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   Hybrid Journal Hybrid journal (It can contain Open Access articles)
     ISSN (Online) 2049-1573
     Published by John Wiley and Sons Homepage  [1602 journals]
  • A note on approximating ABC-MCMC using flexible classifiers
    • Authors: Kim Cuc Pham; David J. Nott, Sanjay Chaudhuri
      Pages: n/a - n/a
      Abstract: A method for approximating Markov chain Monte Carlo algorithms is considered in the setting where the likelihood is intractable. The approach is based on interpreting the likelihood ratio in the Metropolis–Hastings acceptance probability as the odds in the Bayes classification rule for distinguishing whether the observed data were generated using the proposal parameter value or the current one. Approximating the Bayes rule using simulated data from the model and modern flexible classifiers capable of dealing with high-dimensional feature vectors results in new approximate Bayesian computation procedures that are able to perform well with high-dimensional summary statistics. In problems of small to moderate size, it may even be possible to dispense with summary statistics altogether. The synthetic likelihood of Wood corresponds to classification by quadratic discriminant analysis in this framework. Copyright © 2014 John Wiley & Sons, Ltd.
      PubDate: 2014-06-25T00:39:02.926769-05:
      DOI: 10.1002/sta4.56
  • Surface boxplots
    • Authors: Marc G. Genton; Christopher Johnson, Kristin Potter, Georgiy Stenchikov, Ying Sun
      Pages: n/a - n/a
      Abstract: In this paper, we introduce a surface boxplot as a tool for visualization and exploratory analysis of samples of images. First, we use the notion of volume depth to order the images viewed as surfaces. In particular, we define the median image. We use an exact and fast algorithm for the ranking of the images. This allows us to detect potential outlying images that often contain interesting features not present in most of the images. Second, we build a graphical tool to visualize the surface boxplot and its various characteristics. A graph and histogram of the volume depth values allow us to identify images of interest. The code is available in the supporting information of this paper. We apply our surface boxplot to a sample of brain images and to a sample of climate model outputs. Copyright © 2014 John Wiley & Sons Ltd.
      PubDate: 2014-01-22T21:06:22.569842-05:
      DOI: 10.1002/sta4.39
  • A Gaussian compound decision bakeoff
    • Authors: Roger Koenker
      First page: 12
      Abstract: A non-parametric mixture model approach to empirical Bayes compound decisions for the Gaussian location model is compared with a parametric empirical Bayes approach recently suggested by Martin and Walker and several recent more formal Bayes procedures.Copyright © 2014 John Wiley & Sons, Ltd
      PubDate: 2014-01-26T22:05:27.774259-05:
      DOI: 10.1002/sta4.38
  • When are first‒order asymptotics adequate' A diagnostic
    • Authors: Karim Anaya‒Izquierdo; Frank Critchley, Paul Marriott
      First page: 17
      Abstract: This paper looks at boundary effects on inference in an important class of models including, notably, logistic regression. Asymptotic results are not uniform across such models. Accordingly, whatever their order, methods asymptotic in sample size will ultimately “break down” as the boundary is approached, in the sense that effects such as infinite skewness, discreteness and collinearity will dominate. In this paper, a highly interpretable diagnostic tool is proposed, allowing the analyst to check if the boundary is going to have an appreciable effect on standard inferential techniques. Copyright © 2014 John Wiley & Sons Ltd.
      PubDate: 2014-02-21T05:03:14.760846-05:
      DOI: 10.1002/sta4.40
  • A note on the tails of the GO-GARCH process
    • Authors: Nelson Muriel
      First page: 23
      Abstract: The possibility of modeling heavy tails using generalized autoregressive conditional heteroskedasticity (GARCH) models has been rigorously established in the univariate case, and the consequences that this heavy tailedness has on the distributional limits of the sample autocovariance function are well known. In the multivariate case, however, the results have not as yet been provided, and the asymptotic properties of the autocovariance function are not fully understood. In this note, we focus on the generalized orthogonal GARCH (GO-GARCH) model, a multivariate specification that has recently received some attention in the literature. We first show that marginal heavy tailedness is a simple consequence of the definition of the process and argue that all tail indexes should be equal. Next, we show that the finite-dimensional distributions of the GO-GARCH model possess some of the properties known to hold for univariate GARCH and comment on some implications of this fact, which are of practical import. Specifically, we show that the sample autocovariance function may either have a random limit or converge rather slowly to its population counterpart depending on how heavy the tails of the process are. Copyright © 2014 John Wiley & Sons Ltd.
