Seasonal adjustment (SA) is a crucial factor in the process of producing official macroeconomic statistics. The most important SA methods, X-13Arima-Seats and Tramo-Seats, are currently included into JDemetra+, a universal open-source environment, which is available on several platforms and operating systems, as a result of adoption of Java programming language for source codes, and Xml metalanguage for the definition of input specifications. This paper focuses on the potentials of RJDemetra, the R library developed for JDemetra+ suite. Its structure and functionalities will be illustrated with several examples, reporting the associated R scripts. In addition, a new operational practices will be suggested, exposing an alternative procedure to enhance interactive time-series updating in SA revision policies step, and also to ensure consistency checking in input system, in order to improve and to speed up the SA estimation process, providing greater security and efficiency. Finally, the interaction between two very different environments such as SAS-IML, and R will be displayed through a new SAS-R procedure available for estimating Quarterly Accounts SA series.
季节调整（SA）是编制官方宏观经济统计数据过程中的一个关键因素。目前，最重要的季节调整方法 X-13Arima-Seats 和 Tramo-Seats 已被纳入 JDemetra+，这是一个通用的开源环境，可在多个平台和操作系统上使用，因为源代码采用了 Java 编程语言，输入规格的定义采用了 Xml 金属语言。本文重点介绍为 JDemetra+ 套件开发的 R 库 RJDemetra 的潜力。它的结构和功能将通过几个例子加以说明，并报告相关的 R 脚本。此外，还将提出一种新的操作方法，揭示在 SA 修订策略步骤中加强交互式时间序列更新的替代程序，并确保输入系统的一致性检查，以改进和加快 SA 估算过程，提供更高的安全性和效率。最后，将通过一个新的 SAS-R 程序来展示 SAS-IML 和 R 这两个截然不同的环境之间的互动，该程序可用于估算季度账户 SA 序列。
{"title":"RJDemetra, a promising tool for the seasonal adjustment of official statistics","authors":"Giancarlo Lutero, Andrea d’Orazio","doi":"10.3233/sji-230047","DOIUrl":"https://doi.org/10.3233/sji-230047","url":null,"abstract":"Seasonal adjustment (SA) is a crucial factor in the process of producing official macroeconomic statistics. The most important SA methods, X-13Arima-Seats and Tramo-Seats, are currently included into JDemetra+, a universal open-source environment, which is available on several platforms and operating systems, as a result of adoption of Java programming language for source codes, and Xml metalanguage for the definition of input specifications. This paper focuses on the potentials of RJDemetra, the R library developed for JDemetra+ suite. Its structure and functionalities will be illustrated with several examples, reporting the associated R scripts. In addition, a new operational practices will be suggested, exposing an alternative procedure to enhance interactive time-series updating in SA revision policies step, and also to ensure consistency checking in input system, in order to improve and to speed up the SA estimation process, providing greater security and efficiency. Finally, the interaction between two very different environments such as SAS-IML, and R will be displayed through a new SAS-R procedure available for estimating Quarterly Accounts SA series.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"25 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139278078","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}
In Hong Kong, merchandise trade statistics are compiled based on the commodity information given on the trade declarations submitted by traders. Due to the complexity of the standardised commodity classification system (i.e. Hong Kong Harmonized System, or HKHS in short), there are often reporting errors, especially in the commodity codes and quantities. With around 20 million declarations received annually, the availability of this big data source motivates us to adopt deep learning techniques to detect the reporting errors. This paper proposes a mechanism consisting of three deep learning models for checking the commodity code, quantity and value, which offers an end-to-end solution to data quality assurance for declarations. The results show that the proposed mechanism could enhance the accuracy of error detection, which is conducive to improving the quality of trade statistics. With the use of text analytics techniques, the mechanism could fully utilise free-text commodity descriptions declared by traders to check the accuracy of the declared information comprehensively. It also overcomes some limitations of the traditional rule-based models. The whole study demonstrates the potential of using deep learning approach in quality assurance of existing statistical systems for official statistics.
