Australian & New Zealand Journal of Statistics最新文献

{"title":"Clemens William Pratt, 2 July 1936–16 January 2025","authors":"Dennis Trewin","doi":"10.1111/anzs.70012","DOIUrl":"https://doi.org/10.1111/anzs.70012","url":null,"abstract":"<p>Clem Pratt was born in Brisbane in 1936 and died in Melbourne in 2025, aged 88 years. He is survived by his three children, grandchildren and great-grandchildren. He was President of the Statistical Society of Australia (SSA) from 1969 to 1971.</p><p>Clem was educated in Brisbane and was dux of Brisbane High School in 1954. At the age of 18, he enrolled in engineering studies at the University of Queensland, and then pursued postgraduate studies there and at the University of London, being awarded the degrees of BSc (Qld), BEng (Hons) (Qld), MEngSc (Qld) and PhD (London).</p><p>His background was primarily in queueing and congestion theory but also operations research more generally. He studied for his PhD (1961–1963) at Birkbeck College, University of London, under the direction of Professor (later Sir) David Cox; the topic for his dissertation was ‘<i>Congestion Problems In Automatic And Semi-automatic Telephone Exchanges</i>’.</p><p>In the 1960s, as an external part-time lecturer, he taught ‘Statistical Methods for Research Workers’ and ‘Operations Research’ at Melbourne University as part of the third-year Statistics course. I was fortunate to be one of his students and found his lectures inspiring. It was an early experience of the practical use of statistical theory.</p><p>Pratt helped set up the Victorian Branch of the SSA and was an early President of the Branch; it was during his tenure as President that the annual Belz Lecture was established in 1969. There were only three Branches of the SSA at that time (initially NSW, followed by Canberra and then Victoria).</p><p>Pratt had a 38-year career with Telstra (previously the PMG and then Telecom Australia), initially specialising in telephone traffic engineering, and later working in information systems planning and development, network operations, quality management and network planning. During this period, he represented Australia on several international bodies (including the International Telecommunication Union in Geneva and the Economic Commission for Asia &amp; the Far East in Bangkok) and was on the governing body of the International Teletraffic Congresses. He also chaired the Board at the Teletraffic Research Centre at the University of Adelaide in the late 1990s. The Centre, incorporating the Centre for Defence Communications and Information Networks, conducted research and development for both the commercial and defence sectors.</p><p>In retirement, he lived in Melbourne where he enjoyed music, theatre, amateur astronomy and the company of his grandchildren and great-grandchildren. He became non-verbal in the latter years of his life because of a throat surgery but I was fortunate to have several email exchanges with him in late 2023 as part of my research for the SSA History Standing Committee.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"339-340"},"PeriodicalIF":0.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615478","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":"Duncan Standon Ironmonger AM FASSA, 12 October 1931–3 September 2024","authors":"Dennis Trewin,&nbsp;Len Cook","doi":"10.1111/anzs.70011","DOIUrl":"https://doi.org/10.1111/anzs.70011","url":null,"abstract":"&lt;p&gt;Duncan Standon Ironmonger was a key leader in transforming economic statistics so that the contribution of the household economy could be measured, compared and contrasted with that of the market economy. This was a fundamental shift in economic thinking. The centrepiece of a country's economic statistics, the UN System of National Accounts, provided the foundation to capture the non-market production of goods and services not measured by conventional national accounts. Because of this work, these accounts now recognise that the household economy is a major pillar of the ‘standard of living’, providing not only subsistence in many countries on the globe but also a high standard of living in advanced economies. Ironmonger was also an innovator in the application of time-use surveys to policy questions.&lt;/p&gt;&lt;p&gt;Duncan Ironmonger was born in Orange, NSW, in 1931 and died in Melbourne on 3 September 2024, aged 92. He is described as a household economist, but he started his career as a statistician and was a lifelong significant and innovative user of statistics. He was a long-term member of the Statistical Society of Australia. One of his colleagues said he never considered himself disconnected from the Australian Bureau of Statistics (ABS).