Australian & New Zealand Journal of Statistics最新文献

{"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":"147565603","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":"Asymptotic Normality of Generalised Edge Frequency Polygon Estimator for Dependent Data","authors":"Yan Wang,&nbsp;Xuejun Wang,&nbsp;Aiting Shen","doi":"10.1111/anzs.70036","DOIUrl":"https://doi.org/10.1111/anzs.70036","url":null,"abstract":"<div>\u0000 \u0000 <p>Density function estimation is a cornerstone of statistical analysis. This paper focuses on the generalised edge frequency polygon estimator, establishing its asymptotic normality for identically distributed <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math>-mixing random variables. This finding complements the asymptotic theory outlined by Dong and Zheng (2001. Generalized edge frequency polygon for density estimation. Statistics and Probability Letters, 55, 137–145). Theoretical results are substantiated through simulations that assess finite-sample performance and an analysis of a real-world dataset.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288303","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":"R (and Dialects) versus Python for Data Science","authors":"Norman Matloff","doi":"10.1111/anzs.70041","DOIUrl":"10.1111/anzs.70041","url":null,"abstract":"<div>\u0000 \u0000 <p><span>R</span> and <span>Python</span> are the two dominant language tools for data science today. Which one is better, and in what senses? This paper explores such questions, in terms of aspects such as learning curve, clarity of expression, coding philosophy, high-performance computing capability and so on. Also, the article treats base-<span>R</span> and the <span>tidyverse</span> as two separate ‘dialects’ of <span>R</span>, so that the above comparisons are in many cases tripartite in nature.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315433","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":"Asymptotic Normality of Generalised Edge Frequency Polygon Estimator for Dependent Data","authors":"Yan Wang,&nbsp;Xuejun Wang,&nbsp;Aiting Shen","doi":"10.1111/anzs.70036","DOIUrl":"https://doi.org/10.1111/anzs.70036","url":null,"abstract":"<div>\u0000 \u0000 <p>Density function estimation is a cornerstone of statistical analysis. This paper focuses on the generalised edge frequency polygon estimator, establishing its asymptotic normality for identically distributed <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math>-mixing random variables. This finding complements the asymptotic theory outlined by Dong and Zheng (2001. Generalized edge frequency polygon for density estimation. Statistics and Probability Letters, 55, 137–145). Theoretical results are substantiated through simulations that assess finite-sample performance and an analysis of a real-world dataset.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288335","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":"Model Averaging of Partially Linear Multinomial Logit Model for Multi-Categorical Data","authors":"Jialei Liu,&nbsp;Jing Lv","doi":"10.1111/anzs.70040","DOIUrl":"10.1111/anzs.70040","url":null,"abstract":"<div>\u0000 \u0000 <p>Model averaging for categorical response variables has gained a lot of attention in recent years. To further improve the prediction accuracy, we present a partially linear multinomial logit model averaging (PLMLMA) technique. Our candidate models are built by selecting each continuous covariate in turn as the index variable of the non-parametric function, thus avoiding both the artificial selection of index variables and the curse of dimensionality in estimation. The model averaging weights are determined by minimising the Kullback-Leibler (KL) loss. We demonstrate asymptotic optimality by showing that the KL loss between the true model and the model where the log-odds ratio is estimated by the ‘working’ log-odds ratio is asymptotically equivalent to that of the best but impractical model averaging estimator. Furthermore, we establish the convergence rate of the weight estimator without assuming that the true model is included among the candidate models. The superior performance of the proposed method is evidenced by lower mean squared error (MSE) and higher hit rate (HR) in simulations, outperforming various competitors. We also apply our method to wheat variety classification to illustrate the merits of PLMLMA.