{"title":"Anomaly detection in trade declarations using deep learning techniques: A risk-assessment approach to identify misclassification and incorrect valuation","authors":"Benjamin Chan, Ian Ng, Natalie Chung","doi":"10.3233/sji-230081","DOIUrl":"https://doi.org/10.3233/sji-230081","url":null,"abstract":"In Hong Kong, merchandise trade statistics are compiled based on the commodity information given on the trade declarations submitted by traders. Due to the complexity of the standardised commodity classification system (i.e. Hong Kong Harmonized System, or HKHS in short), there are often reporting errors, especially in the commodity codes and quantities. With around 20 million declarations received annually, the availability of this big data source motivates us to adopt deep learning techniques to detect the reporting errors. This paper proposes a mechanism consisting of three deep learning models for checking the commodity code, quantity and value, which offers an end-to-end solution to data quality assurance for declarations. The results show that the proposed mechanism could enhance the accuracy of error detection, which is conducive to improving the quality of trade statistics. With the use of text analytics techniques, the mechanism could fully utilise free-text commodity descriptions declared by traders to check the accuracy of the declared information comprehensively. It also overcomes some limitations of the traditional rule-based models. The whole study demonstrates the potential of using deep learning approach in quality assurance of existing statistical systems for official statistics.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139280162","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}
Laura Antonucci, Antonio Balzanella, Elvira Bruno, Crocetta Crocetta, S. Zio, Lara Fontanella, M. Sanarico, Bruno Scarpa, Rosanna Verde, Giorgio Vittadini
This paper analyses the future prospects of statistics as a profession and how data science will change it. Indeed, according to Hadley Wickham, Chief Scientist at Rstudio, “a data scientist is a useful statistician”, establishing a strong connection between data science and applied statistics. In this direction, the aim is to look to the future by proposing a structural approach to future scenarios. Some possible definitions of data science are then discussed, considering the relationship with statistics as a scientific discipline. The focus then turns to an assessment of the skills required by the labor market for data scientists and the specific characteristics of this profession. Finally, the phases of a data science project are considered, outlining how these can be exploited by a statistician.
{"title":"Data science skills for the next generation of statisticians","authors":"Laura Antonucci, Antonio Balzanella, Elvira Bruno, Crocetta Crocetta, S. Zio, Lara Fontanella, M. Sanarico, Bruno Scarpa, Rosanna Verde, Giorgio Vittadini","doi":"10.3233/sji-230060","DOIUrl":"https://doi.org/10.3233/sji-230060","url":null,"abstract":"This paper analyses the future prospects of statistics as a profession and how data science will change it. Indeed, according to Hadley Wickham, Chief Scientist at Rstudio, “a data scientist is a useful statistician”, establishing a strong connection between data science and applied statistics. In this direction, the aim is to look to the future by proposing a structural approach to future scenarios. Some possible definitions of data science are then discussed, considering the relationship with statistics as a scientific discipline. The focus then turns to an assessment of the skills required by the labor market for data scientists and the specific characteristics of this profession. Finally, the phases of a data science project are considered, outlining how these can be exploited by a statistician.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282262","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}
Martin Karlberg, Vasilis Chasiotis, Photis Stavropoulos, Christine Laaboudi, Mátyás Mészárosa, Despoina-Avgerini Nasiopoulou
Many statistical classifications exist in a statistical ecosystem, where they are interlinked with other classifications. When statistics on the same topic are compiled using different classifications, they need to be transformed in order to become comparable by means of a correspondence table – but sometimes, no correspondence table between the two classifications involved exists. This paper presents the newly developed ‘correspondenceTables’ R package, available on CRAN, which automates much of the ‘mechanical’ work required for developing a correspondence table (thus allowing statistical classification experts to focus on tasks with higher value added). Moreover, the paper presents lessons learned along the way, including unforeseen quality issues with the input data (that required considerable efforts to be successfully tackled), and outlines areas for future improvement.
