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A Festschrift for Adrian Baddeley Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210721
Martin L. Hazelton, R. TurnerThis article introduces a special issue of the Australian and New Zealand Journal of Statistics, being a Festschrift for Adrian Baddeley on the occasion of his 65th birthday.

Dependent radius marks of Laguerre tessellations: a case study Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210721
Dietrich Stoyan, Viktor Beneš, Filip SeitlWe study a particular marked threedimensional point process sample that represents a Laguerre tessellation. It comes from a polycrystalline sample of aluminium alloy material. The ‘points’ are the cell generators while the ‘marks’ are radius marks that control the size and shape of the tessellation cells. Our statistical mark correlation analyses show that the marks of the sample are in clear and

Conditional intensity: A powerful tool for modelling and analysing point process data Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210706
Peter J. DiggleThe conditional intensity function of a spatial point process describes how the probability that a point of the process occurs ‘at’ a particular point in its carrier space depends on the realisation of the process in the remainder of the carrier space. Provided that the point process is simple, the conditional intensity determines all of the properties of the process, in particular its likelihood function

Globally intensityreweighted estimators for K and pair correlation functions Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210517
Thomas Shaw, Jesper M⊘ller, Rasmus Plenge WaagepetersenWe introduce new estimators of the inhomogeneous Kfunction and the pair correlation function of a spatial point process as well as the cross Kfunction and the cross pair correlation function of a bivariate spatial point process under the assumption of secondorder intensityreweighted stationarity. These estimators rely on a ‘global’ normalisation factor which depends on an aggregation of the intensity

What is the effective sample size of a spatial point process? Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210721
Ian W. Renner, David I. Warton, Francis K.C. HuiPoint process models are a natural approach for modelling data that arise as point events. In the case of Poisson counts, these may be fitted easily as a weighted Poisson regression. Point processes lack the notion of sample size. This is problematic for model selection, because various classical criteria such as the Bayesian information criterion (BIC) are a function of the sample size, n, and are

Anna Karenina and the two envelopes problem Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210721
R. D. GillThe Anna Karenina principle is named after the opening sentence in the eponymous novel: Happy families are all alike; every unhappy family is unhappy in its own way. The two envelopes problem (TEP) is a muchstudied paradox in probability theory, mathematical economics, logic and philosophy. Time and again a new analysis is published in which an author claims finally to explain what actually goes wrong

Modelling temporal genetic and spatiotemporal residual effects for highthroughput phenotyping data Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210720
A. P. Verbyla, J. De Faveri, D. M. Deery, G. J. RebetzkeHighthroughput phenomics data are being collected in both the laboratory and the field. The data are often collected at many time points and there may be spatial variation in the laboratory or field that impacts on the growth of the plants, and that may influence the traits of interest. Modelling the genetic effects is of primary interest in such studies, but these effects might be biased if nongenetic

Nonparametric depthbased tests for the multivariate location problem Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210629
Sakineh Dehghan, Mohammad Reza FaridrohaniIn this paper, using the notion of data depth, we describe two classes of affine invariant test statistics for the onesample location problem. The tests are implemented through the idea of permutation tests. The performance of the test against some competitors is investigated with an extensive simulation study. It is observed that the tests perform well when compared to their competitors for a wide

New moderation methods of higher school certificate assessments: a case study of the New South Wales practice Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210629
Yanlin ShiThe Higher School Certificate (HSC) is the credential awarded to secondary school students in New South Wales (NSW), Australia. This paper reviews the current moderation process of the HSC and introduces and compares a range of modern statistical methods. With a comprehensive analysis of the complete 2013â€“2016 HSC results, we show that the monotone spline regression with the Huber loss function consistently

Forecasting the oldage dependency ratio to determine a sustainable pension age Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210629
Rob J. Hyndman, Yijun Zeng, Han Lin ShangWe forecast the oldage dependency ratio for Australia under various pension age proposals, and estimate a pension age scheme that will provide a stable oldage dependency ratio at a specified level. Our approach involves a stochastic population forecasting method based on coherent functional data models for mortality, fertility and net migration, which we use to simulate the future agestructure of