      PubDate: 2014-02-24T11:52:48.803031-05:
      DOI: 10.1002/sta4.41
  • Quantile regression analysis of length-biased survival data
    • Authors: Huixia Judy Wang; Lan Wang
      First page: 31
      Abstract: Length-biased time-to-event data commonly arise in epidemiological cohort studies and cross-sectional surveys. Ignoring length-biased sampling often leads to severe bias in estimating the survival time in the general population. We propose a flexible quantile regression framework for analysing the covariate effects on the population survival time under both length-biased sampling and random censoring. This framework allows for easy interpretation of the statistical model. Furthermore, it allows the covariates to have different impacts at different tails of the survival distribution and thus is able to capture important population heterogeneity. Using an unbiased estimating equation approach, we develop a new estimator that allows the censoring variable to depend on covariates in a non-parametric way. We establish the consistency and asymptotic normality for the proposed estimator. A lack-of-fit test is proposed for diagnosing the adequacy of the population quantile regression model. The finite sample performance of the proposed methods is assessed through a simulation study. We demonstrate that the proposed method is effective in discovering interesting covariate effects by analysing the Canadian Study of Health and Aging dementia data. Copyright © 2014 John Wiley & Sons Ltd
      PubDate: 2014-03-05T02:06:48.16971-05:0
      DOI: 10.1002/sta4.42
  • Beyond axial symmetry: An improved class of models for global data
    • Authors: Stefano Castruccio; Marc G. Genton
      First page: 48
      Abstract: An important class of models for data on a spherical domain, called axially symmetric, assumes stationarity across longitudes but not across latitudes. The main aim of this work is to introduce a new and more flexible class of models by relaxing the assumption of longitudinal stationarity in the context of regularly gridded climate model output. In this investigation, two other related topics are discussed: the lack of fit of an axially symmetric parametric model compared with a non-parametric model and to longitudinally reversible processes, an important subclass of axially symmetric models. Copyright © 2014 John Wiley & Sons, Ltd
      PubDate: 2014-03-13T00:35:32.513891-05:
      DOI: 10.1002/sta4.44
  • Modulation of symmetry for discrete variables and some extensions
    • Authors: Adelchi Azzalini; Giuliana Regoli
      First page: 56
      Abstract: Substantial work has been dedicated in recent years to the construction of families of continuous distributions obtained by applying a modulation factor to a base symmetric density so as to obtain non-symmetric variant forms, often denoted skew-symmetric distributions. All this development has dealt with the case of continuous variables, while here we extend the formulation to the discrete case; moreover, some of the statements are of general validity. The results are illustrated with an application to the distribution of the score difference in sport matches. Copyright © 2014 John Wiley & Sons, Ltd
      PubDate: 2014-03-20T05:08:58.68592-05:0
      DOI: 10.1002/sta4.45
  • A mixture of common skew-t factor analysers
    • Authors: Paula M. Murray; Paul D. McNicholas, Ryan P. Browne
      First page: 68
      Abstract: A mixture of common skew-t factor analysers model is introduced for model-based clustering of high-dimensional data. By assuming common factors, this model allows clustering to be performed in the presence of a large number of mixture components or when the number of dimensions is too large to be well modelled by the mixture of factor analysers model or a variant thereof. Furthermore, assuming that the component densities follow a skew-t distribution allows robust clustering of data with asymmetric clusters. This paper is the first time that skewed common factors have been used, and it marks an important step in robust clustering and classification of high-dimensional data. The alternating expectation–conditional maximization algorithm is employed for parameter estimation. We demonstrate excellent clustering performance when our mixture of common skew-t factor analysers model is applied to real and simulated data. Copyright © 2014 John Wiley & Sons, Ltd
      PubDate: 2014-03-27T01:48:18.491063-05:
      DOI: 10.1002/sta4.43
  • Propensity score estimation in the presence of length-biased sampling: a
           non-parametric adjustment approach
    • Authors: Ashkan Ertefaie; Masoud Asgharian, David Stephens
      First page: 83
      Abstract: The pervasive use of prevalent cohort studies on disease duration increasingly calls for an appropriate methodology to account for the biases that invariably accompany samples formed by such data. It is well known, for example, that subjects with shorter lifetime are less likely to be present in such studies. Moreover, certain covariate values could be preferentially selected into the sample, being linked to the long-term survivors. The existing methodology for estimating the propensity score using data collected on prevalent cases requires the correct conditional survival/hazard function given the treatment and covariates. This requirement can be alleviated if the disease under study has stationary incidence, the so-called stationarity assumption. We propose a non-parametric adjustment technique based on a weighted estimating equation for estimating the propensity score, which does not require modeling the conditional survival/hazard function when the stationarity assumption holds. The estimator's large-sample properties are established, and its small-sample behavior is studied via simulation. The estimated propensity score is utilized to estimate the survival curves. Copyright © 2014 John Wiley & Sons, Ltd
