Australian & New Zealand Journal of Statistics最新文献

{"title":"Using R-Indicators to Address Response Bias: Evidence From the Longitudinal Surveys of Australian Youth","authors":"Somayeh Parvazian,&nbsp;Ronnie Semo","doi":"10.1111/anzs.70009","DOIUrl":"https://doi.org/10.1111/anzs.70009","url":null,"abstract":"<div>\u0000 \u0000 <p>This study uses representative indicators or ‘R-indicators’ as survey quality measures to investigate the possible risk of biased estimators in the latest Longitudinal Surveys of Australian Youth (LSAY) cohort. Using data from the first four waves of the cohort, R-indicators are used to measure how the response composition differs from that of the original sample. We present R-indicators for each survey wave, providing a comparable measure to investigate the quality and level of representativeness of the data over time. We also compute partial R-indicators for a range of auxiliary variables, including state, sector, location, sex, Indigenous status, socio-economic status (SES), mathematics and reading achievement scores and immigration status, to identify groups requiring further targeting in the sampling process. The effects of other activities undertaken to increase the sample size and improve the quality of the data are also explored. These include recruiting a top-up sample and re-engaging with non-respondents using additional efforts such as offering incentives. The article concludes by identifying respondent subgroups that need to be targeted or prioritised for follow-up in future waves. Examples of strategies we have used to engage the identified subgroups are also discussed.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 3","pages":"416-431"},"PeriodicalIF":0.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111409","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":"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":"Forecasting Density-Valued Functional Panel Data","authors":"Cristian F. Jiménez-Varón,&nbsp;Ying Sun,&nbsp;Han Lin Shang","doi":"10.1111/anzs.70013","DOIUrl":"https://doi.org/10.1111/anzs.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>We introduce a statistical method for modelling and forecasting functional panel data represented by multiple densities. Density functions are non-negative and have a constrained integral, and thus do not constitute a linear vector space. We implement a centre log-ratio transformation to transform densities into unconstrained functions. These functions exhibit cross-sectional correlation and temporal dependence. Via a functional analysis-of-variance decomposition, we decompose the unconstrained functional panel data into a deterministic trend component and a time-varying residual component. To produce forecasts for the time-varying component, a functional time series forecasting method, based on the estimation of the long-run covariance, is implemented. By combining the forecasts of the time-varying residual component with the deterministic trend component, we obtain <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>h</mi>\u0000 </mrow>\u0000 <annotation>$$ h $$</annotation>\u0000 </semantics></math>-step-ahead forecast curves for multiple populations. Illustrated by age- and sex-specific life-table death counts in the United States, we apply our proposed method to generate forecasts of the life-table death counts for 51 states.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 3","pages":"401-415"},"PeriodicalIF":0.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110846","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":"Distance Measures for Unweighted Undirected Networks: A Comparison Study","authors":"Anna Simonetto,&nbsp;Matteo Ventura","doi":"10.1111/anzs.70015","DOIUrl":"https://doi.org/10.1111/anzs.70015","url":null,"abstract":"<p>Networks are mathematical structures that allow the representation of complex systems by jointly modelling the elements of the system and the relationships that exist among them. To analyse different contexts or systems, methodological tools are necessary to allow for the quantitative estimation of the differences existing between two or more networks. For this purpose, various tools have been proposed in the literature. This study is an exploratory analysis of the impacts that different methods (distances and spectral methods) have on the comparative evaluation of two networks. The analyses were conducted through a simulation study that considered three different perturbation schemes to investigate the behaviour of each method with increasing randomness in the perturbation scheme (i.e., edge removal). Results show that the distances between adjacency matrices are sensitive only to changes in the network density, while spectral methods are sensitive to changes in both the network density and the degree of the nodes.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 3","pages":"373-384"},"PeriodicalIF":0.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110885","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":"Population Size Estimation Using Covariates Having Missing Values and Measurement Error: Estimating Ethnic Group Sizes in New Zealand","authors":"Paul A. Smith,&nbsp;Peter G.M. van der Heijden,&nbsp;Maarten Cruyff,&nbsp;Francesco Pantalone,&nbsp;Hannes Diener,&nbsp;Kim Dunstan","doi":"10.1111/anzs.70014","DOIUrl":"https://doi.org/10.1111/anzs.70014","url":null,"abstract":"<p>We investigate the use of multiple linked lists for population size estimation and to estimate the relationships between covariates appearing on the lists. Over the lists, the covariates aim to measure the same concept. The relationships between the covariates are not fully known because of missing values on the covariates: some cases do not appear in some lists; some cases are on one or more of the lists but have missing covariate values on some of the lists; and some cases are not observed in any list. In earlier work, multiple system estimation has been combined with latent class analysis to give a consensus estimate where an underlying dichotomous categorical covariate is measured differently in different lists. This was applied to ethnicity covariates in New Zealand with two levels, Māori and non-Māori. In this paper, we apply this approach to ethnicity covariates with a larger number of categories, and find that it produces satisfactory results with four categories. We assess the purity of the latent classes using entropy and conditional probability measures. We also examine the evolution of annual estimates from multiple lists (where one list is the population census) over 2013–2020, finding that the estimated latent class proportions are very stable. We assess the impact of disclosure control measures on the outputs.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 3","pages":"432-453"},"PeriodicalIF":0.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110886","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}