A shared parameter mixture model for longitudinal income data with missing responses and zero rounding Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210617
Francis K.C. Hui, Howard D. BondellThe analysis of longitudinal income data is often made challenging for several reasons. For example, in a national Australian survey on income over time, a nonnegligible proportion of responses are missing, and it is believed the missingness mechanism is nonignorable. Also, there are a large number of reported zero incomes, some of which may be true zeros (corresponding to individuals who legitimately

Adversarial risk analysis for firstprice sealedbid auctions Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210601
Muhammad Ejaz, Chaitanya Joshi, Stephen JoeAdversarial risk analysis (ARA) is an upcoming methodology that is considered to have advantages over the traditional decisiontheoretic and gametheoretic approaches. ARA solutions for firstprice sealedbid (FPSB) auctions have been found but only under strong assumptions which make the model somewhat unrealistic. In this paper, we use ARA methodology to model FPSB auctions using more realistic assumptions

On distance based goodness of fit tests for missing data when missing occurs at random Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210530
Subhra Sankar Dhar, Ujjwal DasVarious nonparametric goodness of fit tests have already been investigated in the literature. However, those tests are rarely used in the case of missing observations. We here study the goodness of fit test for missing data based on Lp distances along with Kolmogorov–Smirnov and Cramer–vonMises distances when missingness occurs at random. The asymptotic distributions of the proposed test statistics

Bayesian decision rules to classification problems Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210524
Yuqi Long, Xingzhong XuIn this paper, we analysed classification rules under Bayesian decision theory. The setup we considered here is fairly general, which can represent all possible parametric models. The Bayes classification rule we investigated minimises the Bayes risk under general loss functions. Among the existing literatures, the 01 loss function appears most frequently, under which the Bayes classification rule

A few statistical principles for data science Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210508
Noel CressieIn any other circumstance, it might make sense to define the extent of the terrain (Data Science) first, and then locate and describe the landmarks (Principles). But this data revolution we are experiencing defies a cadastral survey. Areas are continually being annexed into Data Science. For example, biometrics was traditionally statistics for agriculture in all its forms but now, in Data Science,

Information criteria for inhomogeneous spatial point processes Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210508
Achmad Choiruddin, JeanFrançois Coeurjolly, Rasmus WaagepetersenThe theoretical foundation for a number of model selection criteria is established in the context of inhomogeneous point processes and under various asymptotic settings: infill, increasing domain and combinations of these. For inhomogeneous Poisson processes we consider Akaike's information criterion and the Bayesian information criterion, and in particular we identify the point process analogue of

Depth and outliers for samples of sets and random sets distributions Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210507
Ignacio Cascos, Qiyu Li, Ilya MolchanovWe suggest several constructions suitable to define the depth of setvalued observations with respect to a sample of convex sets or with respect to the distribution of a random closed convex set. With the concept of a depth, it is possible to determine if a given convex set should be regarded an outlier with respect to a sample of convex closed sets. Some of our constructions are motivated by the known

Infill asymptotics for adaptive kernel estimators of spatial intensity Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210507
M.N.M. van LieshoutWe apply the Abramson principle to define adaptive kernel estimators for the intensity function of a spatial point process. We derive asymptotic expansions for the bias and variance under the regime that n independent copies of a simple point process in Euclidean space are superposed. The method is illustrated by means of a simple example and applied to tornado data.

Model‐based inference using judgement post‐stratified samples in finite populations Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210506
Omer Ozturk, Konul Bayramoglu KavlakIn survey sampling studies, statistical inference can be constructed either using design based randomisation or super population model. Design‐based inference using judgement post‐stratified (JPS) sampling is available in the literature. This paper develops statistical inference based on super population model in a finite population setting using JPS sampling design. For a JPS sample, first a simple

Modelling columnarity of pyramidal cells in the human cerebral cortex Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210506
Andreas Dyreborg Christoffersen, Jesper M⊘ller, Heidi S⊘gaard ChristensenFor modelling the location of pyramidal cells in the human cerebral cortex, we suggest a hierarchical point process in that exhibits anisotropy in the form of cylinders extending along the zaxis. The model consists first of a generalised shot noise Cox process for the xycoordinates, providing cylindrical clusters, and next of a Markov random field model for the zcoordinates conditioned on the xycoordinates