      PubDate: 2014-03-27T01:40:37.893836-05:
      DOI: 10.1002/sta4.46
  • Right and left kurtosis measures: large sample estimation and an
           application to financial returns
    • Authors: Anna Maria Fiori; Davide Beltrami
      First page: 95
      Abstract: Although the standard fourth moment coefficient is routinely computed as “the kurtosis” of a distribution, the measure is not easily interpreted and has been a subject of considerable debate in statistical literature. The financial community has recently joined in the debate, calling for more robust estimators of kurtosis in distributions of stock market returns. For these reasons, we here consider alternative measures of right and left kurtosis, which arise from a recent characterization of kurtosis as inequality at either side of the median. Based on Gini's coefficient of concentration, the new measures apply to both symmetric and asymmetric distributions, their interpretation is clear and they are consistent with common risk perceptions of investors and risk managers. In this contribution, we show that the theory of L-statistics provides a natural framework for the construction of empirical estimators of the proposed measures and the derivation of their asymptotic properties under mild moment requirements. A real data example illustrates the potential of these estimators in financial contexts, in which the existence of higher moments is still an open question. Copyright © 2014 John Wiley & Sons, Ltd
      PubDate: 2014-04-02T01:10:08.144316-05:
      DOI: 10.1002/sta4.48
  • Bayesian sparse graphical models and their mixtures
    • Authors: Rajesh Talluri; Veerabhadran Baladandayuthapani, Bani K. Mallick
      First page: 109
      Abstract: We propose Bayesian methods for Gaussian graphical models that lead to sparse and adaptively shrunk estimators of the precision (inverse covariance) matrix. Our methods are based on lasso-type regularization priors leading to parsimonious parameterization of the precision matrix, which is essential in several applications involving learning relationships among the variables. In this context, we introduce a novel type of selection prior that develops a sparse structure on the precision matrix by making most of the elements exactly zero, in addition to ensuring positive definiteness—thus conducting model selection and estimation simultaneously. More importantly, we extend these methods to analyse clustered data using finite mixtures of Gaussian graphical model and infinite mixtures of Gaussian graphical models. We discuss appropriate posterior simulation schemes to implement posterior inference in the proposed models, including the evaluation of normalizing constants that are functions of parameters of interest, which result from the restriction of positive definiteness on the correlation matrix. We evaluate the operating characteristics of our method via several simulations and demonstrate the application to real-data examples in genomics. Copyright © 2014 John Wiley & Sons, Ltd
      PubDate: 2014-04-24T00:28:40.253451-05:
      DOI: 10.1002/sta4.49
  • Multilevel sparse functional principal component analysis
    • Authors: Chongzhi Di; Ciprian M. Crainiceanu, Wolfgang S. Jank
      First page: 126
      Abstract: We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis was proposed recently for such data when functions are densely recorded. Here, we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between-subject and within-subject levels. We address inherent methodological differences in the sparse sampling context to: (i) estimate the covariance operators; (ii) estimate the functional principal component scores; and (iii) predict the underlying curves. Through simulations, the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions. Copyright © 2014 John Wiley & Sons, Ltd
      PubDate: 2014-04-24T00:27:51.75543-05:0
      DOI: 10.1002/sta4.50
  • Classification of non-stationary time series
    • Authors: Karolina Krzemieniewska; Idris A. Eckley, Paul Fearnhead
      Pages: 144 - 157
      Abstract: In this paper we consider the problem of classifying non-stationary time series. The method that we introduce is based on the locally stationary wavelet paradigm and seeks to take account of the fact that there may be within-class variation in the signals being analysed. Specifically, we seek to identify the most stable spectral coefficients within each training group and use these to classify a new, previously unseen, time series. In both simulated examples and an aerosol spray example provided by an industrial collaborator, our approach is found to yield superior classification performance when compared against the current state of the art. Copyright © 2014 John Wiley & Sons, Ltd.
