发布求助
文献互助 智能选刊 最新文献

Australian & New Zealand Journal of Statistics最新文献

筛选
英文 中文
Bayesian decision rules to classification problems 分类问题的贝叶斯决策规则
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-05-24 DOI: 10.1111/anzs.12325
Yuqi Long, Xingzhong Xu
{"title":"Bayesian decision rules to classification problems","authors":"Yuqi Long,&nbsp;Xingzhong Xu","doi":"10.1111/anzs.12325","DOIUrl":"10.1111/anzs.12325","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we analysed classification rules under Bayesian decision theory. The setup we considered here is fairly general, which can represent all possible parametric models. The Bayes classification rule we investigated minimises the Bayes risk under general loss functions. Among the existing literatures, the 0-1 loss function appears most frequently, under which the Bayes classification rule is determined by the posterior predictive densities. Theoretically, we extended the Bernstein–von Mises theorem to the multiple-sample case. On this basis, the oracle property of Bayes classification rule has been discussed in detail, which refers to the convergence of the Bayes classification rule to the one built from the true distributions, as the sample size tends to infinity. Simulations show that the Bayes classification rules do have some advantages over the traditional classifiers, especially when the number of features approaches the sample size.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 2","pages":"394-415"},"PeriodicalIF":1.1,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12325","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89269715","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: 1
Globally intensity-reweighted estimators for K- and pair correlation functions K-和对相关函数的全局强度重加权估计
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-05-17 DOI: 10.1111/anzs.12318
Thomas Shaw, Jesper M⊘ller, Rasmus Plenge Waagepetersen
{"title":"Globally intensity-reweighted estimators for K- and pair correlation functions","authors":"Thomas Shaw,&nbsp;Jesper M⊘ller,&nbsp;Rasmus Plenge Waagepetersen","doi":"10.1111/anzs.12318","DOIUrl":"10.1111/anzs.12318","url":null,"abstract":"<div>\u0000 \u0000 <p>We introduce new estimators of the inhomogeneous <i>K</i>-function and the pair correlation function of a spatial point process as well as the cross <i>K</i>-function and the cross pair correlation function of a bivariate spatial point process under the assumption of second-order intensity-reweighted stationarity. These estimators rely on a ‘global’ normalisation factor which depends on an aggregation of the intensity function, while the existing estimators depend ‘locally’ on the intensity function at the individual observed points. The advantages of our new global estimators over the existing local estimators are demonstrated by theoretical considerations and a simulation study.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 1","pages":"93-118"},"PeriodicalIF":1.1,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75341203","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: 9
A few statistical principles for data science 数据科学的一些统计原则
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-05-08 DOI: 10.1111/anzs.12324
Noel Cressie
{"title":"A few statistical principles for data science","authors":"Noel Cressie","doi":"10.1111/anzs.12324","DOIUrl":"10.1111/anzs.12324","url":null,"abstract":"<div>\u0000 \u0000 <p>In any other circumstance, it might make sense to define the extent of the terrain (Data Science) first, and then locate and describe the landmarks (Principles). But this data revolution we are experiencing defies a cadastral survey. Areas are continually being annexed into Data Science. For example, biometrics was traditionally statistics for agriculture in all its forms but now, in Data Science, it means the study of characteristics that can be used to identify an individual. Examples of non-intrusive measurements include height, weight, fingerprints, retina scan, voice, photograph/video (facial landmarks and facial expressions) and gait. A multivariate analysis of such data would be a complex project for a statistician, but a software engineer might appear to have no trouble with it at all. In any applied-statistics project, the statistician worries about uncertainty and quantifies it by modelling data as realisations generated from a probability space. Another approach to uncertainty quantification is to find similar data sets, and then use the variability of results between these data sets to capture the uncertainty. Both approaches allow ‘error bars’ to be put on estimates obtained from the original data set, although the interpretations are different. A third approach, that concentrates on giving a single answer and gives up on uncertainty quantification, could be considered as Data Engineering, although it has staked a claim in the Data Science terrain. This article presents a few (actually nine) statistical principles for data scientists that have helped me, and continue to help me, when I work on complex interdisciplinary projects.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 1","pages":"182-200"},"PeriodicalIF":1.1,"publicationDate":"2021-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82540496","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: 4