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217042","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":"A Random-Effects Approach to Regression Involving Many Categorical Predictors and Their Interactions","authors":"Hanmei Sun,&nbsp;Jiangshan Zhang,&nbsp;Jiming Jiang","doi":"10.1111/anzs.70034","DOIUrl":"10.1111/anzs.70034","url":null,"abstract":"<div>\u0000 \u0000 <p>Linear model prediction with a large number of potential predictors is both statistically and computationally challenging. The traditional approaches are largely based on shrinkage selection/estimation methods, which are applicable even when the number of potential predictors is (much) larger than the sample size. A situation of the latter scenario occurs when the candidate predictors involve many binary indicators corresponding to categories of some categorical predictors as well as their interactions. We propose an alternative approach to the shrinkage prediction methods in such a case based on mixed model prediction, which effectively treats combinations of the categorical effects as random effects. We establish theoretical validity of the proposed method and demonstrate empirically its advantage over the shrinkage methods. We also develop measures of uncertainty for the proposed method and evaluate their performance empirically. A real-data example is considered.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057740","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 Impact of R on Statistical Science for Spatial Point Processes","authors":"Adrian Baddeley,&nbsp;Ege Rubak,&nbsp;Rolf Turner","doi":"10.1111/anzs.70037","DOIUrl":"10.1111/anzs.70037","url":null,"abstract":"<div>\u0000 \u0000 <p>A spatial point pattern is a dataset representing the observed locations of things or events, such as disease cases, distant galaxies, trees, crimes, earthquakes or road accidents. The stochastic mechanism that generated the data is called a spatial point process. The statistical analysis of such data has been completely transformed by the availability of <span>R</span>. The <span>R</span> environment has enabled fundamental methodological research to proceed hand in hand with software development, leading to substantial advances in statistical methodology for spatial point processes which were immediately applicable to real data. This article is a broad historical account of the breakthroughs in statistical methodology for spatial point processes that were facilitated by <span>R</span> software, specifically the authors' package <span>spatstat</span>. It focuses on spatial point process modelling, model-fitting, model diagnostics, challenges to statistical inference, lessons to be learned and new challenges for future research.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146058048","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":"Co-Clustering Analysis of Multi-Layer Directed Networks: A Spectral Approach","authors":"Xiangchao Li,&nbsp;Qing Yang","doi":"10.1111/anzs.70035","DOIUrl":"10.1111/anzs.70035","url":null,"abstract":"<div>\u0000 \u0000 <p>This article focuses on community detection in multi-layer-directed degree-corrected block models. A spectral co-clustering method building upon a bias-adjusted aggregation matrix is proposed to identify the community memberships. We theoretically demonstrate the efficacy of our approach by controlling the proportion of misclustered nodes. Both simulations and real-data analysis provide empirical evidence supporting the effectiveness of our proposed methodology.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146058093","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":"Addendum to ‘The Incremental Progression From Fixed to Random Factors in the Analysis of Variance: A New Synthesis’","authors":"","doi":"10.1111/anzs.70022","DOIUrl":"10.1111/anzs.70022","url":null,"abstract":"<p>\u0000 <span>Anderson, M. J.</span>, <span>Gorley, R. N.</span>, and <span>Terlizzi, A.</span> (<span>2025</span>). “ <span>The Incremental Progression From Fixed to Random Factors in the Analysis of Variance: A New Synthesis</span>.” <i>Australian &amp; New Zealand Journal of Statistics</i> <span>67</span>(<span>1</span>): <span>3</span>–<span>30</span>.</p><p>This article was presented on Tuesday, 3 December 2024, as the inaugural live-streamed <i>Australian &amp; New Zealand Journal of Statistics</i> Read Paper at the New Zealand Statistical Association (NZSA) Conference held at Victoria University of Wellington, New Zealand, with discussion, including presentations by invited discussants.</p><p>The title of the paper should have been ‘The Incremental Progression From Fixed to Random Factors in the Analysis of Variance: A New Synthesis (with discussion)’.</p><p>The written discussion comments and authors' response should have appeared alongside the article upon publication. These are now added to the original article.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146058064","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":"Seminal Ideas and Controversies in Statistics. By Roderick J. A. Little, Boca Raton, FL: CRC Press, 2025. 243 pp. AU$120 (Paperback). ISBN: 978-1-032-49356-5","authors":"Luke R. Lloyd-Jones","doi":"10.1111/anzs.70030","DOIUrl":"10.1111/anzs.70030","url":null,"abstract":"","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057998","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}