Australian & New Zealand Journal of Statistics最新文献_第3页

Identifying changes in the distribution of income from higher-order moments with an application to Australia 从高阶矩确定收入分配的变化并应用于澳大利亚

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2024-01-17 DOI: 10.1111/anzs.12405

Vance L. Martin, Jialu Shi, Yang Song, Wenying Yao

引用次数: 0

Exact testing for heteroscedasticity in a two-way layout in variety frost trials when incorporating a covariate 在品种霜冻试验的双向布局中，在纳入协变量时对异方差进行精确测试

IF 0.8 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2024-01-01 DOI: 10.1111/anzs.12404

Angelika A. Pilkington, Brenton R. Clarke, Dean A. Diepeveen

{"title":"Exact testing for heteroscedasticity in a two-way layout in variety frost trials when incorporating a covariate","authors":"Angelika A. Pilkington, Brenton R. Clarke, Dean A. Diepeveen","doi":"10.1111/anzs.12404","DOIUrl":"10.1111/anzs.12404","url":null,"abstract":"<p>Two-way layouts are common in grain industry research where it is often the case that there are one or more covariates. It is widely recognised that when estimating fixed effect parameters, one should also examine for possible extra error variance structure. An exact test for heteroscedasticity, when there is a covariate, is illustrated for a data set from frost trials in Western Australia. While the general algebra for the test is known, albeit in past literature, there are computational aspects of implementing the test for the two way when there are covariates. In this scenario the test is shown to have greater power than the industry standard, and because of its exact size, is preferable to use of the restricted maximum likelihood ratio test (REMLRT) based on the approximate asymptotic distribution in this instance. Formulation of the exact test considered here involves creation of appropriate contrasts in the experimental design. This is illustrated using specific choices of observations corresponding to an index set in the linear model for the two-way layout. Also an algorithm supplied complements the test. Comparisons of size and power then ensue. The test has natural extensions when there are unbalanced data, and more than one covariate may be present. Results can be extended to Balanced Incomplete Block Designs.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 4","pages":"309-326"},"PeriodicalIF":0.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12404","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139078183","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}

引用次数: 0

Exact likelihoods for N-mixture models with time-to-detection data 具有时间检测数据的 N 混合物模型的精确似然值

IF 0.8 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-12-11 DOI: 10.1111/anzs.12401

Linda M. Haines, Res Altwegg, D. L. Borchers

{"title":"Exact likelihoods for N-mixture models with time-to-detection data","authors":"Linda M. Haines, Res Altwegg, D. L. Borchers","doi":"10.1111/anzs.12401","DOIUrl":"10.1111/anzs.12401","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper is concerned with the formulation of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>-mixture models for estimating the abundance and probability of detection of a species from binary response, count and time-to-detection data. A modelling framework, which encompasses time-to-first-detection within the context of detection/non-detection and time-to-each-detection and time-to-first-detection within the context of count data, is introduced. Two observation processes which depend on whether or not double counting is assumed to occur are also considered. The main focus of the paper is on the derivation of explicit forms for the likelihoods associated with each of the proposed models. Closed-form expressions for the likelihoods associated with time-to-detection data are new and are developed from the theory of order statistics. A key finding of the study is that, based on the assumption of no double counting, the likelihoods associated with times-to-detection together with count data are the product of the likelihood for the counts alone and a term which depends on the detection probability parameter. This result demonstrates that, in this case, recording times-to-detection could well improve precision in estimation over recording counts alone. In contrast, for the double counting protocol with exponential arrival times, no information was found to be gained by recording times-to-detection in addition to the count data. An <span>R</span> package and an accompanying vignette are also introduced in order to complement the algebraic results and to demonstrate the use of the models in practice.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 4","pages":"327-343"},"PeriodicalIF":0.8,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138567024","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}

引用次数: 0

Model averaged tail area confidence intervals in nested linear regression models 嵌套线性回归模型中的模型平均尾区置信区间