      PubDate: 2014-05-18T21:47:12.854853-05:
      DOI: 10.1002/sta4.51
  • Probability of success: estimation framework, properties and applications
    • Authors: Peter Hu
      Pages: 158 - 171
      Abstract: Probability of success (PoS), defined as Bayesian expected power, has drawn more and more attention as an alternative metric complementary to the conventional conditional power for study planning. This paper describes an estimation framework for PoS on the basis of a proposed joint posterior distribution for the location and scale parameters of an effect of interest, followed by illustrations of how to estimate this quantity efficiently. Some features of this PoS framework are disclosed via the affiliated settings with non-informative prior. The upper limit of PoS, obtained when sample size approaches infinity, is derived in closed form. Three applications of this framework are given to demonstrate the benefits of using the concept of PoS in strategic planning of a confirmatory study and in interim monitoring of drug effectiveness and, as lessons learnt, how a non-inferiority study could be powered appropriately and how change of trend to achieving non-inferiority could be tracked. Copyright © 2014 John Wiley & Sons, Ltd.
      PubDate: 2014-06-02T21:10:25.02844-05:0
      DOI: 10.1002/sta4.52
  • Voxelwise single-subject analysis of imaging metabolic response to therapy
           in neuro-oncology
    • Authors: Mengye Guo; Jeffrey T. Yap, Annick D. Van den Abbeele, Nancy U. Lin, Armin Schwartzman
      Pages: 172 - 186
      Abstract: F-18-Fluorodeoxyglucose positron emission tomography (FDG-PET) has been used to evaluate the metabolic response of metastatic brain tumours to treatment by comparing their tumour glucose metabolism before and after treatment. The standard analysis based on regions-of-interest has the advantage of simplicity. However, it is by definition restricted to those regions and is subject to observer variability. In addition, the observed changes in tumour metabolism are often confounded by normal changes in the tissue background, which can be heterogenous. We propose an analysis pipeline for automatically detecting the change at each voxel in the entire brain of a single subject, while adjusting for changes in the background. The complete analysis includes image registration, segmentation, a hierarchical model for background adjustment and voxelwise statistical comparisons. We demonstrate the method's ability to identify areas of tumour response and/or progression in two subjects enrolled in a clinical trial using FDG-PET to evaluate lapatinib for the treatment of brain metastases in breast cancer patients. Copyright © 2014 John Wiley & Sons, Ltd.
      PubDate: 2014-06-10T20:49:22.686867-05:
      DOI: 10.1002/sta4.53
  • Adaptation in some linear inverse problems
    • Authors: Iain M. Johnstone; Debashis Paul
      Pages: 187 - 199
      Abstract: We consider the linear inverse problem of estimating an unknown signal f from noisy measurements on Kf where the linear operator K admits a wavelet–vaguelette decomposition. We formulate the problem in the Gaussian sequence model and propose estimation based on complexity penalized regression on a level-by-level basis. We adopt squared error loss and show that the estimator achieves exact rate-adaptive optimality as f varies over a wide range of the Besov function classes. Copyright © 2014 John Wiley & Sons, Ltd.
      PubDate: 2014-06-17T02:14:04.67599-05:0
      DOI: 10.1002/sta4.54
  • Spline approximations to conditional Archimedean copula
    • Authors: Philippe Lambert
      Pages: 200 - 217
      Abstract: We propose a flexible copula model to describe changes with a covariate in the dependence structure of (conditionally exchangeable) random variables. The starting point is a spline approximation to the generator of an Archimedean copula. Changes in the dependence structure with a covariate x are modelled by flexible regression of the spline coefficients on x. The performances and properties of the spline estimate of the reference generator and the abilities of these conditional models to approximate conditional copulas are studied through extensive simulations. Inference is made using Bayesian arguments with posterior distributions explored using importance sampling or adaptive Markov chain Monte Carlo algorithms. The modelling strategy is illustrated with the analysis of bivariate growth curve data. Copyright © 2014 John Wiley & Sons, Ltd.
      PubDate: 2014-06-18T21:55:20.870959-05:
      DOI: 10.1002/sta4.55
  • Wiley‐Blackwell Announces Launch of Stat – The ISI's Journal
           for the Rapid Dissemination of Statistics Research
    • Pages: n/a - n/a
      PubDate: 2012-04-17T04:34:14.600281-05:
      DOI: 10.1002/sta4.1
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