Australian & New Zealand Journal of Statistics最新文献

{"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}

{"title":"The incremental progression from fixed to random factors in the analysis of variance: a new synthesis","authors":"Marti J. Anderson,&nbsp;Ray N. Gorley,&nbsp;Antonio Terlizzi","doi":"10.1111/anzs.70001","DOIUrl":"https://doi.org/10.1111/anzs.70001","url":null,"abstract":"<p>Classically, the distinction between a fixed versus a random factor in analysis of variance has been considered a binary choice. Here we consider that any given factor can also occur along an incremental series of steps between these two extremes, depending on the sampling fraction of its levels from the wider population. Fixed factors occur where all possible levels are drawn, and random factors occur in the limit as the population of possible levels approaches infinity. When some identifiable fraction of a finite population of possible levels is drawn, the factor can be thought of as something in between fixed and random, and can be analysed explicitly as finite directly within the analysis of variance (ANOVA) framework. Requiring explicit specification of the population size from which observed levels are drawn for each factor, we provide a unified approach to derive expectations of mean squares (EMS) in ANOVA for any types of factors along the entire graded progression from fixed to random, inclusive, that may be nested within or crossed with one another, from balanced, asymmetrical or unbalanced designs, including multi-level hierarchical sampling designs, mixed models and interactions. Implications for estimation of variance components, tailored bootstrap methods and tests of hypotheses under minimal assumptions of exchangeability are described and further extended to multivariate dissimilarity-based settings.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 1","pages":"3-30"},"PeriodicalIF":0.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831039","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":"Least-squares estimators of the linear-by-linear association parameter from an ordinal log-linear model","authors":"Eric J. Beh,&nbsp;Sidra Zafar,&nbsp;Irene L. Hudson","doi":"10.1111/anzs.70000","DOIUrl":"https://doi.org/10.1111/anzs.70000","url":null,"abstract":"<p>When modelling the association between the ordinal categorical variables of a contingency table, ordinal log-linear models are typically used; these models are a variation of the more popular log-linear model, which has attracted considerable attention in the statistics and allied literature since the late 1960s. Estimating the parameters of an ordinal log-linear model usually involves the use of iterative techniques, typically Newton's method and iterative proportional fitting. However, the early 2000s brought with it more direct estimation methods that do not require the use of iterative techniques. When the focus is on the parameter that reflects the linear-by-linear association between the variables, these methods have proven to provide unbiased, consistent and normally distributed estimates. Despite this new work, no attention has been given to the estimation of the least-squares estimator. Therefore, this article derives the least-squares estimator of the linear-by-linear association parameter and shows it to be equivalent to one of the existing non-iterative estimators recently described. We also derive two further least-squares estimators based on the Box-Cox transformation and derive their variance.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"137-156"},"PeriodicalIF":0.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615167","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":"spbal: An R package for spatially balanced master sampling","authors":"B. L. Robertson,&nbsp;P. Davies,&nbsp;O. Gansell,&nbsp;P. van Dam-Bates,&nbsp;T. McDonald","doi":"10.1111/anzs.12435","DOIUrl":"https://doi.org/10.1111/anzs.12435","url":null,"abstract":"<p>One of the most critical design features for sampling spatial populations is being able to draw spatially balanced samples. A substantial body of literature on sampling methodology has shown that spatially balanced samples can improve the precision of commonly used design-based estimators in various settings. Spatially balanced master samples offer several practical advantages for practitioners, including adjusting the sample size to match budgetary constraints, intensifying a previous sample or defining a panel design for surveying over time. These designs are of practical importance and should be easy to generate with reliable and efficient software. The <span>spbal</span> <span>R</span> package provides explicit functionality for spatially balanced master sampling designs from point and areal resources. Stratified and panel designs are also possible with <span>spbal</span>. In this article, we demonstrate the flexibility of <span>spbal</span> with several example designs using spatial populations from New Zealand.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"320-336"},"PeriodicalIF":0.