Australian & New Zealand Journal of Statistics最新文献_第6页

A Festschrift for Geoff McLachlan 杰夫·麦克拉克伦的纪念

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-08-01 DOI: 10.1111/anzs.12372

Hien Nguyen, Sharon Lee, Florence Forbes

引用次数: 0

Bayesian hierarchical mixture models for detecting non-normal clusters applied to noisy genomic and environmental datasets 用于检测非正态聚类的贝叶斯层次混合模型应用于嘈杂的基因组和环境数据集

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-08-01 DOI: 10.1111/anzs.12370

Huizi Zhang, Ben Swallow, Mayetri Gupta

{"title":"Bayesian hierarchical mixture models for detecting non-normal clusters applied to noisy genomic and environmental datasets","authors":"Huizi Zhang, Ben Swallow, Mayetri Gupta","doi":"10.1111/anzs.12370","DOIUrl":"10.1111/anzs.12370","url":null,"abstract":"<p>Clustering to find subgroups with common features is often a necessary first step in the statistical modelling and analysis of large and complex datasets. Although follow-up analyses often make use of complex statistical models that are appropriate for the specific application, most popular clustering approaches are either nonparametric, or based on Gaussian mixture models and their variants, often for reasons of computational efficiency. Certain characteristics in the data, such as the presence of outliers, or non-ellipsoidal cluster shapes, that are common in modern scientific datasets, often lead these methods to fail to detect the cluster components accurately. In this article, we present two efficient and robust Bayesian clustering approaches that seek to overcome these limitations—a model-based ‘tight’ clustering approach to cluster points in the presence of outliers, and a hierarchical Laplace mixture-based approach to cluster heavy-tailed and otherwise non-normal cluster components—and illustrate their power and accuracy in detecting meaningful clusters in datasets from genomics, imaging and the environmental sciences.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"64 2","pages":"313-337"},"PeriodicalIF":1.1,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83368306","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: 1

Bayesian non-parametric spatial prior for traffic crash risk mapping: A case study of Victoria, Australia 交通碰撞风险映射的贝叶斯非参数空间先验:以澳大利亚维多利亚州为例

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-07-06 DOI: 10.1111/anzs.12369

J.-B. Durand, F. Forbes, C.D. Phan, L. Truong, H.D. Nguyen, F. Dama

引用次数: 1

Visualising the pattern of long-term genotype performance by leveraging a genomic prediction model 利用基因组预测模型可视化长期基因型表现模式

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-05-03 DOI: 10.1111/anzs.12362

Vivi N. Arief, Ian H. DeLacy, Thomas Payne, Kaye E. Basford

{"title":"Visualising the pattern of long-term genotype performance by leveraging a genomic prediction model","authors":"Vivi N. Arief, Ian H. DeLacy, Thomas Payne, Kaye E. Basford","doi":"10.1111/anzs.12362","DOIUrl":"10.1111/anzs.12362","url":null,"abstract":"<p>Historical data from plant breeding programs provide valuable resources to study the response of genotypes to the changing environment (i.e. genotype-by-environment interaction). Such data have been used to evaluate the pattern of genotype performance across regions or locations, but its use to evaluate the long-term pattern of genotype performance across environments (i.e. locations-by-years) has been hampered by the lack of common genotypes across years. This lack of common genotypes is due to the structure of the breeding program, especially for annual crops, where only a proportion of selected genotypes are tested in subsequent years. This has resulted in a sparse prediction of the performance of genotypes across years (i.e. a genotype-by-year table). A genomic prediction method that fitted both a relationship matrix among genotypes and a relationship matrix among environments (i.e. years) could overcome this limitation and produce a dense genotype-by-year table, thereby enabling some evaluation of long-term genotype performance. In this paper, we applied the genomic prediction model to the yield data from CIMMYT's Elite Spring Wheat Yield Trials (ESWYT) to visualise the pattern of genotype performance over 25 years.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"64 2","pages":"297-312"},"PeriodicalIF":1.1,"publicationDate":"2022-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72714347","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: 1

Functional dimension reduction based on fuzzy partition and transformation 基于模糊划分和变换的功能降维

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-04-25 DOI: 10.1111/anzs.12363

Beiting Liang, Taoxuan Gao, Defa Bai, Guochang Wang

{"title":"Functional dimension reduction based on fuzzy partition and transformation","authors":"Beiting Liang, Taoxuan Gao, Defa Bai, Guochang Wang","doi":"10.1111/anzs.12363","DOIUrl":"10.1111/anzs.12363","url":null,"abstract":"<div>\u0000 \u0000 <p>Functional sliced inverse regression (FSIR) is the among most popular methods for the functional dimension reduction. However, FSIR has two evident shortcomings. On the one hand, the number of samples in each slice must not be too small and selecting a suitable <i>S</i> is difficult, particularly for data with small sample size, where <i>S</i> indicates the number of slices. On the other hand, FSIR and its related methods are well-known for their poor performance when the link function is an even (or symmetric) dependency. To solve these two problems, we propose three new types of estimation methods. First, we propose the functional fuzzy inverse regression (FFIR) method based on a fuzzy partition. Compared with FSIR that uses a hard partition, the fuzzy partition uses all samples with different weights to estimate the mean in each slice. Therefore, FFIR exhibits good performance even for data with small sample size. Second, we suggest two transformation approaches, namely, FSIRR and FSIRP, avoiding the symmetric dependency between the response and the predictor. FSIRR eliminates the symmetric dependency by transforming the response variable, while FSIRP overcomes the symmetric dependency by transforming the functional predictor. Third, we propose the FFIRR and FFIRP methods by combining the advantages of FFIR and two transformation methods. FFIRR and FFIRP replace the FSIR method on the transformation data via FFIR. Simulation and real data analysis show that three types of proposed methods exhibit better performance than FSIR in terms of the estimation accuracy and stability.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"64 1","pages":"45-66"},"PeriodicalIF":1.1,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81694850","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

