Australian & New Zealand Journal of Statistics最新文献

{"title":"A Personal History of the tidyverse","authors":"Hadley Wickham","doi":"10.1111/anzs.70045","DOIUrl":"https://doi.org/10.1111/anzs.70045","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, I trace the evolution of the <span>tidyverse</span>, a cohesive ecosystem of <span>R</span> packages for data science. Beginning with early packages I created during my PhD at Iowa State University, I explain how they coalesced into a unified collection with consistent principles. I detail key innovations including tidy data, <span>tibbles</span>, the pipe operator, tidy evaluation and the role of hex stickers in community building. I describe the transition from my individual efforts to a collaborative enterprise supported by a team at <span>Posit</span> and a vibrant global community. I highlight the importance of human-centred design, consistency, composability and inclusivity as our guiding principles. I discuss how our recent priorities have shifted from innovation to maintenance as the ecosystem has matured. Looking to the future, I discuss our current areas of focus including <span>Positron</span> (a new data science IDE), <span>R</span> in production environments and integrating large language models into data science workflows.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting the Age Structure of the Scientific Workforce in Australia","authors":"Rob J. Hyndman,&nbsp;Khuyen Vanh Nguyen","doi":"10.1111/anzs.70043","DOIUrl":"https://doi.org/10.1111/anzs.70043","url":null,"abstract":"<p>Planning for a future workforce requires forecasts of age structure changes to inform policy decisions, particularly related to universities and immigration. We propose a new dynamic statistical model for forecasting the age structure of a workforce. Our approach is inspired by a stochastic model used in population forecasting, replacing births with graduate entry, modelling exits through death and retirement and including a remainder term that captures migration and career changes. Functional data models are used to model age-specific components, while ARIMA models are used for time series components. Simulation is employed to generate forecast distributions, capturing uncertainty from all components. The approach is illustrated using data on Australia's scientific workforce, allowing us to forecast the age distribution of various scientific disciplines for the next ten years. This analysis was central to an Australian Academy of Science initiative examining the capability of Australia's science system and identifying workforce gaps.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Normality: Gain-Probability Analysis for Symmetric Scale Mixture of Normal Distributions","authors":"Tingting Tong,&nbsp;David Trafimow,&nbsp;Xiangfei Chen,&nbsp;Tonghui Wang,&nbsp;S. T. Boris Choy,&nbsp;Ziyuan Wang","doi":"10.1111/anzs.70042","DOIUrl":"https://doi.org/10.1111/anzs.70042","url":null,"abstract":"<p>Gain-Probability (G-P) analysis quantifies the probability that a randomly selected individual from one group scores higher or lower than an individual from another group, by varying magnitudes. While G-P methods have been developed under normality and various skewed distributions, symmetric heavy-tailed settings remain largely unexplored, despite their prevalence in finance, environmental science, and other applied domains. We extend the G-P framework to the broad family of scale mixtures of normal (SMN) distributions, including the Student's t, slash, variance gamma (VG), and Pearson Type VII distributions. Analytical expressions for G-P under SMN are derived for both independent and matched data, and parameter estimation is performed using the expectation maximisation (EM) algorithm. Simulation studies show that the proposed estimators are accurate, robust to heavy tails, and improve with sample size, with performance most sensitive to group separation and noise level. An application to daily returns of US and Chinese equity indices demonstrates how G-P analysis captures distributional tail effects that are overlooked by traditional tests. The results support G-P analysis under SMN as a practical, interpretable alternative to significance testing, enabling robust inference for symmetric heavy-tailed data in diverse applied settings.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting the Age Structure of the Scientific Workforce in Australia","authors":"Rob J. Hyndman,&nbsp;Khuyen Vanh Nguyen","doi":"10.1111/anzs.70043","DOIUrl":"https://doi.org/10.1111/anzs.70043","url":null,"abstract":"<p>Planning for a future workforce requires forecasts of age structure changes to inform policy decisions, particularly related to universities and immigration. We propose a new dynamic statistical model for forecasting the age structure of a workforce. Our approach is inspired by a stochastic model used in population forecasting, replacing births with graduate entry, modelling exits through death and retirement and including a remainder term that captures migration and career changes. Functional data models are used to model age-specific components, while ARIMA models are used for time series components. Simulation is employed to generate forecast distributions, capturing uncertainty from all components. The approach is illustrated using data on Australia's scientific workforce, allowing us to forecast the age distribution of various scientific disciplines for the next ten years. This analysis was central to an Australian Academy of Science initiative examining the capability of Australia's science system and identifying workforce gaps.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Normality: Gain-Probability Analysis for Symmetric Scale Mixture of Normal Distributions","authors":"Tingting Tong,&nbsp;David Trafimow,&nbsp;Xiangfei Chen,&nbsp;Tonghui Wang,&nbsp;S. T. Boris Choy,&nbsp;Ziyuan Wang","doi":"10.1111/anzs.70042","DOIUrl":"https://doi.org/10.1111/anzs.70042","url":null,"abstract":"<p>Gain-Probability (G-P) analysis quantifies the probability that a randomly selected individual from one group scores higher or lower than an individual from another group, by varying magnitudes. While G-P methods have been developed under normality and various skewed distributions, symmetric heavy-tailed settings remain largely unexplored, despite their prevalence in finance, environmental science, and other applied domains. We extend the G-P framework to the broad family of scale mixtures of normal (SMN) distributions, including the Student's t, slash, variance gamma (VG), and Pearson Type VII distributions. Analytical expressions for G-P under SMN are derived for both independent and matched data, and parameter estimation is performed using the expectation maximisation (EM) algorithm. Simulation studies show that the proposed estimators are accurate, robust to heavy tails, and improve with sample size, with performance most sensitive to group separation and noise level. An application to daily returns of US and Chinese equity indices demonstrates how G-P analysis captures distributional tail effects that are overlooked by traditional tests. The results support G-P analysis under SMN as a practical, interpretable alternative to significance testing, enabling robust inference for symmetric heavy-tailed data in diverse applied settings.