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12435","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615078","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":"Online semiparametric regression via sequential Monte Carlo","authors":"Marianne Menictas,&nbsp;Chris J. Oates,&nbsp;Matt P. Wand","doi":"10.1111/anzs.12440","DOIUrl":"https://doi.org/10.1111/anzs.12440","url":null,"abstract":"<div>\u0000 \u0000 <p>We develop and describe online algorithms for performing online semiparametric regression analyses. Earlier work on this topic is by Luts, Broderick and Wand (2014), <i>Journal of Computational and Graphical Statististics</i>, <b>23</b>, 589–615, where online mean-field variational Bayes (MFVB) was employed. In this article we instead develop sequential Monte Carlo approaches to circumvent well-known inaccuracies inherent in variational approaches. For Gaussian response semiparametric regression models, our new algorithms share the online MFVB property of only requiring updating and storage of sufficient statistics quantities of streaming data. In the non-Gaussian case, accurate online semiparametric regression requires the full data to be kept in storage. The new algorithms allow for new options concerning accuracy–speed trade-offs for online semiparametric regression.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 2","pages":"224-249"},"PeriodicalIF":0.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615474","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":"Text in R graphics","authors":"Paul Murrell","doi":"10.1111/anzs.12438","DOIUrl":"10.1111/anzs.12438","url":null,"abstract":"<p><span>R</span> graphics has provided the ability to draw simple text labels on plots since <span>R</span> version 1.0.0. However, these basic text-drawing facilities are quite limited. Over the past 25 years, more sophisticated text-drawing features have been added: support for the Unicode character set; access to system fonts; text paths; support for text markup; and rendering of typeset text. This article charts the development of text-drawing support in <span>R</span> and provides demonstrations of some of the more sophisticated features.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"68 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146130284","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":"Simultaneous clustering of individuals and covariates for high-dimensional longitudinal data","authors":"Chao Han,&nbsp;Jiaqi Wu,&nbsp;Weiping Zhang","doi":"10.1111/anzs.12437","DOIUrl":"https://doi.org/10.1111/anzs.12437","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper considers identifying and estimating high-dimensional longitudinal data models with latent subgroups and clustered covariates. We propose a regularised approach to recover group structures while simultaneously detecting clusters of significant covariates. The consistency and asymptotic normality are established for the estimator under mild conditions. Besides, we develop an effective algorithm based on local quadratic approximation to optimise the objective function. The finite-sample performance is illustrated through extensive simulations, and the proposed method is applied to study the shift in the economic structure of European countries before and after the debt crisis.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 1","pages":"31-50"},"PeriodicalIF":0.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831281","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":"Application of nonparametric approach to extreme value inference in distribution estimation of sample maximum and its properties","authors":"T. Moriyama","doi":"10.1111/anzs.12436","DOIUrl":"https://doi.org/10.1111/anzs.12436","url":null,"abstract":"<div>\u0000 \u0000 <p>Extreme value theory has constructed asymptotic properties of the sample maximum. This article concerns probability distribution estimation of the sample maximum. The traditional approach is parametric fitting to the limiting distribution—the generalised extreme value distribution; however, the model in non-limiting cases is misspecified to a certain extent. We propose a plug-in type of nonparametric estimator that does not need model specification. Asymptotic properties of the distribution estimator are derived. The simulation study numerically investigates the relative performance in finite-sample cases. This study assumes that the underlying distribution of the original sample belongs to one of the Hall class, the Weibull class or the bounded class, whose types of the limiting distributions are all different: the Fréchet, Gumbel or Weibull. It is proven that the convergence rate of the parametric fitting estimator depends on both the extreme value index and the second-order parameter, and gets slower as the extreme value index tends to zero. On the other hand, the rate of the nonparametric estimator is proven to be independent of the extreme value index under certain conditions. The numerical performances of the parametric fitting estimator and the nonparametric estimator are compared, which shows that the nonparametric estimator performs better, especially for the extreme value index close to zero. Finally, we report two real case studies: the Potomac River peak stream flow (cfs) data and the Danish Fire Insurance data.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"67 1","pages":"51-76"},"PeriodicalIF":0.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831282","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}