Smooth tests of goodness of fit for the distributional assumption of regression models 回归模型分布假设拟合优度的平滑检验

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-04-18 DOI: 10.1111/anzs.12361

J. C. W. Rayner, Paul Rippon, Thomas Suesse, Olivier Thas

引用次数: 1

Modal clustering on PPGMMGA projection subspace PPGMMGA投影子空间上的模态聚类

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-04-14 DOI: 10.1111/anzs.12360

Luca Scrucca

引用次数: 1

MPS: An R package for modelling shifted families of distributions MPS:一个R软件包，用于建模移位的分布族

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-04-14 DOI: 10.1111/anzs.12359

Mahdi Teimouri, Saralees Nadarajah

{"title":"MPS: An R package for modelling shifted families of distributions","authors":"Mahdi Teimouri, Saralees Nadarajah","doi":"10.1111/anzs.12359","DOIUrl":"10.1111/anzs.12359","url":null,"abstract":"<div>\u0000 \u0000 <p>Generalised statistical distributions have been widely used over the last decades for modelling phenomena in different fields. The generalisations have been made to produce distributions with more flexibility and lead to more accurate modelling in practice. Statistical analysis of the generalised distributions requires new statistical packages. The <span>Newdistns</span> package due to Nadarajah and Rocha provides <span>R</span> routines with functionality to compute probability density function (PDF), cumulative distribution function (CDF), quantile function, random numbers and parameter estimates of 19 families of distributions with applications in survival analysis. Here, we introduce an <span>R</span> package, called <span>MPS</span>, for computing PDF, CDF, quantile function, random numbers, Q–Q plots and parameter estimates for 24 shifted new families of distributions. By considering an extra location parameter, each family will be defined on the whole real line and so covers a broader range of applicability. We adopt the well-known maximum product spacing approach to estimate parameters of the families because under some situations the maximum likelihood (ML) estimators fail to exist. We demonstrate <span>MPS</span> by analysing two well-known real data sets. For the first data set, the ML estimators break down, but <span>MPS</span> works well. For the second set, adding a location parameter results in a reasonable model while the absence of the location parameter makes the model quite inappropriate. The <span>MPS</span> is available from CRAN at https://cran.r-project.org/package=MPS.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"64 1","pages":"86-108"},"PeriodicalIF":1.1,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84200205","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

Fast and efficient algorithms for sparse semiparametric bifunctional regression 稀疏半参数双泛函回归的快速有效算法

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-03-08 DOI: 10.1111/anzs.12355

Silvia Novo, Philippe Vieu, Germán Aneiros

{"title":"Fast and efficient algorithms for sparse semiparametric bifunctional regression","authors":"Silvia Novo, Philippe Vieu, Germán Aneiros","doi":"10.1111/anzs.12355","DOIUrl":"10.1111/anzs.12355","url":null,"abstract":"<div>\u0000 \u0000 <p>A new sparse semiparametric model is proposed, which incorporates the influence of two functional random variables in a scalar response in a flexible and interpretable manner. One of the functional covariates is included through a single-index structure, while the other is included linearly through the high-dimensional vector formed by its discretised observations. For this model, two new algorithms are presented for selecting relevant variables in the linear part and estimating the model. Both procedures utilise the functional origin of linear covariates. Finite sample experiments demonstrated the scope of application of both algorithms: the first method is a fast algorithm that provides a solution (without loss in predictive ability) for the significant computational time required by standard variable selection methods for estimating this model, and the second algorithm completes the set of relevant linear covariates provided by the first, thus improving its predictive efficiency. Some asymptotic results theoretically support both procedures. A real data application demonstrated the applicability of the presented methodology from a predictive perspective in terms of the interpretability of outputs and low computational cost.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 4","pages":"606-638"},"PeriodicalIF":1.1,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83590071","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

Bessel regression and bbreg package to analyse bounded data 贝塞尔回归和bbreg包分析有界数据

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-02-12 DOI: 10.1111/anzs.12354

Wagner Barreto-Souza, Vinícius D. Mayrink, Alexandre B. Simas

{"title":"Bessel regression and bbreg package to analyse bounded data","authors":"Wagner Barreto-Souza, Vinícius D. Mayrink, Alexandre B. Simas","doi":"10.1111/anzs.12354","DOIUrl":"10.1111/anzs.12354","url":null,"abstract":"<div>\u0000 \u0000 <p>Beta regression has been extensively used by statisticians and practitioners to model bounded continuous data without a strong competitor having the same main features. A class of normalised inverse-Gaussian (N-IG) process was introduced in the literature and has been explored in the Bayesian context as a powerful alternative to the Dirichlet process. Until this moment, no attention has been paid to the univariate N-IG distribution in the classical inference. In this paper, we propose the bessel regression based on the univariate N-IG distribution, which is an alternative to the beta model. The estimation of the parameters is done through an expectation–maximisation (EM) algorithm and the paper discusses how to perform inference. A useful and practical discrimination procedure is proposed for model selection between bessel and beta regressions. A new <span>R</span> package called <span>bbreg</span> is developed for fitting both bessel and beta regression models based on the EM-algorithm and further providing graphical tools for model adequacy and model selection as well. Proper documentation for this package is available. The performances of the models are evaluated under misspecification in a simulation study. An empirical illustration is explored to confront results from bessel and beta regressions by using the new <span>R</span> package <span>bbreg</span>.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"63 4","pages":"685-706"},"PeriodicalIF":1.1,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81092039","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