Stereological inference on mean particle shape from vertical sections Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210309
Eva B. Vedel JensenIt was a major breakthrough when designbased stereological methods for vertical sections were developed by Adrian Baddeley and coworkers in the 1980s. Most importantly, it was shown how to estimate in a designbased fashion surface area from observations in random vertical sections with uniform position and uniform rotation around the vertical axis. The great practical importance of these developments

Projection properties of three‐level screening designs Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210222
Mohammed A. Alomair, Stelios D. Georgiou, Manohar AggarwalScreening designs are important for finding the factors that have a major effect on industrial experiments. In regard to quantitative factors, certain experimenters prefer three‐level rather than two‐level factors because having three levels can provide some assessments for capturing curvature in the response. In a recent paper, Jones and Nachtsheim, Journal of Quality Technology43, 1–15, proposed

Regression trees for poverty mapping Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210217
Penelope Bilton, Geoff Jones, Siva Ganesh, Stephen HaslettPoverty mapping is used to facilitate efficient allocation of aid resources, with the objective of ending poverty, the first of the United Nations Sustainable Development Goals. Levels of poverty across small geographic domains within a country are estimated using a statistical model, and the resulting estimates displayed on a poverty map. Current methodology for small area estimation of poverty utilises

A loss‐based prior for Gaussian graphical models Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210222
Laurenţiu Cătălin Hinoveanu, Fabrizio Leisen, Cristiano VillaGaussian graphical models play an important role in various areas such as genetics, finance, statistical physics and others. They are a powerful modelling tool, which allows one to describe the relationships among the variables of interest. From the Bayesian perspective, there are two sources of randomness: one is related to the multivariate distribution and the quantities that may parametrise the

Efficient error variance estimation in non‐parametric regression Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210217
Zhijian Li, Wei LinError variance estimation plays a key role in the analysis of homogeneous non‐parametric regression models. For a random design model, most methods in the literature for error variance estimation assume the independence between the predictor variable X and the error ε. In this work, we derive the optimal semi‐parametric efficiency bound for the error variance without such an independence assumption

The focussed information criterion for generalised linear regression models for time series Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210222
S. C. Pandhare, T. V. RamanathanThe present paper proposes the focussed information criterion (FIC) to tackle the model selection problems pertinent to generalised linear models (GLM) for time series. As a first step towards constructing the FIC, we formally discuss the local asymptotic theory of quasi‐maximum likelihood estimation for time series GLM under potential model misspecification. The general FIC formula is derived subsequently

Estimation of Poisson mean with under‐reported counts: a double sampling approach Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210222
Debjit Sengupta, Tathagata Banerjee, Surupa RoyCount data arising in various fields of applications are often under‐reported. Ignoring undercount naturally leads to biased estimators and inaccurate confidence intervals. In the presence of undercount, in this paper, we develop likelihood‐based methodologies for estimation of mean using validation data. The asymptotic distributions of the competing estimators of the mean are derived. The impact of

Bayesian estimation and model comparison for linear dynamic panel models with missing values Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20210222
Christian Aßmann, Marcel PreisingPanel data are collected over several time periods for the same units and hence allow for modelling both latent heterogeneity and dynamics. Since in a dynamic setup, the dependent variable also appears as an explanatory variable in later periods, missing values lead to substantial loss of information and the possibility of inefficient estimation. For linear dynamic panel models with fixed or random

Sparse vector error correction models with application to cointegration‐based trading Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20201019
Renjie Lu, Philip L.H. Yu, Xiaohang WangInspired by constructing large‐size cointegrated portfolios, this paper considers a vector error correction model and develops the adaptive Lasso estimator of the cointegrating vectors. The asymptotic properties of the estimators and the oracle property of the adaptive Lasso are derived. An optimisation algorithm for estimating the model parameters is proposed. The simulation study shows the effectiveness