{"title":"An R package for automatically generating candidate correspondence tables between classifications","authors":"Martin Karlberg, Vasilis Chasiotis, Photis Stavropoulos, Christine Laaboudi, Mátyás Mészárosa, Despoina-Avgerini Nasiopoulou","doi":"10.3233/sji-230039","DOIUrl":"https://doi.org/10.3233/sji-230039","url":null,"abstract":"Many statistical classifications exist in a statistical ecosystem, where they are interlinked with other classifications. When statistics on the same topic are compiled using different classifications, they need to be transformed in order to become comparable by means of a correspondence table – but sometimes, no correspondence table between the two classifications involved exists. This paper presents the newly developed ‘correspondenceTables’ R package, available on CRAN, which automates much of the ‘mechanical’ work required for developing a correspondence table (thus allowing statistical classification experts to focus on tasks with higher value added). Moreover, the paper presents lessons learned along the way, including unforeseen quality issues with the input data (that required considerable efforts to be successfully tackled), and outlines areas for future improvement.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"53 S3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135545334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is an ever-present demand for statistical agencies to improve the timeliness, granularity and cost-efficiency of their official statistics. Our methodology for small area estimation using time-to-event data addresses these demands, as it utilises existing data sources to produce timely estimates at finer levels of geography. We illustrate this methodology with our application to the Australian Building Activity Survey, which has been successfully repurposed to obtain small area estimates of newly completed dwellings with associated uncertainty estimates. The methodology is widely applicable, and we discuss further subject areas where it can be introduced to improve value for users of official statistics.
{"title":"It’s time to build: A small area estimation methodology for time-to-event data","authors":"Nelson J.Y. Chua, Benjamin Y.B. Long","doi":"10.3233/sji-230075","DOIUrl":"https://doi.org/10.3233/sji-230075","url":null,"abstract":"There is an ever-present demand for statistical agencies to improve the timeliness, granularity and cost-efficiency of their official statistics. Our methodology for small area estimation using time-to-event data addresses these demands, as it utilises existing data sources to produce timely estimates at finer levels of geography. We illustrate this methodology with our application to the Australian Building Activity Survey, which has been successfully repurposed to obtain small area estimates of newly completed dwellings with associated uncertainty estimates. The methodology is widely applicable, and we discuss further subject areas where it can be introduced to improve value for users of official statistics.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"92 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139286447","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}
Floris Fonville, P. G. V. D. Heijden, Arno P.J.M. Siebes, D. Oberski
Household financial distress is a complicated problem. Several social problems have been identified as potential risk factors. Conversely, financial distress has also been identified as a risk factor for some of those social problems. Graphical models can be used to better understand the co-dependencies between these problems. In this approach, problem variables are network nodes and the relations between them are represented by weighted edges. Linked administrative data on social service usage by 6,848 households from neighbourhoods with a high proportion of social housing were used to estimate a pairwise Markov random field with binary variables. The main challenges in graph estimation from data are (a) determining which nodes are directly connected by edges and (b) assigning weights to those edges. The eLasso method used in psychological networks addresses both these challenges. In the resulting graph financial distress occupies a central position that connects to both youth related problems as well as adult social problems. The graph approach contributes to a better theoretical understanding of financial distress and it offers valuable insights to social policy makers.