&lt;/p&gt;&lt;p&gt;Prior to his starting school, Duncan's family moved to Yass to open a stock-and- station agent business. Duncan went to school locally but finished his schooling at Canberra Grammar School. At the time he commenced university studies, there was a branch of the University of Melbourne in Canberra (later to become ANU). He undertook part-time studies in economics there, supported by a Commonwealth Scholarship, and graduated with a Master of Commerce degree. He also received a scholarship to study at Cambridge University, where he was awarded a PhD in economics (on the Theory of Consumer Behaviour).&lt;/p&gt;&lt;p&gt;His economic and statistical career really began around 1960 in Canberra at the Commonwealth Bureau of Census and Statistics (CBCS), now the ABS, although he first started work at the CBCS, around 1950, prior to undertaking his university studies.&lt;/p&gt;&lt;p&gt;On his return to the ABS after finishing his PhD studies, he contributed to the creation of a new system for reporting the national accounts. This was a period of rapid development for the national accounts. At that time, only annual national accounts were published, but during the 1960s, quarterly national accounts were produced (dating back to 1958), constant price estimates were developed, and the first national input–output tables were produced.&lt;/p&gt;&lt;p&gt;Duncan would have contributed to all these developments, helped by his exposure to Richard Stone, the father of national accounts, while at Cambridge.&lt;/p&gt;&lt;p&gt;Duncan left the CBCS (now the ABS) in 1966 for the Institute of Applied Economic and Social Research (now known as the Melbourne Institute) at the University of Melbourne, where he spent 18 years. He was recruited as a Senior Research Fellow and then be","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"337-338"},"PeriodicalIF":0.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615475","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":"A Divide and Conquer Algorithm of Bayesian Density Estimation","authors":"Ya Su","doi":"10.1111/anzs.70008","DOIUrl":"https://doi.org/10.1111/anzs.70008","url":null,"abstract":"<p>Datasets for statistical analysis become extremely large even when stored on one single machine with some difficulty. Even when the data can be stored in one machine, the computational cost would still be intimidating. We propose a divide and conquer solution to density estimation using Bayesian mixture modelling, including the infinite mixture case. The methodology can be generalised to other application problems where a Bayesian mixture model is adopted. The proposed prior on each machine or subgroup modifies the original prior on both mixing probabilities and the rest of parameters in the distributions being mixed. The ultimate estimator is obtained by taking the average of the posterior samples corresponding to the proposed prior on each subset. Despite the tremendous reduction in time thanks to data splitting, the posterior contraction rate of the proposed estimator stays the same (up to a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>log</mi>\u0000 </mrow>\u0000 <annotation>$$ log $$</annotation>\u0000 </semantics></math> factor) as that using the original prior when the data is analysed as a whole. Simulation studies also justify the competency of the proposed method compared to the established WASP estimator in the finite-dimension case. In addition, one of our simulations is performed in a shape-constrained deconvolution context and reveals promising results. The application to a GWAS dataset reveals the advantage over a naive divide and conquer method that uses the original prior.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"250-264"},"PeriodicalIF":0.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615485","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":"Homogeneity and Sparsity Pursuit Using Robust Adaptive Fused Lasso","authors":"Le Chang,&nbsp;Yanlin Shi","doi":"10.1111/anzs.70010","DOIUrl":"https://doi.org/10.1111/anzs.70010","url":null,"abstract":"<p>Fused lasso regression is a popular method for identifying homogeneous groups and sparsity patterns in regression coefficients based on either the presumed order or a more general graph structure of the covariates. However, the traditional fused lasso may yield misleading outcomes in the presence of outliers. In this paper, we propose an extension of the fused lasso, namely the robust adaptive fused lasso (RAFL), which pursues homogeneity and sparsity patterns in regression coefficients while accounting for potential outliers within the data. By using Huber's loss or Tukey's biweight loss, RAFL can resist outliers in the responses or in both the responses and the covariates. We also demonstrate that when the adaptive weights are properly chosen, the proposed RAFL achieves consistency in variable selection, consistency in grouping and asymptotic normality. Furthermore, a novel optimization algorithm, which employs the alternating direction method of multipliers, embedded with an accelerated proximal gradient algorithm, is developed to solve RAFL efficiently. Our simulation study shows that RAFL offers substantial improvements in terms of both grouping accuracy and prediction accuracy compared with the fused lasso, particularly when dealing with contaminated data. Additionally, a real analysis of cookie data demonstrates the effectiveness of RAFL.