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

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 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 Newdistns package due to Nadarajah and Rocha provides R 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 R package, called MPS, 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 MPS by analysing two well-known real data sets. For the first data set, the ML estimators break down, but MPS 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 MPS is available from CRAN at https://cran.r-project.org/package=MPS.\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":null,"pages":null},"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 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.\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":null,"pages":null},"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 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 R package called bbreg 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 R package bbreg.\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":null,"pages":null},"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

Modelling students’ career indicators via mixtures of parsimonious matrix-normal distributions 通过简洁矩阵-正态分布的混合模型对学生的职业指标进行建模

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-02-10 DOI: 10.1111/anzs.12351

Salvatore D. Tomarchio, Salvatore Ingrassia, Volodymyr Melnykov

引用次数: 2

Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis 监视贝叶斯聚类分析中数据簇数的先验性和分区分布

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-02-10 DOI: 10.1111/anzs.12350

Jan Greve, Bettina Grün, Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter

{"title":"Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis","authors":"Jan Greve, Bettina Grün, Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter","doi":"10.1111/anzs.12350","DOIUrl":"10.1111/anzs.12350","url":null,"abstract":"Cluster analysis aims at partitioning data into groups or clusters. In applications, it is common to deal with problems where the number of clusters is unknown. Bayesian mixture models employed in such applications usually specify a flexible prior that takes into account the uncertainty with respect to the number of clusters. However, a major empirical challenge involving the use of these models is in the characterisation of the induced prior on the partitions. This work introduces an approach to compute descriptive statistics of the prior on the partitions for three selected Bayesian mixture models developed in the areas of Bayesian finite mixtures and Bayesian nonparametrics. The proposed methodology involves computationally efficient enumeration of the prior on the number of clusters in-sample (termed as ‘data clusters’) and determining the first two prior moments of symmetric additive statistics characterising the partitions. The accompanying reference implementation is made available in the R package fipp. Finally, we illustrate the proposed methodology through comparisons and also discuss the implications for prior elicitation in applications.","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72830218","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: 11

Variable selection and debiased estimation for single-index expectile model 单指标期望模型的变量选择与去偏估计

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-02-02 DOI: 10.1111/anzs.12348

Rong Jiang, Yexun Peng, Yufei Deng

引用次数: 0

Efficient estimation of partially linear tail index models using B-splines 部分线性尾指数模型的b样条有效估计

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-02-02 DOI: 10.1111/anzs.12357

Yaolan Ma, Bo Wei

引用次数: 0

Properties of the affine-invariant ensemble sampler's ‘stretch move’ in high dimensions 高维仿射不变系综采样器“拉伸移动”的性质

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2022-02-02 DOI: 10.1111/anzs.12358

David Huijser, Jesse Goodman, Brendon J. Brewer

{"title":"Properties of the affine-invariant ensemble sampler's ‘stretch move’ in high dimensions","authors":"David Huijser, Jesse Goodman, Brendon J. Brewer","doi":"10.1111/anzs.12358","DOIUrl":"10.1111/anzs.12358","url":null,"abstract":"<div>\u0000 \u0000 We present theoretical and practical properties of the affine-invariant ensemble sampler Markov Chain Monte Carlo method. In high dimensions, the sampler's ‘stretch move’ has unusual and undesirable properties. We demonstrate this with an n-dimensional correlated Gaussian toy problem with a known mean and covariance structure, and a multivariate version of the Rosenbrock problem. Visual inspection of a trace plots suggests the burn-in period is short. Upon closer inspection, we discover the mean and the variance of the target distribution do not match the known values, and the chain takes a very long time to converge. This problem becomes severe as n increases beyond 50. We also applied different diagnostics adapted to be applicable to ensemble methods to determine any lack of convergence. The diagnostics include the Gelman–Rubin method, the Heidelberger–Welch test, the integrated autocorrelation and the acceptance rate. The trace plot of individual walkers appears to be useful as well. We therefore conclude that the stretch move should be used with caution in moderate to high dimensions. We also present some heuristic results explaining this behaviour.\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88726225","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: 6