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Unified robust estimation Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2024-02-21 Zhu Wang
SummaryRobust estimation is primarily concerned with providing reliable parameter estimates in the presence of outliers. Numerous robust loss functions have been proposed in regression and classification, along with various computing algorithms. In modern penalised generalised linear models (GLMs), however, there is limited research on robust estimation that can provide weights to determine the outlier
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Asymptotics for the conditional self-weighted M estimator of GRCA(p) models and its statistical inference Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2024-02-21 Chi Yao, Wei Yu, Xuejun Wang
Under the p$$ p $$-order generalised random coefficient autoregressive (GRCA(p$$ p $$)) model with random coefficients Φt,$$ {\boldsymbol{\Phi}}_t, $$ we propose a conditional self-weighted M$$ M $$ estimator of EΦt$$ \mathrm{E}{\boldsymbol{\Phi}}_t $$. We investigate the asymptotic normality of this estimator with possibly heavy-tailed random variables. Furthermore, a Wald test statistic is constructed
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Latent heterogeneity in COVID-19 hospitalisations: a cluster-weighted approach to analyse mortality Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2024-02-13 Paolo Berta, Salvatore Ingrassia, Giorgio Vittadini, Daniele Spinelli
The COVID-19 pandemic caused an unprecedented excess mortality. Since 2020, many studies have focussed on the characteristics of COVID-19 patients who did not survive. From the statistical point of view, what seems to dominate is the large heterogeneity of the populations affected by COVID-19 and the extreme difficulty in identifying subpopulations who died affected by a plurality of contemporary characteristics
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Identifying changes in the distribution of income from higher-order moments with an application to Australia Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2024-01-17 Vance L. Martin, Jialu Shi, Yang Song, Wenying Yao
Changes in the distribution of income over time are identified based on an adjusted two-sample version of the Neyman smooth test by using subsampling methods to approximate the sampling distribution of the test statistic when samples are not independent of each other. A range of Monte Carlo experiments show that the approach corrects for size distortions arising from dependent samples as well as generating
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A novel response model and target selection method with applications to marketing Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2024-01-18 Y. Cai
Response models used in marketing are not always constructed for later marketing optimisation, which often results in unsatisfactory results in target selection for future marketing activities. To solve this problem, we develop a new binary response model and a new marketing target selection method. The proposed model can predict multiple propensity scores per customer through customer-specific propensity
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Exact testing for heteroscedasticity in a two-way layout in variety frost trials when incorporating a covariate Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2024-01-01 Angelika A. Pilkington, Brenton R. Clarke, Dean A. Diepeveen
Two-way layouts are common in grain industry research where it is often the case that there are one or more covariates. It is widely recognised that when estimating fixed effect parameters, one should also examine for possible extra error variance structure. An exact test for heteroscedasticity, when there is a covariate, is illustrated for a data set from frost trials in Western Australia. While the
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Exact likelihoods for N-mixture models with time-to-detection data Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-12-11 Linda M. Haines, Res Altwegg, D. L. Borchers
This paper is concerned with the formulation of N$$ N $$-mixture models for estimating the abundance and probability of detection of a species from binary response, count and time-to-detection data. A modelling framework, which encompasses time-to-first-detection within the context of detection/non-detection and time-to-each-detection and time-to-first-detection within the context of count data, is
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Model averaged tail area confidence intervals in nested linear regression models Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-12-07 Paul Kabaila, Ayesha Perera
The performance, in terms of coverage and expected length, of the model averaged tail area (MATA) confidence interval, proposed by Turek & Fletcher (2012, Computational Statistics & Data Analysis, 56, 2809–2815), depends greatly on the data-based model weights used in its construction. We generalise the computationally convenient exact formulae due to Kabaila, Welsh & Abeysekera (2016, Scandinavian
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The role of pairwise matching in experimental design for an incidence outcome Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-11-27 Adam Kapelner, Abba M. Krieger, David Azriel
We consider the problem of evaluating designs for a two-arm randomised experiment with an incidence (binary) outcome under a non-parametric general response model. Our two main results are that the a priori pair matching design is (1) the optimal design as measured by mean squared error among all block designs which includes complete randomisation. And (2), this pair-matching design is minimax, that
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Measurement errors in semi-parametric generalised regression models Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-10-11 Mohammad W. Hattab, David Ruppert
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
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Comparisons of distributions of Australian mental health scores Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-10-11 D. Gunawan, William E. Griffiths, D. Chotikapanich