IF 0.8 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-12-07 DOI: 10.1111/anzs.12402

Paul Kabaila, Ayesha Perera

{"title":"Model averaged tail area confidence intervals in nested linear regression models","authors":"Paul Kabaila, Ayesha Perera","doi":"10.1111/anzs.12402","DOIUrl":"10.1111/anzs.12402","url":null,"abstract":"<div>\u0000 \u0000 <p>The performance, in terms of coverage and expected length, of the model averaged tail area (MATA) confidence interval, proposed by Turek & Fletcher (2012, <i>Computational Statistics & Data Analysis</i>, 56, 2809–2815), depends greatly on the data-based model weights used in its construction. We generalise the computationally convenient exact formulae due to Kabaila, Welsh & Abeysekera (2016, <i>Scandinavian Journal of Statistics</i>, 43, 35–48) for the coverage and expected length of this confidence interval for two nested linear regression models to the case of two or more nested linear regression models. This permits the numerical assessment of the performance, in terms of coverage probability and scaled expected length, of the MATA confidence interval for any given data-based model weights in the context of three or more nested linear regression models. We illustrate this numerical assessment of performance of the MATA confidence interval, for model weights based on any given Generalised Information Criterion, in the context of three nested linear regression models using the real life ‘Cholesterol’ data. This provides a very informative further exploration of the influence of these model weights on the performance of this confidence interval.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 4","pages":"364-378"},"PeriodicalIF":0.8,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548308","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}

引用次数: 0

The role of pairwise matching in experimental design for an incidence outcome 成对匹配在实验设计中对发生率结果的作用

IF 0.8 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-11-27 DOI: 10.1111/anzs.12403

Adam Kapelner, Abba M. Krieger, David Azriel

引用次数: 0

Measurement errors in semi-parametric generalised regression models 半参数广义回归模型的测量误差

IF 0.8 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-10-11 DOI: 10.1111/anzs.12400

Mohammad W. Hattab, David Ruppert

{"title":"Measurement errors in semi-parametric generalised regression models","authors":"Mohammad W. Hattab, David Ruppert","doi":"10.1111/anzs.12400","DOIUrl":"10.1111/anzs.12400","url":null,"abstract":"<div>\u0000 \u0000 <p>Regression models that ignore measurement error in predictors may produce highly biased estimates leading to erroneous inferences. It is well known that it is extremely difficult to take measurement error into account in Gaussian non-parametric regression. This problem becomes even more difficult when considering other families such as binary, Poisson and negative binomial regression. We present a novel method aiming to correct for measurement error when estimating regression functions. Our approach is sufficiently flexible to cover virtually all distributions and link functions regularly considered in generalised linear models. This approach depends on approximating the first and the second moment of the response after integrating out the true unobserved predictors in any semi-parametric generalised regression model. By the latter is meant a model with both linear and non-parametric effects that are connected to the mean response by a link function and with a response distribution in an exponential family or quasi-likelihood model. Unlike previous methods, the method we now propose is not restricted to truncated splines and can utilise various basis functions. Moreover, it can operate without making any distributional assumption about the unobserved predictor. Through extensive simulation studies, we study the performance of our method under many scenarios.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 4","pages":"344-363"},"PeriodicalIF":0.8,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136212682","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}

引用次数: 0

Comparisons of distributions of Australian mental health scores 澳大利亚心理健康得分分布的比较

IF 0.8 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-10-11 DOI: 10.1111/anzs.12399

D. Gunawan, William E. Griffiths, D. Chotikapanich

引用次数: 0

Embedding latent class regression and latent class distal outcome models into cluster-weighted latent class analysis: a detailed simulation experiment 将潜在类别回归和潜在类别远端结果模型嵌入聚类加权潜在类别分析：一个详细的模拟实验

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-09-22 DOI: 10.1111/anzs.12396

