Australian & New Zealand Journal of Statistics

Cover image for Vol. 59 Issue 2

Edited By: James Curran, Michael Martin, Martin Hazelton, Chris Triggs, Petra Graham, Rolf Turner and Samuel Müller

Impact Factor: 0.679

ISI Journal Citation Reports © Ranking: 2016: 87/124 (Statistics & Probability)

Online ISSN: 1467-842X

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  1. On generalised estimating equations for vector regression (pages 195–213)

    A. Huang

    Version of Record online: 20 JUN 2017 | DOI: 10.1111/anzs.12191

    The popular generalised estimating equations framework is extended to vector regression settings with multivariate responses.

  2. Efficient estimation of nonparametric spatial models with general correlation structures (pages 215–233)

    Hongxia Wang, Yuehua Wu and Elton Chan

    Version of Record online: 20 JUN 2017 | DOI: 10.1111/anzs.12192

    Firstly, we consider spatial correlation and spatial heterogeneity simultaneously. Secondly, we consider a general spatial correlation structure (second-order stationarity and isotropy) rather than assuming that the correlation satisfies some specific form (such as that given by a spatial autoregressive model). Finally we establish the asymptotic normality of our estimators under mild conditions. The results of our simulation and case studies indicate that our estimators perform better than those arising from traditional locally linear methods which ignore spatial correlation.

  3. The use of length-biased distributions in statistical monitoring (pages 155–167)

    S. Bersimis and P. Economou

    Version of Record online: 18 JUN 2017 | DOI: 10.1111/anzs.12190

    This paper examines the case of changes occurring in the sampling mechanism from Phase I to Phase II which produce biased samples during Phase II monitoring.

  4. Pseudo-R2 statistics under complex sampling (pages 187–194)

    Thomas Lumley

    Version of Record online: 9 JUN 2017 | DOI: 10.1111/anzs.12187

    This paper shows how to estimate popular two pseudo-R2 summaries under complex sampling; the standard definitions are biased even for logistic regression under case-control sampling.

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    Association models in the weighted log ratio analysis for rates (pages 169–185)

    A. D'Ambra, A. Crisci and L. D'Ambra

    Version of Record online: 26 APR 2017 | DOI: 10.1111/anzs.12185

    The paper considers the Goodman's association models in the Weighted Log Ratio Analysis context. We combine these two methods in order to analyse a rates matrix.