Bayesian non-parametric estimates of Australian distributions of mental health scores are obtained to assess how the mental health status of the population has changed over time, and to compare the mental health status of female/male and Aboriginal/non-Aboriginal population subgroups. First-order and second-order stochastic dominance are used to compare distributions, with results presented in terms
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Embedding latent class regression and latent class distal outcome models into cluster-weighted latent class analysis: a detailed simulation experiment Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-09-22 Roberto Di Mari, Antonio Punzo, Zsuzsa Bakk
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
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Bayesian modelling of effects of prenatal alcohol exposure on child cognition based on data from multiple cohorts Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-09-08 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
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)
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The multivariate component zero-inflated Poisson model for correlated count data analysis Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-08-27 Qin Wu, Guo-Liang Tian, Tao Li, Man-Lai Tang, Chi Zhang
Multivariate zero-inflated Poisson (ZIP) distributions are important tools for modelling and analysing correlated count data with extra zeros. Unfortunately, existing multivariate ZIP distributions consider only the overall zero-inflation while the component zero-inflation is not well addressed. This paper proposes a flexible multivariate ZIP distribution, called the multivariate component ZIP distribution
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Short-term forecasting with a computationally efficient nonparametric transfer function model Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-08-01 Jun. M. Liu
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
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Asymptotics of M-estimator in multivariate linear regression models for a class of random errors Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-07-21 Yi Wu, Wei Yu, Xuejun Wang
It is known that linear regression models have immense applications in various areas such as engineering technology, economics and social sciences. In this paper, we investigate the asymptotic properties of M-estimator in multivariate linear regression model based on a class of random errors satisfying a generalised Bernstein-type inequality. By using the generalised Bernstein-type inequality, we obtain
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On the selection of predictors by using greedy algorithms and information theoretic criteria Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-06-29 Fangyao Li, Christopher M. Triggs, Ciprian Doru Giurcăneanu
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
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Visual assessment of matrix-variate normality Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-06-17 Nikola Počuča, Michael P.B. Gallaugher, Katharine M. Clark, Paul D. McNicholas
In recent years, the analysis of three-way data has become ever more prevalent in the literature. It is becoming increasingly common to analyse such data by means of matrix-variate distributions, the most prevalent of which is the matrix-variate normal distribution. Although many methods exist for assessing multivariate normality, there is a relative paucity of approaches for assessing matrix-variate
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Robust PCA for high-dimensional data based on characteristic transformation Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-06-13 Lingyu He, Yanrong Yang, Bo Zhang
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
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Bayesian neural tree models for nonparametric regression Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-06-12 Tanujit Chakraborty, Gauri Kamat, Ashis Kumar Chakraborty
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
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A nonparametric mixture approach to density and null proportion estimation in large-scale multiple comparison problems Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-04-04 Xiangjie Xue, Yong Wang
A new method for estimating the proportion of null effects is proposed for solving large-scale multiple comparison problems. It utilises maximum likelihood estimation of nonparametric mixtures, which also provides a density estimate of the test statistics. It overcomes the problem of the usual nonparametric maximum likelihood estimator that cannot produce a positive probability at the location of null
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A method to reduce the width of confidence intervals by using a normal scores transformation Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-03-17 T. W. O’Gorman
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
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Variable selection in heterogeneous panel data models with cross-sectional dependence Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-02-15 Xiaoling Mei, Bin Peng, Huanjun Zhu
This paper studies the Bridge estimator for a high-dimensional panel data model with heterogeneous varying coefficients, where the random errors are assumed to be serially correlated and cross-sectionally dependent. We establish oracle efficiency and the asymptotic distribution of the Bridge estimator, when the number of covariates increases to infinity with the sample size in both dimensions. A BIC-type
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On two conjectures about perturbations of the stochastic growth rate Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-02-15 Stefano Giaimo
The stochastic growth rate describes long-run growth of a population that lives in a fluctuating environment. Perturbation analysis of the stochastic growth rate provides crucial information for population managers, ecologists and evolutionary biologists. This analysis quantifies the response of the stochastic growth rate to changes in demographic parameters. A form of this analysis deals with changes