Roberto Di Mari, Antonio Punzo, Zsuzsa Bakk

{"title":"Embedding latent class regression and latent class distal outcome models into cluster-weighted latent class analysis: a detailed simulation experiment","authors":"Roberto Di Mari, Antonio Punzo, Zsuzsa Bakk","doi":"10.1111/anzs.12396","DOIUrl":"https://doi.org/10.1111/anzs.12396","url":null,"abstract":"<p>Usually in latent class (LC) analysis, external predictors are taken to be cluster conditional probability predictors (LC models with external predictors), and/or score conditional probability predictors (LC regression models). In such cases, their distribution is not of interest. Class-specific distribution is of interest in the distal outcome model, when the distribution of the external variables is assumed to depend on LC membership. In this paper, we consider a more general formulation, that embeds both the LC regression and the distal outcome models, as is typically done in cluster-weighted modelling. This allows us to investigate (1) whether the distribution of the external variables differs across classes, (2) whether there are significant direct effects of the external variables on the indicators, by modelling jointly the relationship between the external and the latent variables. We show the advantages of the proposed modelling approach through a set of artificial examples, an extensive simulation study and an empirical application about psychological contracts among employees and employers in Belgium and the Netherlands.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 3","pages":"213-233"},"PeriodicalIF":1.1,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50141418","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}

引用次数: 0

Bayesian modelling of effects of prenatal alcohol exposure on child cognition based on data from multiple cohorts 基于多个队列数据的产前酒精暴露对儿童认知影响的贝叶斯建模

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-09-08 DOI: 10.1111/anzs.12397

Khue-Dung Dang, Louise M. Ryan, Tugba Akkaya Hocagil, Richard J. Cook, Gale A. Richardson, Nancy L. Day, Claire D. Coles, Heather Carmichael Olson, Sandra W. Jacobson, Joseph L. Jacobson

{"title":"Bayesian modelling of effects of prenatal alcohol exposure on child cognition based on data from multiple cohorts","authors":"Khue-Dung Dang, Louise M. Ryan, Tugba Akkaya Hocagil, Richard J. Cook, Gale A. Richardson, Nancy L. Day, Claire D. Coles, Heather Carmichael Olson, Sandra W. Jacobson, Joseph L. Jacobson","doi":"10.1111/anzs.12397","DOIUrl":"https://doi.org/10.1111/anzs.12397","url":null,"abstract":"<div>\u0000 \u0000 <p>High levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, but the exact nature of the dose-response relationship is less well understood. To investigate this relationship, data were assembled from six longitudinal birth cohort studies examining the effects of PAE on cognitive outcomes from early school age through adolescence. Structural equation models (SEMs) are a natural approach to consider, because of the way they conceptualise multiple observed outcomes as relating to an underlying latent variable of interest, which can then be modelled as a function of exposure and other predictors of interest. However, conventional SEMs could not be fitted in this context because slightly different outcome measures were used in the six studies. In this paper we propose a multi-group Bayesian SEM that maps the unobserved cognition variable to a broad range of observed outcomes. The relation between these variables and PAE is then examined while controlling for potential confounders via propensity score adjustment. By examining different possible dose-response functions, the proposed framework is used to investigate whether there is a threshold PAE level that results in minimal cognitive deficit.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 3","pages":"167-186"},"PeriodicalIF":1.1,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50125566","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}

引用次数: 0

Statistical methods for astronomical data analysis. By A. K. Chattopadhyay and T. Chattopadhyay. New York: Springer. 2014. 349 pages. UK£49.99 (hardback). ISBN: 978-1-4939-1506-4. 天文数据分析的统计方法。作者:A. K.Chattopadhyay和T.Chattopadhyay纽约:斯普林格出版社，2014。349页。英国£49.99(精装)。ISBN: 978量量4939还是1506 4。

IF 0.8 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-09-04 DOI: 10.1111/anzs.12398

Soumita Modak

引用次数: 0