{"title":"Understanding financial distress by using Markov random fields on linked administrative data","authors":"Floris Fonville, P. G. V. D. Heijden, Arno P.J.M. Siebes, D. Oberski","doi":"10.3233/sji-230028","DOIUrl":"https://doi.org/10.3233/sji-230028","url":null,"abstract":"Household financial distress is a complicated problem. Several social problems have been identified as potential risk factors. Conversely, financial distress has also been identified as a risk factor for some of those social problems. Graphical models can be used to better understand the co-dependencies between these problems. In this approach, problem variables are network nodes and the relations between them are represented by weighted edges. Linked administrative data on social service usage by 6,848 households from neighbourhoods with a high proportion of social housing were used to estimate a pairwise Markov random field with binary variables. The main challenges in graph estimation from data are (a) determining which nodes are directly connected by edges and (b) assigning weights to those edges. The eLasso method used in psychological networks addresses both these challenges. In the resulting graph financial distress occupies a central position that connects to both youth related problems as well as adult social problems. The graph approach contributes to a better theoretical understanding of financial distress and it offers valuable insights to social policy makers.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139289416","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}
Identity and ownership are two conceptual pillars used to define Indigenous enterprise. Approaches that use administrative data offer the opportunity to identify Indigenous-owned enterprises without the burden of a survey. It remains unclear, however, if Indigenous-owned enterprises are also likely to self-identify as Indigenous. Thus, in this paper we examine if self-identification as an Indigenous business in Aotearoa New Zealand is driven by Māori ownership. We link information from businesses that had the opportunity to self-identify as Māori in an annual survey with administrative data from Stats NZ’s Integrated Data Infrastructure to calculate their proportion of Māori ownership. Then, we fit models of varying complexity using a Bayesian multilevel approach to predict the probability of self-identification as a Māori business as a function of businesses’ demographic variables and proportion of Indigenous ownership. Using model comparison and out-of-sample predictions we show that Māori ownership is a weak predictor of self-identification as a Māori business. We also show how the probability of self-identification as an Indigenous enterprise changes between regions, sectors, and industries to illustrate the benefits of a quantitative approach to target businesses likely to self-identify as Māori. Predicting the extent to which enterprise owners might choose to self-identify as a Māori business is critical to identifying a robust population of Indigenous businesses and to have better estimates of the Indigenous economy.
身份和所有权是用来界定土著企业的两个概念支柱。使用行政数据的方法为确定土著企业提供了机会，而无需进行调查。然而，土著所有企业是否也有可能自我认定为土著企业，这一点仍不清楚。因此，在本文中，我们研究了在新西兰奥特亚罗瓦，毛利人所有制是否会推动土著企业的自我认同。我们将有机会在年度调查中自我认定为毛利人的企业信息与新西兰统计局综合数据基础设施（Stats NZ's Integrated Data Infrastructure）中的行政数据联系起来，计算出毛利人所有权的比例。然后，我们使用贝叶斯多层次方法拟合了不同复杂程度的模型，以预测自我认定为毛利企业的概率与企业的人口统计学变量和土著所有权比例之间的函数关系。通过模型比较和样本外预测，我们发现毛利人所有权对自我认定为毛利企业的预测作用较弱。我们还展示了不同地区、部门和行业之间自我认定为土著企业的概率是如何变化的，以说明采用定量方法来锁定可能自我认定为毛利人的企业的益处。预测企业主可能选择自我认定为毛利企业的程度，对于确定土著企业的强大人口数量和更好地估计土著经济至关重要。
{"title":"Māori businesses in Aotearoa New Zealand: Modelling Indigenous enterprise using self-identification and ownership","authors":"Loretoa Alba Cervantes, Mikab Jason Paul","doi":"10.3233/sji-230084","DOIUrl":"https://doi.org/10.3233/sji-230084","url":null,"abstract":"Identity and ownership are two conceptual pillars used to define Indigenous enterprise. Approaches that use administrative data offer the opportunity to identify Indigenous-owned enterprises without the burden of a survey. It remains unclear, however, if Indigenous-owned enterprises are also likely to self-identify as Indigenous. Thus, in this paper we examine if self-identification as an Indigenous business in Aotearoa New Zealand is driven by Māori ownership. We link information from businesses that had the opportunity to self-identify as Māori in an annual survey with administrative data from Stats NZ’s Integrated Data Infrastructure to calculate their proportion of Māori ownership. Then, we fit models of varying complexity using a Bayesian multilevel approach to predict the probability of self-identification as a Māori business as a function of businesses’ demographic variables and proportion of Indigenous ownership. Using model comparison and out-of-sample predictions we show that Māori ownership is a weak predictor of self-identification as a Māori business. We also show how the probability of self-identification as an Indigenous enterprise changes between regions, sectors, and industries to illustrate the benefits of a quantitative approach to target businesses likely to self-identify as Māori. Predicting the extent to which enterprise owners might choose to self-identify as a Māori business is critical to identifying a robust population of Indigenous businesses and to have better estimates of the Indigenous economy.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139289675","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}
For many people, their gender is the same as their sex recorded at birth. For some, gender and sex recorded at birth may not align, or they may not fall exclusively into the binary categories of male or female. There is growing recognition of the need to have quality estimates and projections of the population in a context beyond binary sex and gender. However, there is currently little demographic literature on this topic and production of such data is limited. In this paper, we use the demographic equation as a framework to describe the implications of considering sex and gender diversity in the production of population projections. In doing so, we consider implications for base population estimates, births, deaths and migration. We also consider implications of acknowledging gender as a concept that can change over time. We outline existing Australian and international approaches to data collection and address implications for the formation of projection assumptions. We conclude by outlining possible future directions for forming population projections that consider sex and gender beyond the binary.