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A Richards growth model to predict fruit weight Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2023-01-05 Daniel Gerhard, Elena Moltchanova
The Richards model comprises several popular sigmoidal and monomolecular growth curves. We illustrate fitting of a Bayesian Richards model by splitting the full growth model into several submodels, followed by a model selection procedure. The performance of the methodology is evaluated by Monte Carlo simulations. A double-sigmoidal version of the Richards model is applied to model grape bunch weight
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A new minification integer-valued autoregressive process driven by explanatory variables Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-12-28 Lianyong Qian, Fukang Zhu
The discrete minification model based on the modified negative binomial operator, as an extension to the continuous minification model, can be used to describe an extreme value after few increasing values. To make this model more practical and flexible, a new minification integer-valued autoregressive process driven by explanatory variables is proposed. Ergodicity of the new process is discussed. The
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Minimum cost-compression risk in principal component analysis Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-12-28 Bhargab Chattopadhyay, Swarnali Banerjee
Principal Component Analysis (PCA) is a popular multivariate analytic tool which can be used for dimension reduction without losing much information. Data vectors containing a large number of features arriving sequentially may be correlated with each other. An effective algorithm for such situations is online PCA. Existing Online PCA research works revolve around proposing efficient scalable updating
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Permutation entropy and its variants for measuring temporal dependence Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-12-08 Xin Huang, Han Lin Shang, David Pitt
Permutation entropy (PE) is an ordinal-based non-parametric complexity measure for studying the temporal dependence structure in a linear or non-linear time series. Based on the PE, we propose a new measure, namely permutation dependence (PD), to quantify the strength of the temporal dependence in a univariate time series and remedy the major drawbacks of PE. We demonstrate that the PE and PD are viable
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Small area estimation under a semi-parametric covariate measured with error Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-12-08 Reyhane Sefidkar, Mahmoud Torabi, Amir Kavousi
In recent years, small area estimation has played an important role in statistics as it deals with the problem of obtaining reliable estimates for parameters of interest in areas with small or even zero sample sizes corresponding to population sizes. Nested error linear regression models are often used in small area estimation assuming that the covariates are measured without error and also the relationship
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The place of probability distributions in statistical learning. A commented book review of ‘Distributions for modeling location, scale, and shape using GAMLSS in R’ by Rigby et al. (2021) Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-09-23 Fernando Marmolejo-Ramos, Raydonal Ospina, Freddy Hernández-Barajas
Generalised additive models for location, scale and shape (GAMLSS) is a type of distributional regression framework that enables modelling numeric dependent variables via probability distributions other than those of the exponential family. While the cogs behind GAMLSS are provided in Stasinopoulos et al. 2017's book ‘Flexible regression and smoothing using GAMLSS in R, the new book by Rigby et al
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Penalised, post-pretest, and post-shrinkage strategies in nonlinear growth models Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-09-04 Janjira Piladaeng, S. Ejaz Ahmed, Supranee Lisawadi
In nonlinear growth models, we considered the parameter estimation under subspace information for low-dimensional and high-dimensional data. We proposed novel estimators based on pretest and shrinkage strategies to improve the estimation efficiency and to establish asymptotic properties. We used simulation studies and a real data example to confirm the theoretical results. We also applied two well-known
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Robust subtractive stability measures for fast and exhaustive feature importance ranking and selection in generalised linear models Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-09-02 Connor Smith, Boris Guennewig, Samuel Muller
We introduce the relatively new concept of subtractive lack-of-fit measures in the context of robust regression, in particular in generalised linear models. We devise a fast and robust feature selection framework for regression that empirically enjoys better performance than other selection methods while remaining computationally feasible when fully exhaustive methods are not. Our method builds on
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Multivariate Kruskal_Wallis tests based on principal component score and latent source of independent component analysis Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-08-04 Amitava Mukherjee, Hidetoshi Murakami
Analysing multivariate and high_dimensional multi_sample data is essential in many scientific fields. One of the most crucial and popular topics in modern nonparametric statistics is multi_sample comparison problems for such multivariate and high_dimensional data. The Kruskal_Wallis test is widely used in the multi_sample problem. For multivariate or high_dimensional data, it is imperative to specify
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A Festschrift for Geoff McLachlan Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-08-01 Hien Nguyen, Sharon Lee, Florence Forbes
This article introduces a special issue of the Australian and New Zealand Journal of Statistics, dedicated as a Festschrift for Geoff McLachlan on the occasion of his 75th birthday.