Information criteria for inhomogeneous spatial point processes 非齐次空间点过程的信息准则
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-05-08 DOI: 10.1111/anzs.12327
Achmad Choiruddin, Jean-François Coeurjolly, Rasmus Waagepetersen
{"title":"Information criteria for inhomogeneous spatial point processes","authors":"Achmad Choiruddin,&nbsp;Jean-François Coeurjolly,&nbsp;Rasmus Waagepetersen","doi":"10.1111/anzs.12327","DOIUrl":"10.1111/anzs.12327","url":null,"abstract":"<div>\u0000 \u0000 <p>The theoretical foundation for a number of model selection criteria is established in the context of inhomogeneous point processes and under various asymptotic settings: infill, increasing domain and combinations of these. For inhomogeneous Poisson processes we consider Akaike's information criterion and the Bayesian information criterion, and in particular we identify the point process analogue of ‘sample size’ needed for the Bayesian information criterion. Considering general inhomogeneous point processes we derive new composite likelihood and composite Bayesian information criteria for selecting a regression model for the intensity function. The proposed model selection criteria are evaluated using simulations of Poisson processes and cluster point processes.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 1","pages":"119-143"},"PeriodicalIF":1.1,"publicationDate":"2021-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12327","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80429406","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: 24
Depth and outliers for samples of sets and random sets distributions 集和随机集分布样本的深度和离群值
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-05-07 DOI: 10.1111/anzs.12326
Ignacio Cascos, Qiyu Li, Ilya Molchanov
{"title":"Depth and outliers for samples of sets and random sets distributions","authors":"Ignacio Cascos,&nbsp;Qiyu Li,&nbsp;Ilya Molchanov","doi":"10.1111/anzs.12326","DOIUrl":"10.1111/anzs.12326","url":null,"abstract":"<div>\u0000 \u0000 <p>We suggest several constructions suitable to define the depth of set-valued observations with respect to a sample of convex sets or with respect to the distribution of a random closed convex set. With the concept of a depth, it is possible to determine if a given convex set should be regarded an outlier with respect to a sample of convex closed sets. Some of our constructions are motivated by the known concepts of half-space depth and band depth for function-valued data. A novel construction derives the depth from a family of non-linear expectations of random sets. Furthermore, we address the role of positions of sets for evaluation of their depth. Two case studies concern interval regression for Greek wine data and detection of outliers in a sample of particles.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 1","pages":"55-82"},"PeriodicalIF":1.1,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88387347","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: 5
Infill asymptotics for adaptive kernel estimators of spatial intensity 空间强度自适应核估计的填充渐近性
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-05-07 DOI: 10.1111/anzs.12319
M.N.M. van Lieshout
{"title":"Infill asymptotics for adaptive kernel estimators of spatial intensity","authors":"M.N.M. van Lieshout","doi":"10.1111/anzs.12319","DOIUrl":"10.1111/anzs.12319","url":null,"abstract":"We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a spatial point process. We derive asymptotic expansions for the bias and variance under the regime that n independent copies of a simple point process in Euclidean space are superposed. The method is illustrated by means of a simple example and applied to tornado data.","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 1","pages":"159-181"},"PeriodicalIF":1.1,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80890931","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: 3
Model-based inference using judgement post-stratified samples in finite populations 基于模型的推理，在有限种群中使用判断后分层样本
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-05-06 DOI: 10.1111/anzs.12320
Omer Ozturk, Konul Bayramoglu Kavlak
{"title":"Model-based inference using judgement post-stratified samples in finite populations","authors":"Omer Ozturk,&nbsp;Konul Bayramoglu Kavlak","doi":"10.1111/anzs.12320","DOIUrl":"10.1111/anzs.12320","url":null,"abstract":"<div>\u0000 \u0000 <p>In survey sampling studies, statistical inference can be constructed either using design based randomisation or super population model. Design-based inference using judgement post-stratified (JPS) sampling is available in the literature. This paper develops statistical inference based on super population model in a finite population setting using JPS sampling design. For a JPS sample, first a simple random sample (SRS) is constructed without replacement. The sample units in this SRS are then stratified based on judgement ranking in a small comparison set to induce a data structure in the sample. The paper shows that the mean of a JPS sample is model unbiased and has smaller mean square prediction error (MSPE) than the MSPE of a simple random sample mean. Using an unbiased estimator of the MSPE, the paper also constructs prediction confidence interval for the population mean. A small-scale empirical study shows that the JPS sample predictor performs better than an SRS predictor when the quality of ranking information in JPS sampling is not poor. The paper also shows that the coverage probabilities of prediction intervals are very close to the nominal coverage probability. Proposed inferential procedure is applied to a real data set obtained from an agricultural research farm.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 2","pages":"377-393"},"PeriodicalIF":1.1,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89597791","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: 1