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"More Than 25 Years of CRAN","authors":"Kurt Hornik,&nbsp;Uwe Ligges","doi":"10.1111/anzs.70039","DOIUrl":"https://doi.org/10.1111/anzs.70039","url":null,"abstract":"<p>We discuss the development of the package repository of CRAN, its design principles and how these are put into practice. We provide insights into how the regular and submission checks are organised and the actions resulting from these checks. We describe some of the challenges of the current approach, including constraints on the available human resources and how these can possibly be mitigated in order to keep CRAN feasible and successful in the future.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"More Than 25 Years of CRAN","authors":"Kurt Hornik,&nbsp;Uwe Ligges","doi":"10.1111/anzs.70039","DOIUrl":"https://doi.org/10.1111/anzs.70039","url":null,"abstract":"<p>We discuss the development of the package repository of CRAN, its design principles and how these are put into practice. We provide insights into how the regular and submission checks are organised and the actions resulting from these checks. We describe some of the challenges of the current approach, including constraints on the available human resources and how these can possibly be mitigated in order to keep CRAN feasible and successful in the future.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating Synthetic Data With Locally Estimated Distributions for Disclosure Control","authors":"Ali Furkan Kalay","doi":"10.1111/anzs.70032","DOIUrl":"https://doi.org/10.1111/anzs.70032","url":null,"abstract":"<div>\u0000 \u0000 <p>Synthetic data methods enable sharing sensitive datasets while safeguarding privacy. This paper introduces the Local Resampler (LR) framework, implemented in the open-source Python package <span>synloc</span>, which offers two neighbourhood-selection strategies for synthetic data generation: <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>-nearest neighbour (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>NN-LR) and constrained K-Means clustering (C-LR). The central theoretical contribution is the formal demonstration that <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>NN-LR systematically underrepresents outliers through an inherent statistical property, providing algorithmic disclosure control without manual intervention. Comprehensive empirical evaluations demonstrate that while <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>NN-LR achieves stronger privacy protection, C-LR excels at preserving marginal distributions and correlational structures. Simulations across multimodal, non-convex-support and skewed distributions show that LR methods match or exceed in performance established synthesisers while requiring minimal computational demand. Both LR variants offer regulatory compliance through parametric modelling aligned with the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>-anonymity framework. The explicit utility–privacy trade-off, controlled through LR hyperparameters, enables users to tailor synthetic data generation to specific disclosure control requirements.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Efficiency of Bivariate Fay-Herriot Small Area Estimators","authors":"Mossamet Kamrun Nesa,&nbsp;Robert Graham Clark,&nbsp;Carole L. Birrell","doi":"10.1111/anzs.70033","DOIUrl":"https://doi.org/10.1111/anzs.70033","url":null,"abstract":"<div>\u0000 \u0000 <p>Fay–Herriot (FH) estimators are widely used to produce small-area statistics when only area-level aggregate data are available. This paper investigates the conditions under which bivariate FH models give useful reductions in approximate prediction mean squared error (APMSE) compared to separate univariate models. The APMSE is shown to be equal under these two approaches if the sampling errors and the area-level random effects have proportional variance-covariance matrices, even if there is high correlation between the two variables of interest. The ratio of APMSEs is calculated numerically for a range of settings, and this numerical study is summarised using a novel regression tree approach. Univariate and bivariate estimators are compared using data on 30 indicators from the 2011–2012 New Zealand Health Survey, with MSEs estimated by a parametric bootstrap approach. The results suggest that bivariate modelling can be worthwhile, but only for a minority of cases.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Efficiency of Bivariate Fay-Herriot Small Area Estimators","authors":"Mossamet Kamrun Nesa,&nbsp;Robert Graham Clark,&nbsp;Carole L. Birrell","doi":"10.1111/anzs.70033","DOIUrl":"https://doi.org/10.1111/anzs.70033","url":null,"abstract":"<div>\u0000 \u0000 <p>Fay–Herriot (FH) estimators are widely used to produce small-area statistics when only area-level aggregate data are available. This paper investigates the conditions under which bivariate FH models give useful reductions in approximate prediction mean squared error (APMSE) compared to separate univariate models. The APMSE is shown to be equal under these two approaches if the sampling errors and the area-level random effects have proportional variance-covariance matrices, even if there is high correlation between the two variables of interest. The ratio of APMSEs is calculated numerically for a range of settings, and this numerical study is summarised using a novel regression tree approach. Univariate and bivariate estimators are compared using data on 30 indicators from the 2011–2012 New Zealand Health Survey, with MSEs estimated by a parametric bootstrap approach. The results suggest that bivariate modelling can be worthwhile, but only for a minority of cases.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}