Statistical Journal of the IAOS最新文献_第3页

RJDemetra, a promising tool for the seasonal adjustment of official statistics RJDemetra - 用于官方统计数据季节性调整的有前途的工具

Statistical Journal of the IAOS Pub Date : 2023-11-13 DOI: 10.3233/sji-230047

Giancarlo Lutero, Andrea d’Orazio

引用次数: 0

Anomaly detection in trade declarations using deep learning techniques: A risk-assessment approach to identify misclassification and incorrect valuation 利用深度学习技术检测贸易申报中的异常情况：识别错误分类和错误估值的风险评估方法

Statistical Journal of the IAOS Pub Date : 2023-11-11 DOI: 10.3233/sji-230081

Benjamin Chan, Ian Ng, Natalie Chung

{"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}

引用次数: 0

Data science skills for the next generation of statisticians 下一代统计人员的数据科学技能

Statistical Journal of the IAOS Pub Date : 2023-11-09 DOI: 10.3233/sji-230060

Laura Antonucci, Antonio Balzanella, Elvira Bruno, Crocetta Crocetta, S. Zio, Lara Fontanella, M. Sanarico, Bruno Scarpa, Rosanna Verde, Giorgio Vittadini

引用次数: 0

An R package for automatically generating candidate correspondence tables between classifications 一个R包，用于自动生成分类之间的候选对应表

Statistical Journal of the IAOS Pub Date : 2023-11-07 DOI: 10.3233/sji-230039

Martin Karlberg, Vasilis Chasiotis, Photis Stavropoulos, Christine Laaboudi, Mátyás Mészárosa, Despoina-Avgerini Nasiopoulou

引用次数: 0

It’s time to build: A small area estimation methodology for time-to-event data 是时候建设了：时间到事件数据的小面积估算方法

Statistical Journal of the IAOS Pub Date : 2023-11-07 DOI: 10.3233/sji-230075

Nelson J.Y. Chua, Benjamin Y.B. Long

引用次数: 0

Understanding financial distress by using Markov random fields on linked administrative data 利用关联行政数据的马尔可夫随机场了解财务困境

Statistical Journal of the IAOS Pub Date : 2023-11-04 DOI: 10.3233/sji-230028

Floris Fonville, P. G. V. D. Heijden, Arno P.J.M. Siebes, D. Oberski

{"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}

引用次数: 0

Māori businesses in Aotearoa New Zealand: Modelling Indigenous enterprise using self-identification and ownership 新西兰奥特亚罗瓦的毛利企业：利用自我认同和所有权建立土著企业模型

Statistical Journal of the IAOS Pub Date : 2023-11-03 DOI: 10.3233/sji-230084

Loretoa Alba Cervantes, Mikab Jason Paul

{"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}

引用次数: 0

Beyond the binary: Sex and gender diversity in population projections 超越二元:人口预测中的性别和性别多样性

Statistical Journal of the IAOS Pub Date : 2023-09-12 DOI: 10.3233/sji-230044

Peta Darby, Rachel Jeffreson

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Interview with Reimund Mink, about his book ‘Official Statistics – A Plaything of Politics? On the interaction of Politics, Official Statistics, and Ethical Principles’1 采访雷蒙德·明克，关于他的书《官方统计——政治的玩物?》论政治、官方统计和伦理原则的相互作用

Statistical Journal of the IAOS Pub Date : 2023-09-12 DOI: 10.3233/sji-230057

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SJIAOS Discussion Platform sjiao讨论平台

Statistical Journal of the IAOS Pub Date : 2023-09-12 DOI: 10.3233/sji-230068

引用次数: 0