Australian & New Zealand Journal of Statistics最新文献_第4页

The multivariate component zero-inflated Poisson model for correlated count data analysis 用于相关计数数据分析的多元零膨胀泊松模型

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-08-27 DOI: 10.1111/anzs.12395

Qin Wu, Guo-Liang Tian, Tao Li, Man-Lai Tang, Chi Zhang

引用次数: 0

Short-term forecasting with a computationally efficient nonparametric transfer function model 一种计算高效的非参数传递函数模型的短期预测

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-08-01 DOI: 10.1111/anzs.12394

Jun. M. Liu

{"title":"Short-term forecasting with a computationally efficient nonparametric transfer function model","authors":"Jun. M. Liu","doi":"10.1111/anzs.12394","DOIUrl":"https://doi.org/10.1111/anzs.12394","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper a semi-parametric approach is developed to model non-linear relationships in time series data using polynomial splines. Polynomial splines require very little assumption about the functional form of the underlying relationship, so they are very flexible and can be used to model highly non-linear relationships. Polynomial splines are also computationally very efficient. The serial correlation in the data is accounted for by modelling the noise as an autoregressive integrated moving average (ARIMA) process, by doing so, the efficiency in nonparametric estimation is improved and correct inferences can be obtained. The explicit structure of the ARIMA model allows the correlation information to be used to improve forecasting performance. An algorithm is developed to automatically select and estimate the polynomial spline model and the ARIMA model through backfitting. This method is applied on a real-life data set to forecast hourly electricity usage. The non-linear effect of temperature on hourly electricity usage is allowed to be different at different hours of the day and days of the week. The forecasting performance of the developed method is evaluated in post-sample forecasting and compared with several well-accepted models. The results show the performance of the proposed model is comparable with a long short-term memory deep learning model.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 3","pages":"187-212"},"PeriodicalIF":1.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50114984","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

Asymptotics of M-estimator in multivariate linear regression models for a class of random errors 一类随机误差的多元线性回归模型中M-估计的渐近性

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-07-21 DOI: 10.1111/anzs.12393

Yi Wu, Wei Yu, Xuejun Wang

引用次数: 0

On the selection of predictors by using greedy algorithms and information theoretic criteria 利用贪婪算法和信息论准则选择预测因子

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-06-29 DOI: 10.1111/anzs.12387

Fangyao Li, Christopher M. Triggs, Ciprian Doru Giurcăneanu

{"title":"On the selection of predictors by using greedy algorithms and information theoretic criteria","authors":"Fangyao Li, Christopher M. Triggs, Ciprian Doru Giurcăneanu","doi":"10.1111/anzs.12387","DOIUrl":"https://doi.org/10.1111/anzs.12387","url":null,"abstract":"<p>We discuss the use of the following greedy algorithms in the prediction of multivariate time series: Matching Pursuit Algorithm (MPA), Orthogonal Matching Pursuit (OMP), Relaxed Matching Pursuit (RMP), Frank–Wolfe Algorithm (FWA) and Constrained Matching Pursuit (CMP). The last two are known to be solvers for the lasso problem. Some of the algorithms are well-known (e.g. OMP), while others are less popular (e.g. RMP). We provide a unified presentation of all the algorithms, and evaluate their computational complexity for the high-dimensional case and for the big data case. We show how 12 information theoretic (IT) criteria can be used jointly with the greedy algorithms. As part of this effort, we derive new theoretical results that allow modification of the IT criteria such that to be compatible with RMP. The prediction capabilities are tested in experiments with two data sets. The first one involves air pollution data measured in Auckland (New Zealand) and the second one concerns the House Price Index in England (the United Kingdom).</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 2","pages":"77-100"},"PeriodicalIF":1.1,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50155532","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

Visual assessment of matrix-variate normality 矩阵变量正态性的可视化评估

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-06-17 DOI: 10.1111/anzs.12388

Nikola Počuča, Michael P.B. Gallaugher, Katharine M. Clark, Paul D. McNicholas

引用次数: 1

Robust PCA for high-dimensional data based on characteristic transformation 基于特征变换的高维数据鲁棒主成分分析

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-06-13 DOI: 10.1111/anzs.12385