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"157-174"},"PeriodicalIF":0.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615493","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":"High-dimensional graphical inference via partially penalised regression","authors":"Ni Zhao,&nbsp;Zemin Zheng,&nbsp;Yang Li","doi":"10.1111/anzs.70005","DOIUrl":"https://doi.org/10.1111/anzs.70005","url":null,"abstract":"<div>\u0000 \u0000 <p>Graphical models are important tools to characterise the conditional independence structure among a set of variables. Despite the rapid development of statistical inference for high-dimensional graphical models, existing methods typically need a stringent constraint on the sample size. In this paper, we develop a new graphical projection estimator (GPE) for statistical inference in Gaussian graphical models via partially penalised regression. The suggested inference procedure takes advantage of the strong signals, which can be identified in advance, and utilises partially penalised regression to avoid the penalisation on them when constructing the GPE. It leads to enhanced inference efficiency by removing the impacts of strong signals that contribute to the bias term. We show that the proposed GPE can enjoy asymptotic normality under a relaxed constraint on the sample size, which is of the same order as that needed for consistent estimation. The usefulness of our method is demonstrated through simulations and a prostate tumour gene expression dataset.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"265-291"},"PeriodicalIF":0.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615012","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":"How data or error covariance can change and still retain BLUEs as well as their covariance or the sum of squares of errors","authors":"Stephen J. Haslett,&nbsp;Jarkko Isotalo,&nbsp;Augustyn Markiewicz,&nbsp;Simo Puntanen","doi":"10.1111/anzs.70003","DOIUrl":"https://doi.org/10.1111/anzs.70003","url":null,"abstract":"<p>Misspecification of the error covariance in linear models usually leads to incorrect inference and conclusions. We consider two linear models, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>A</mi>\u0000 </mrow>\u0000 <annotation>$$ mathcal{A} $$</annotation>\u0000 </semantics></math>\u0000and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>B</mi>\u0000 </mrow>\u0000 <annotation>$$ mathcal{B} $$</annotation>\u0000 </semantics></math>, with the same design matrix but different error covariance matrices. The conditions under which every representation of the best linear unbiased estimator (BLUE) of any estimable parametric vector under <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>A</mi>\u0000 </mrow>\u0000 <annotation>$$ mathcal{A} $$</annotation>\u0000 </semantics></math> remains BLUE under <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>B</mi>\u0000 </mrow>\u0000 <annotation>$$ mathcal{B} $$</annotation>\u0000 </semantics></math>\u0000have been well known since C.R. Rao's paper in 1971: Unified theory of linear estimation, <i>Sankhyā Ser. A</i>, Vol. 33, pp. 371–394. However, there are no previously published results on retaining the weighted sum of squares of errors (SSE) for non-full-rank design or error covariance matrices, and the question of when the covariance matrix of the BLUEs is also retained has been partially explored only recently. For change in any specified error covariance matrix, we provide necessary and sufficient conditions (nasc) for both BLUEs and their covariance matrix to remain unaltered and to retain this property for all submodels. We also consider nasc for SSE to be unchanged. We decompose SSE under error covariance changes, and derive nasc under which error covariance change leaves hypothesis tests for fixed-effect deletion under normality unaltered. We also show that simultaneous retention of BLUEs and both their covariance and SSE is not possible. We outline the effects of weak and strong error covariance singularity. We provide applications (via data cloning) to maintaining data confidentiality in Official Statistics without using Confidentialised Unit Record Files (CURFs), to certain types of experimental design and to estimation of fixed parameters for linear models for single nucleotide polymorphisms (SNPs) in genetics.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"175-201"},"PeriodicalIF":0.8,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615521","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":"Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science. By Aubrey Clayton, New York, Columbia University Press, 1st ed., 2021. 