Inference for short‐memory time series models based on modified empirical likelihood Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20201019
Ramadha D. Piyadi Gamage, Wei NingEmpirical likelihood (EL) has been extensively studied to make statistical inferences for independent and dependent observations. However, it experiences the problem of under‐coverage which causes the coverage probability of the EL‐based confidence intervals to be lower than the nominal level, especially in small sample sizes. In this paper, we propose modified versions of different EL‐related methods

On goodness‐of‐fit measures for Poisson regression models Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20201009
Takeshi Kurosawa, Francis K.C. Hui, A.H. Welsh, Kousuke Shinmura, Nobuoki EshimaIn this article, we study the statistical properties of the goodness‐of‐fit measure mpp proposed by (Eshima & Tabata 2007, Statistics & Probability Letters 77, 583–593) for generalised linear models. Focusing on the special case of Poisson regression using the canonical log link function, and assuming a random vector X of covariates, we obtain an explicit form for mpp that enables us to study its properties

Approximate two‐sided tolerance intervals for normal mixture distributions Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20201019
Shin‐Fu TsaiUniversal and individual two‐sided tolerance intervals that take the inherent structure of normal mixture distributions into account are introduced in this paper for the purpose of monitoring the overall population and specific subpopulations. On the basis of generalised fiducial inference, a Markov chain Monte Carlo sampler is proposed to generate realisations from the generalised fiducial distributions

stratifyR: An R Package for optimal stratification and sample allocation for univariate populations Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20201019
K. G. Reddy, M. G. M. KhanThis R package determines optimal stratification of univariate populations under stratified sampling designs using a parametric‐based method. It determines the optimum strata boundaries (OSB), optimum sample sizes (OSS) and multiple other quantities for the study variable, y, using the best‐fit probability density function of a study variable available from survey data. The method requires the parameters

Detecting changes in task length due to task‐switching in the presence of repeated length‐biased sampling Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200716
Scott R. Walter, Bruce M. Brown, William T.M. DunsmuirClinical work is characterised by frequent interjection of external prompts causing clinicians to switch from a primary task to deal with an incoming secondary task, a phenomenon associated with negative effects in experimental studies. This is an important yet underexplored aspect of work in safety critical settings in general, since an increase in task length due to task‐switching implies reduced

Modelling the travel time of transit vehicles in real‐time through a GTFS‐based road network using GPS vehicle locations Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200704
Tom Elliott, Thomas LumleyPredicting the arrival time of a transit vehicle involves not only knowledge of its current position and schedule adherence, but also traffic conditions along the remainder of the route. Road networks are dynamic and can quickly change from free‐flowing to highly congested, which impacts the arrival time of transit vehicles, particularly buses which often share the road with other vehicles, so reliable

On a class of bivariate mixed Sarmanov distributions Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200708
Raluca VernicMultivariate distributions are more and more used to model the dependence encountered in many fields. However, classical multivariate distributions can be restrictive by their nature, while Sarmanov's multivariate distribution, by joining different marginals in a flexible and tractable dependence structure, often provides a valuable alternative. In this paper, we introduce some bivariate mixed Sarmanov

Robust estimation for longitudinal data under outcome‐dependent visit processes Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200704
John M. Neuhaus, Charles E. McCullochIn longitudinal data where the timing and frequency of the measurement of outcomes may be associated with the value of the outcome, significant bias can occur. Previous results depended on correct specification of the outcome process and a somewhat unrealistic visit process model. In practice, this will never exactly be the case, so it is important to understand to what degree the results hold when

Modal non‐linear regression in the presence of Laplace measurement error Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200709
Jianhong Shi, Jie Zhang, Xiaorui Wang, Weixing SongIn this paper, we propose a robust estimation procedure for a class of non‐linear regression models when the covariates are contaminated with Laplace measurement error, aiming at constructing an estimation procedure for the regression parameters which are less affected by the possible outliers, and heavy‐tailed underlying distribution, as well as reducing the bias introduced by the measurement error

Variable selection for first‐order Poisson integer‐valued autoregressive model with covariables Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200712
Xinyang WangIn recent years, modelling count data has become one of the most important and popular topics in time‐series analysis. At the same time, variable selection methods have become widely used in many fields as an effective statistical modelling tool. In this paper, we consider using a variable selection method to solve a modelling problem regarding the first‐order Poisson integer‐valued autoregressive