Lingyu He, Yanrong Yang, Bo Zhang

{"title":"Robust PCA for high-dimensional data based on characteristic transformation","authors":"Lingyu He, Yanrong Yang, Bo Zhang","doi":"10.1111/anzs.12385","DOIUrl":"https://doi.org/10.1111/anzs.12385","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we propose a novel robust principal component analysis (PCA) for high-dimensional data in the presence of various heterogeneities, in particular strong tailing and outliers. A transformation motivated by the characteristic function is constructed to improve the robustness of the classical PCA. The suggested method has the distinct advantage of dealing with heavy-tail-distributed data, whose covariances may be non-existent (positively infinite, for instance), in addition to the usual outliers. The proposed approach is also a case of kernel principal component analysis (KPCA) and employs the robust and non-linear properties via a bounded and non-linear kernel function. The merits of the new method are illustrated by some statistical properties, including the upper bound of the excess error and the behaviour of the large eigenvalues under a spiked covariance model. Additionally, using a variety of simulations, we demonstrate the benefits of our approach over the classical PCA. Finally, using data on protein expression in mice of various genotypes in a biological study, we apply the novel robust PCA to categorise the mice and find that our approach is more effective at identifying abnormal mice than the classical PCA.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 2","pages":"127-151"},"PeriodicalIF":1.1,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150434","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

Bayesian neural tree models for nonparametric regression 非参数回归的贝叶斯神经树模型

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-06-12 DOI: 10.1111/anzs.12386

Tanujit Chakraborty, Gauri Kamat, Ashis Kumar Chakraborty

{"title":"Bayesian neural tree models for nonparametric regression","authors":"Tanujit Chakraborty, Gauri Kamat, Ashis Kumar Chakraborty","doi":"10.1111/anzs.12386","DOIUrl":"https://doi.org/10.1111/anzs.12386","url":null,"abstract":"<div>\u0000 \u0000 <p>Frequentist and Bayesian methods differ in many aspects but share some basic optimal properties. In real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable depending on some subjective criteria. Nonparametric classification and regression techniques, such as decision trees and neural networks, have both frequentist (classification and regression trees (CARTs) and artificial neural networks) as well as Bayesian counterparts (Bayesian CART and Bayesian neural networks) to learning from data. In this paper, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. BNT models can simultaneously perform feature selection and prediction, are highly flexible, and generalise well in settings with limited training observations. We study the statistical consistency of the proposed approaches and derive the optimal value of a vital model parameter. The excellent performance of the newly proposed BNT models is shown using simulation studies. We also provide some illustrative examples using a wide variety of standard regression datasets from a public available machine learning repository to show the superiority of the proposed models in comparison to popularly used Bayesian CART and Bayesian neural network models.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 2","pages":"101-126"},"PeriodicalIF":1.1,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50139340","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

A nonparametric mixture approach to density and null proportion estimation in large-scale multiple comparison problems 大规模多重比较问题中密度和零比估计的非参数混合方法

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-04-04 DOI: 10.1111/anzs.12383

Xiangjie Xue, Yong Wang

引用次数: 0

A method to reduce the width of confidence intervals by using a normal scores transformation 一种利用正态分数变换减小置信区间宽度的方法

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-03-17 DOI: 10.1111/anzs.12384

T. W. O’Gorman

{"title":"A method to reduce the width of confidence intervals by using a normal scores transformation","authors":"T. W. O’Gorman","doi":"10.1111/anzs.12384","DOIUrl":"https://doi.org/10.1111/anzs.12384","url":null,"abstract":"<div>\u0000 \u0000 <p>In stating the results of their research, scientists usually want to publish narrow confidence intervals because they give precise estimates of the effects of interest. In many cases, the researcher would want to use the narrowest interval that maintains the desired coverage probability. In this manuscript, we propose a new method of finding confidence intervals that are often narrower than traditional confidence intervals for any individual parameter in a linear model if the errors are from a skewed distribution or from a long-tailed symmetric distribution. If the errors are normally distributed, we show that the width of the proposed normal scores confidence interval will not be much greater than the width of the traditional interval. If the dataset includes predictor variables that are uncorrelated or moderately correlated then the confidence intervals will maintain their coverage probability. However, if the covariates are highly correlated, then the coverage probability of the proposed confidence interval may be slightly lower than the nominal value. The procedure is not computationally intensive and an R program is available to determine the normal scores 95% confidence interval. Whenever the covariates are not highly correlated, the normal scores confidence interval is recommended for the analysis of datasets having 50 or more observations.</p>\u0000 </div>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"65 1","pages":"35-48"},"PeriodicalIF":1.1,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50136144","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

Variable selection in heterogeneous panel data models with cross-sectional dependence 具有截面相关性的异质面板数据模型中的变量选择

IF 1.1 4区数学

Australian & New Zealand Journal of Statistics Pub Date : 2023-02-15 DOI: 10.1111/anzs.12381

Xiaoling Mei, Bin Peng, Huanjun Zhu

引用次数: 0