{"title":"Beyond the binary: Sex and gender diversity in population projections","authors":"Peta Darby, Rachel Jeffreson","doi":"10.3233/sji-230044","DOIUrl":"https://doi.org/10.3233/sji-230044","url":null,"abstract":"For many people, their gender is the same as their sex recorded at birth. For some, gender and sex recorded at birth may not align, or they may not fall exclusively into the binary categories of male or female. There is growing recognition of the need to have quality estimates and projections of the population in a context beyond binary sex and gender. However, there is currently little demographic literature on this topic and production of such data is limited. In this paper, we use the demographic equation as a framework to describe the implications of considering sex and gender diversity in the production of population projections. In doing so, we consider implications for base population estimates, births, deaths and migration. We also consider implications of acknowledging gender as a concept that can change over time. We outline existing Australian and international approaches to data collection and address implications for the formation of projection assumptions. We conclude by outlining possible future directions for forming population projections that consider sex and gender beyond the binary.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824854","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}
Reimund Mink, a former employee from the European Central Bank recently published the book ‘Official Statistics – A Plaything of Politics? On the interaction of Politics, Official Statistics, and Ethical Principles’. The experience from Reimund with government finance statistics in European but also in many non-European countries is a rich source for dedicated reflections and lessons to learn from the roles that official statistics, (can) play in politics. His book informs in great detail on the backgrounds for and details of the role of official statistics and their political use. Ivo Havinga, former Assistant Director Economics Statistics of the United Nations Statistics Division, Department of Economic and Social Affairs, managing partner, and academic, and present senior advisor in statistical information systems for sustainable development, was found willing to interview the author Reimund Mink.
{"title":"Interview with Reimund Mink, about his book ‘Official Statistics – A Plaything of Politics? On the interaction of Politics, Official Statistics, and Ethical Principles’1","authors":"","doi":"10.3233/sji-230057","DOIUrl":"https://doi.org/10.3233/sji-230057","url":null,"abstract":"Reimund Mink, a former employee from the European Central Bank recently published the book ‘Official Statistics – A Plaything of Politics? On the interaction of Politics, Official Statistics, and Ethical Principles’. The experience from Reimund with government finance statistics in European but also in many non-European countries is a rich source for dedicated reflections and lessons to learn from the roles that official statistics, (can) play in politics. His book informs in great detail on the backgrounds for and details of the role of official statistics and their political use. Ivo Havinga, former Assistant Director Economics Statistics of the United Nations Statistics Division, Department of Economic and Social Affairs, managing partner, and academic, and present senior advisor in statistical information systems for sustainable development, was found willing to interview the author Reimund Mink.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SJIAOS Discussion Platform","authors":"","doi":"10.3233/sji-230068","DOIUrl":"https://doi.org/10.3233/sji-230068","url":null,"abstract":"","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824856","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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