A folded model for compositional data analysis Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200601
Michail Tsagris, Connie StewartA folded type model is developed for analyzing compositional data. The proposed model, which is based upon the $\alpha$transformation for compositional data, provides a new and flexible class of distributions for modeling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation. The model is validated

Spatial modelling of the two‐party preferred vote in Australian federal elections: 2001–2016 Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200601
Jeremy Forbes, Dianne Cook, Rob J. HyndmanWe examine the relationships between electoral sociodemographic characteristics and twoparty preference in the six Australian federal elections held between 2001 to 2016. Sociodemographic information is derived from the Australian Census, which occurs every five years. Since a Census is not directly available for each election, spatiotemporal imputation is employed to estimate Census data for the

Pitman Medal 2016 and 2018 Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200428
Pitman medal awarded by Statistical Society of Australia for outstanding achievement.

A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200428
Hassan S. Bakouch, Y. Sunecher, N. Mamode Khan, V. JowaheerThis paper considers modelling of a non‐stationary bivariate integer‐valued autoregressive process of order 1 (BINAR(1)) where the cross‐dependence between the counting series is formed through the relationship of the current series with the previous‐lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi‐likelihood

Maximum likelihood estimation for outcome‐dependent samples Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200428
Robert Graham ClarkIn outcome‐dependent sampling, the continuous or binary outcome variable in a regression model is available in advance to guide selection of a sample on which explanatory variables are then measured. Selection probabilities may either be a smooth function of the outcome variable or be based on a stratification of the outcome. In many cases, only data from the final sample is accessible to the analyst

Generalised regression estimation via the bootstrap Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200408
James G. Booth, Alan H. WelshA generalised regression estimation procedure is proposed that can lead to much improved estimation of population characteristics, such as quantiles, variances and coefficients of variation. The method involves conditioning on the discrepancy between an estimate of an auxiliary parameter and its known population value. The key distributional assumption is joint asymptotic normality of the estimates

The VGAM package for negative binomial regression Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200405
Thomas W. YeeNegative binomial (NB) regression is the most common full‐likelihood method for analysing count data exhibiting overdispersion with respect to the Poisson distribution. Usually most practitioners are content to fit one of two NB variants, however other important variants exist. It is demonstrated here that the VGAMR package can fit them all under a common statistical framework founded upon a generalised

Bayesian weighted inference from surveys Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200301
David Gunawan, Anastasios Panagiotelis, William Griffiths, Duangkamon ChotikapanichData from large surveys are often supplemented with sampling weights that are designed to reflect unequal probabilities of response and selection inherent in complex survey sampling methods. We propose two methods for Bayesian estimation of parametric models in a setting where the survey data and the weights are available, but where information on how the weights were constructed is unavailable. The

The score test for the twosample occupancy model Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20200301
N. Karavarsamis, G. Guillera‐Arroita, R.M. Huggins, B.J.T. MorganThe score test statistic computed using the observed information is easy to compute numerically. Its large sample distribution under the null hypothesis is well known and is equivalent to that of the score test based on the expected information, the likelihoodratio test and the Wald test. However, several authors have noted that under the alternative hypothesis this no longer holds and in particular

Rachel Fewster: Recipient of NZSA Campbell Award 2018 Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20191201
Russell MillarThe origins of Rachel Fewster’s tenure in The Department of Statistics at the University of Auckland can be traced back to the 1997 meeting of the Australasian Region of the International Biometrics Society, held at Paradise Cove in South Australia. It was there that some newly converted Bayesians (Renate Meyer and Russell Millar) met a keen young PhD student from the University of St Andrews. She

Series estimation for single‐index models under constraints Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20190901
Chaohua Dong, Jiti Gao, Bin PengThis paper discusses a semiparametric singleindex model. The link function is allowed to be unbounded and has unbounded support that fill the gap in the literature. The link function is treated as a point in an infinitely many dimensional function space which enables us to derive the estimates for the index parameter and the link function simultaneously. This approach is different from the profile

Bayesian density regression for discrete outcomes Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20190901
Georgios PapageorgiouWe develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either