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Bayesian hierarchical mixture models for detecting non-normal clusters applied to noisy genomic and environmental datasets Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-08-01 Huizi Zhang, Ben Swallow, Mayetri Gupta
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
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Bayesian non-parametric spatial prior for traffic crash risk mapping: A case study of Victoria, Australia Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-07-06 J.-B. Durand, F. Forbes, C.D. Phan, L. Truong, H.D. Nguyen, F. Dama
We develop a Bayesian non-parametric (BNP) model coupled with Markov random fields (MRFs) for risk mapping, to infer homogeneous spatial regions in terms of risks. In contrast to most existing methods, the proposed approach does not require an arbitrary commitment to a specified number of risk classes and determines their risk levels automatically. We consider settings in which the relevant information
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Visualising the pattern of long-term genotype performance by leveraging a genomic prediction model Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-05-03 Vivi N. Arief, Ian H. DeLacy, Thomas Payne, Kaye E. Basford
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
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Functional dimension reduction based on fuzzy partition and transformation Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-04-25 Beiting Liang, Taoxuan Gao, Defa Bai, Guochang Wang
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 S is difficult, particularly for data with small sample size, where S indicates the number of slices. On the other hand, FSIR and its related
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Erratum Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-04-27
In the article by Williams et al., ‘Experimental design in practice: The importance of blocking and treatment structures’, first published on 08 November 2021. The supplemental material file was not added. The link to the Supplementary File has now been added to the article. We apologise for this error.
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Smooth tests of goodness of fit for the distributional assumption of regression models Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-04-18 J. C. W. Rayner, Paul Rippon, Thomas Suesse, Olivier Thas
We focus on regression models that consist of (i) a model for the conditional mean of the outcome and (ii) a distributional assumption about the distribution of the outcome, both conditional on the regressors. Generalised linear models form a well-known example. The choice of the outcome distribution is often motivated by prior or background knowledge of the researcher, or it is simply chosen for convenience
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MPS: An R package for modelling shifted families of distributions Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-04-14 Mahdi Teimouri, Saralees Nadarajah
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
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Modal clustering on PPGMMGA projection subspace Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-04-14 Luca Scrucca
PPGMMGA is a projection pursuit (PP) algorithm aimed at detecting and visualising clustering structures in multivariate data. The algorithm uses the negentropy as PP index obtained by fitting Gaussian mixture models (GMMs) for density estimation and, then, exploits genetic algorithms (GAs) for its optimisation. Since the PPGMMGA algorithm is a dimension reduction technique specifically introduced for
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Fast and efficient algorithms for sparse semiparametric bifunctional regression Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-03-08 Silvia Novo, Philippe Vieu, Germán Aneiros
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
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Bessel regression and bbreg package to analyse bounded data Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-02-12 Wagner Barreto-Souza, Vinícius D. Mayrink, Alexandre B. Simas
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
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Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-02-10 Jan Greve, Bettina Grün, Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter
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
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Modelling students’ career indicators via mixtures of parsimonious matrix-normal distributions Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-02-10 Salvatore D. Tomarchio, Salvatore Ingrassia, Volodymyr Melnykov
The evaluation of the teaching efficiency, under different points of view, is an important aspect for the university system because it helps managers to improve more and more the quality of the education and helps students to achieve strong professional skills. In this framework, students’ careers as well as teachers’ qualification and quantity adequacy indicators are analysed based on data sets provided
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Properties of the affine-invariant ensemble sampler's ‘stretch move’ in high dimensions Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-02-02 David Huijser, Jesse Goodman, Brendon J. Brewer
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
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Variable selection and debiased estimation for single-index expectile model Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-02-02 Rong Jiang, Yexun Peng, Yufei Deng
This article develops a penalised asymmetric least squares estimator for single-index expectile model. The oracle property of the proposed estimator is established. Moreover, the debiasing technique is used to construct an estimator that is asymptotically normal, which enables the construction of valid confidence intervals and hypothesis testing. Simulation studies and one real data application are
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Efficient estimation of partially linear tail index models using B-splines Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-02-02 Yaolan Ma, Bo Wei