Modelling columnarity of pyramidal cells in the human cerebral cortex 模拟人类大脑皮层锥体细胞的柱状结构
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-05-06 DOI: 10.1111/anzs.12321
Andreas Dyreborg Christoffersen, Jesper M⊘ller, Heidi S⊘gaard Christensen
{"title":"Modelling columnarity of pyramidal cells in the human cerebral cortex","authors":"Andreas Dyreborg Christoffersen,&nbsp;Jesper M⊘ller,&nbsp;Heidi S⊘gaard Christensen","doi":"10.1111/anzs.12321","DOIUrl":"10.1111/anzs.12321","url":null,"abstract":"<div>\u0000 \u0000 <p>For modelling the location of pyramidal cells in the human cerebral cortex, we suggest a hierarchical point process in that exhibits anisotropy in the form of cylinders extending along the <i>z</i>-axis. The model consists first of a generalised shot noise Cox process for the <i>xy</i>-coordinates, providing cylindrical clusters, and next of a Markov random field model for the <i>z</i>-coordinates conditioned on the <i>xy</i>-coordinates, providing either repulsion, aggregation or both within specified areas of interaction. Several cases of these hierarchical point processes are fitted to two pyramidal cell data sets, and of these a final model allowing for both repulsion and attraction between the points seem adequate. We discuss how the final model relates to the so-called minicolumn hypothesis in neuroscience.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 1","pages":"33-54"},"PeriodicalIF":1.1,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86876749","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: 7
Stereological inference on mean particle shape from vertical sections 垂直剖面平均粒子形状的立体推理
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-03-09 DOI: 10.1111/anzs.12309
Eva B. Vedel Jensen
{"title":"Stereological inference on mean particle shape from vertical sections","authors":"Eva B. Vedel Jensen","doi":"10.1111/anzs.12309","DOIUrl":"10.1111/anzs.12309","url":null,"abstract":"<div>\u0000 \u0000 <p>It was a major breakthrough when design-based stereological methods for <i>vertical sections</i> were developed by Adrian Baddeley and coworkers in the 1980s. Most importantly, it was shown how to estimate in a design-based fashion surface area from observations in random vertical sections with uniform position and uniform rotation around the vertical axis. The great practical importance of these developments is due to the fact that some biostructures can only be recognised on vertical sections. Later, local design-based estimation of mean particle volume from vertical sections was developed. In the present paper, we review these important advances in stereology. Quite recently, vertical sections have gained renewed interest, since it has been shown that mean particle shape can be estimated from such sections. These new developments are also reviewed in the present paper.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 1","pages":"6-18"},"PeriodicalIF":1.1,"publicationDate":"2021-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73552048","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: 2
Estimation of Poisson mean with under-reported counts: a double sampling approach 漏报计数的泊松均值估计:双重抽样方法
IF 1.1 4区 数学
Australian & New Zealand Journal of Statistics Pub Date : 2021-02-22 DOI: 10.1111/anzs.12308
Debjit Sengupta, Tathagata Banerjee, Surupa Roy
{"title":"Estimation of Poisson mean with under-reported counts: a double sampling approach","authors":"Debjit Sengupta,&nbsp;Tathagata Banerjee,&nbsp;Surupa Roy","doi":"10.1111/anzs.12308","DOIUrl":"10.1111/anzs.12308","url":null,"abstract":"<div>\u0000 \u0000 <p>Count data arising in various fields of applications are often under-reported. Ignoring undercount naturally leads to biased estimators and inaccurate confidence intervals. In the presence of undercount, in this paper, we develop likelihood-based methodologies for estimation of mean using validation data. The asymptotic distributions of the competing estimators of the mean are derived. The impact of ignoring undercount on the coverage and length of the confidence intervals is investigated using extensive numerical studies. Finally an analysis of heat mortality data is presented.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"62 4","pages":"508-535"},"PeriodicalIF":1.1,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/anzs.12308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81551963","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: 1
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信