported cases, number of laboratory confirmed cases and interaction between them. The decrease in case fatality rate may depend on number of laboratory confirmed individuals and total reported cases. The Ljung–Box test shows that the residuals of the fitted models are not auto-correlated. Observed data are compared with the fitted data from the optimal models. Results show that the optimal models fit the data well. Fold change is estimated based on crude case-fatality risk to investigate whether there is massive underreporting and under-testing of bacterial meningitis cases in Nigeria. There is an evidence of massive underreporting and under-testing of bacterial meningitis cases in Nigeria.","PeriodicalId":36561,"journal":{"name":"Communications in Statistics Case Studies Data Analysis and Applications","volume":"5 1","pages":"105 - 118"},"PeriodicalIF":0.0,"publicationDate":"2021-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81184005","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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