Exact or approximate inference in graphical models: why the choice is dictated by the treewidth, and how variable elimination can be exploited Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20190601
N. Peyrard, M.‐J. Cros, S. Givry, A. Franc, S. Robin, R. Sabbadin, T. Schiex, M. VignesProbabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper, we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle

Climate regime shift detection with a trans‐dimensional, sequential Monte Carlo, variational Bayes method Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20190601
Clare A. McGrory, Daniel C. Ahfock, Ricardo T. LemosWe present an application study which exemplifies a cutting edge statistical approach for detecting climate regime shifts. The algorithm uses Bayesian computational techniques that make timeefficient analysis of large volumes of climate data possible. Output includes probabilistic estimates of the number and duration of regimes, the number and probability distribution of hidden states, and the probability

Confidence intervals centred on bootstrap smoothed estimators Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20190301
Paul Kabaila, Christeen WijethungaBootstrap smoothed (bagged) parameter estimators have been proposed as an improvement on estimators found after preliminary databased model selection. The key result of Efron (2014) is a very convenient and widely applicable formula for a delta method approximation to the standard deviation of the bootstrap smoothed estimator. This approximation provides an easily computed guide to the accuracy of

Posterior sampling in two classes of multivariate fractionally integrated models: corrigendum to Ravishanker, N. and B. K. Ray (1997) Australian Journal of Statistics 39 (3), 295311 Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20190220
Ross Doppelt, Keith O'HaraWe discuss posterior sampling for two distinct multivariate generalizations of the univariate ARIMA model with fractional integration. The existing approach to Bayesian estimation, introduced by Ravishanker and Ray (1997), claims to provide a posteriorsampling algorithm for fractionally integrated vector autoregressive moving averages (FIVARMAs). We show that this algorithm produces posterior draws

Nonparametric kernel estimation of the impact of tax policy on the demand for private health insurance in Australia Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20180801
Xiaodong Gong, Jiti GaoThis paper is motivated by our attempt to answer an empirical question: how is private health insurance takeup in Australia affected by the income threshold at which the Medicare Levy Surcharge (MLS) kicks in? We propose a new difference deconvolution kernel estimator for the location and size of regression discontinuities. We also propose a bootstrapping procedure for estimating confidence bands

Bayesian inference for a partially observed birthdeath process using data on proportions Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20180530
Richard J. Boys, Holly F. Ainsworth, Colin S. GillespieStochastic kinetic models are often used to describe complex biological processes. Typically these models are analytically intractable and have unknown parameters which need to be estimated from observed data. Ideally we would have measurements on all interacting chemical species in the process, observed continuously in time. However, in practice, measurements are taken only at a relatively few timepoints

A note on the robustness of PBIBD(2)s against breakdown in the event of observation loss Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20180305
Janet D. GodolphinRobustness against design breakdown following observation loss is investigated for Partially Balanced Incomplete Block Designs with two associate classes (PBIBD(2)s). New results are obtained which add to the body of knowledge on PBIBD(2)s. In particular, using an approach based on the Evalue of a design, all PBIBD(2)s with triangular and Latin square association schemes are established as having

On expectation propagation for generalised, linear and mixed models Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20180301
Andy S.I. Kim, Matt P. WandExpectation propagation is a general approach to deterministic approximate Bayesian inference for graphical models, although its literature is confined mostly to machine learning applications. We investigate the utility of expectation propagation in generalised, linear, and mixed model settings. We show that, even though the algebra and computations are complicated, the notion of message passing on

Consistency of a hybrid block bootstrap for distribution and variance estimation for sample quantiles of weakly dependent sequences Aust. N. Z. J. Stat. (IF 0.64) Pub Date : 20180301
Todd A. Kuffner, Stephen M. S. Lee, G. A. YoungConsistency and optimality of block bootstrap schemes for distribution and variance estimation of smooth functionals of dependent data have been thoroughly investigated by Hall, Horowitz & Jing (1995), among others. However, for nonsmooth functionals, such as quantiles, much less is known. Existing results, due to Sun & Lahiri (2006), regarding strong consistency for distribution and variance estimation