368 pages. AU$ 57.95 (hardcover). ISBN: 10:0231199945.","authors":"Mahdi Nouraie","doi":"10.1111/anzs.70007","DOIUrl":"https://doi.org/10.1111/anzs.70007","url":null,"abstract":"","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"344-345"},"PeriodicalIF":0.8,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615516","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":"Autocovariance function estimation via difference schemes for a semiparametric change point model with \u0000 \u0000 \u0000 m\u0000 \u0000 $$ m $$\u0000 -dependent errors","authors":"Michael Levine,&nbsp;Inder Tecuapetla-Gómez","doi":"10.1111/anzs.70002","DOIUrl":"https://doi.org/10.1111/anzs.70002","url":null,"abstract":"<div>\u0000 \u0000 <p>We discuss a broad class of difference-based estimators of the autocovariance function in a semiparametric regression model where the signal consists of the sum of a smooth function and another stepwise function whose number of jumps and locations are unknown (change points) while the errors are stationary and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>m</mi>\u0000 </mrow>\u0000 <annotation>$$ m $$</annotation>\u0000 </semantics></math>-dependent. We establish that the influence of the smooth part of the signal over the bias of our estimators is negligible; this is a general result as it does not depend on the distribution of the errors. We show that the influence of the unknown smooth function is negligible also in the mean squared error (MSE) of our estimators. Although we assumed Gaussian errors to derive the latter result, our finite sample studies suggest that the class of proposed estimators still show small MSE when the errors are not Gaussian. Our simulation study also demonstrates that, when the error process is mis-specified as an AR<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mn>1</mn>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ (1) $$</annotation>\u0000 </semantics></math> instead of an <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>m</mi>\u0000 </mrow>\u0000 <annotation>$$ m $$</annotation>\u0000 </semantics></math>-dependent process, our proposed method can estimate autocovariances about as well as some methods specifically designed for the AR(1) case, and sometimes even better than them. We also allow both the number of change points and the magnitude of the largest jump grow with the sample size <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <annotation>$$ n $$</annotation>\u0000 </semantics></math>. In this case, we provide conditions on the interplay between the growth rate of these two quantities as well as the vanishing rate of the modulus of continuity (of the signal's smooth part) that ensure <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msqrt>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </msqrt>\u0000 </mrow>\u0000 <annotation>$$ sqrt{n} $$</annotation>\u0000 </semantics></math> consistency of our autocovariance estimators. As an application, we use our approach to provide a better understanding of the possible autocovariance structure of a time series of global averaged annual temperature anomalies. Finally, the <span>R</span> package <span>dbacf</span> complements this article.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"202-223"},"PeriodicalIF":0.8,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615522","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":"Functional Data Analysis with R. By C. M. Crainiceanu, J. Goldsmith, A. Leroux, and E. Cui, Boca Raton, FL: Chapman and Hall/CRC. 2024. 338 pages. AU$ 138.40 (hardback). ISBN: 978-1-032-24471-6.","authors":"Faïcel Chamroukhi","doi":"10.1111/anzs.70006","DOIUrl":"https://doi.org/10.1111/anzs.70006","url":null,"abstract":"","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"341-343"},"PeriodicalIF":0.8,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615518","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":"Statistical balancing as an unconstrained optimisation problem","authors":"N. T. Longford","doi":"10.1111/anzs.70004","DOIUrl":"https://doi.org/10.1111/anzs.70004","url":null,"abstract":"<div>\u0000 \u0000 <p>Within the potential outcomes framework, balancing the treatment groups is a key step in estimating the average treatment effect in an observational study. Methods for optimal matching or weighting solve nonlinear programming problems. We present an alternative, related to ridge regression. Its solution has a closed form and is a smooth function of a set of tuning parameters. The method is accompanied by a simple way of exploring the sensitivity with respect to bias due to an unobserved confounder. It is applied to retrospective studies in neonatal research, concerned with clinical care for preterm born babies in the first few weeks of their lives.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"292-319"},"PeriodicalIF":0.8,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615515","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}