The tail index is an important parameter in extreme value theory. In this paper, we consider a simple yet flexible spline estimation method for partially linear tail index models. We approximate the unknown function by B-splines and construct an approximate log-likelihood function to estimate the coefficients of the linear covariates and the B-spline basis functions. Consistency and asymptotic normality
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Global implicit function theorems and the online expectation–maximisation algorithm Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-01-24 Hien Duy Nguyen, Florence Forbes
The expectation–maximisation (EM) algorithm framework is an important tool for statistical computation. Due to the changing nature of data, online and mini-batch variants of EM and EM-like algorithms have become increasingly popular. The consistency of the estimator sequences that are produced by these EM variants often rely on an assumption regarding the continuous differentiability of a parameter
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Sufficient dimension reduction for clustered data via finite mixture modelling Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-01-22 F.K.C. Hui, L.H. Nghiem
Sufficient dimension reduction (SDR) is an attractive approach to regression modelling. However, despite its rich literature and growing popularity in application, surprisingly little research has been done on how to perform SDR for clustered data, for example as is commonly arises in longitudinal studies. Indeed, current popular SDR methods have been mostly based on a marginal estimating equation
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Bayesian credible intervals for population attributable risk from case–control, cohort and cross-sectional studies Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-01-17 Sarah Pirikahu, Geoffrey Jones, Martin L. Hazelton
Population attributable risk (PAR) and population attributable fraction (PAF) are used in epidemiology to predict the impact of removing a risk factor from the population. Until recently, no standard approach for calculating confidence intervals or the variance for PAR in particular was available in the literature. Previously we outlined a fully Bayesian approach to provide credible intervals for the
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Measuring the values of cricket players Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-01-15 Pranjal Chandrakar, Shubhabrata Das
Sports franchises that participate in team sports can make better decisions regarding their players’ financial compensation, renewal of the contracts, bidding strategies during the auction, etc., if they can adequately assess the value or worth of their players. Evaluating the value of a player in a team sport is difficult because various team members play different roles. In this study, we resolve
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Detection boundary for a sparse gamma scale mixture model Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2022-01-11 Michael I. Stewart
We derive the detection boundary for the one-sided version of the gamma scale mixture model where the contaminating component has a larger mean than the known reference distribution. We also derive an adaptive test which is able to almost uniformly attain the best possible performance in terms of detection of local alternatives.
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Odds-symmetry model for cumulative probabilities and decomposition of a conditional symmetry model in square contingency tables Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2021-12-06 Shuji Ando
For the analysis of square contingency tables, it is necessary to estimate an unknown distribution with high confidence from an obtained observation. For that purpose, we need to introduce a statistical model that fits the data well and has parsimony. This study proposes asymmetry models based on cumulative probabilities for square contingency tables with the same row and column ordinal classifications
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Proportional inverse Gaussian distribution: A new tool for analysing continuous proportional data Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2021-11-23 Pengyi Liu, Guo-Liang Tian, Kam Chuen Yuen, Chi Zhang, Man-Lai Tang
Outcomes in the form of rates, fractions, proportions and percentages often appear in various fields. Existing beta and simplex distributions are frequently unable to exhibit satisfactory performances in fitting such continuous data. This paper aims to develop the normalised inverse Gaussian (N-IG) distribution proposed by Lijoi, Mena & Prünster (2005, Journal of the American Statistical Association
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BNPdensity: Bayesian nonparametric mixture modelling in R Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2021-11-17 J. Arbel, G. Kon Kam King, A. Lijoi, L. Nieto-Barajas, I. Prünster
Robust statistical data modelling under potential model mis-specification often requires leaving the parametric world for the nonparametric. In the latter, parameters are infinite dimensional objects such as functions, probability distributions or infinite vectors. In the Bayesian nonparametric approach, prior distributions are designed for these parameters, which provide a handle to manage the complexity
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Experimental design in practice: The importance of blocking and treatment structures Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2021-11-08 E.R. Williams, C.G. Forde, J. Imaki, K. Oelkers
Experimental design and analysis has evolved substantially over the last 100 years, driven to a large extent by the power and availability of the computer. To demonstrate this development and encourage the use of experimental design in practice, three experiments from different research areas are presented. In these examples multiple blocking factors have been employed and they show how extraneous
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Accelerating adaptation in the adaptive Metropolis–Hastings random walk algorithm Aust. N. Z. J. Stat. (IF 1.1) Pub Date : 2021-11-03 Simon E.F. Spencer
The Metropolis–Hastings random walk algorithm remains popular with practitioners due to the wide variety of situations in which it can be successfully applied and the extreme ease with which it can be implemented. Adaptive versions of the algorithm use information from the early iterations of the Markov chain to improve the efficiency of the proposal. The aim of this paper is to reduce the number of