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

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 1.1 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: 1

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

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":"https://doi.org/10.1111/anzs.12400","url":null,"abstract":"Summary 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.","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"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

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":null,"pages":null},"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":null,"pages":null},"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

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":null,"pages":null},"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":null,"pages":null},"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