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Journal Cover Stat
  [SJR: 0.985]   [H-I: 5]   [1 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Online) 2049-1573
   Published by John Wiley and Sons Homepage  [1616 journals]
  • Robust quantile regression using a generalized class of skewed
    • Authors: Christian Galarza Morales; Victor Lachos Davila, Celso Barbosa Cabral, Luis Castro Cepero
      Abstract: It is well known that the widely popular mean regression model could be inadequate if the probability distribution of the observed responses do not follow a symmetric distribution. To deal with this situation, the quantile regression turns to be a more robust alternative for accommodating outliers and the misspecification of the error distribution because it characterizes the entire conditional distribution of the outcome variable. This paper presents a likelihood-based approach for the estimation of the regression quantiles based on a new family of skewed distributions. This family includes the skewed version of normal, Student-t, Laplace, contaminated normal and slash distribution, all with the zero quantile property for the error term and with a convenient and novel stochastic representation that facilitates the implementation of the expectation–maximization algorithm for maximum likelihood estimation of the pth quantile regression parameters. We evaluate the performance of the proposed expectation–maximization algorithm and the asymptotic properties of the maximum likelihood estimates through empirical experiments and application to a real-life dataset. The algorithm is implemented in the R package lqr, providing full estimation and inference for the parameters as well as simulation envelope plots useful for assessing the goodness of fit. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-03-15T00:30:31.492398-05:
      DOI: 10.1002/sta4.140
  • Covariate selection for multilevel models with missing data
    • Authors: Miguel Marino; Orfeu M. Buxton, Yi Li
      Pages: 31 - 46
      Abstract: Missing covariate data hamper variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods that are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data are present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyse the Healthy Directions–Small Business cancer prevention study, which evaluated a behavioural intervention programme targeting multiple risk-related behaviours in a working-class, multi-ethnic population. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-01-08T18:50:26.241275-05:
      DOI: 10.1002/sta4.133
  • A parametric model bridging between bounded and unbounded variograms
    • Authors: Martin Schlather; Olga Moreva
      Pages: 47 - 52
      Abstract: A simple variogram model with two parameters is presented that includes the power variogram for fractional Brownian motion, a modified De Wijsian model, the generalized Cauchy model and the multiquadric model. One parameter controls the sample path roughness of the process. The other parameter allows for a smooth transition between bounded and unbounded variograms, that is, between stationary and intrinsically stationary processes in a Gaussian framework, or between mixing and non-ergodic Brown–Resnick processes when modeling spatial extremes. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-02-07T18:00:37.543933-05:
      DOI: 10.1002/sta4.134
  • A Bayesian supervised dual-dimensionality reduction model for simultaneous
           decoding of LFP and spike train signals
    • Authors: Andrew Holbrook; Alexander Vandenberg-Rodes, Norbert Fortin, Babak Shahbaba
      Pages: 53 - 67
      Abstract: Neuroscientists are increasingly collecting multimodal data during experiments and observational studies. Different data modalities—such as electroencephalogram, functional magnetic resonance imaging, local field potential (LFP) and spike trains—offer different views of the complex systems contributing to neural phenomena. Here, we focus on joint modelling of LFP and spike train data and present a novel Bayesian method for neural decoding to infer behavioural and experimental conditions. This model performs supervised dual-dimensionality reduction: it learns low-dimensional representations of two different sources of information that not only explain variation in the input data itself but also predict extraneuronal outcomes. Despite being one probabilistic unit, the model consists of multiple modules: exponential principal components analysis (PCA) and wavelet PCA are used for dimensionality reduction in the spike train and LFP modules, respectively; these modules simultaneously interface with a Bayesian binary regression module. We demonstrate how this model may be used for prediction, parametric inference and identification of influential predictors. In prediction, the hierarchical model outperforms other models trained on LFP alone, spike train alone and combined LFP and spike train data. We compare two methods for modelling the loading matrix and find them to perform similarly. Finally, model parameters and their posterior distributions yield scientific insights. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-02-07T21:48:34.295858-05:
      DOI: 10.1002/sta4.137
  • “Stationary” point processes are uncommon on linear networks
    • Authors: Adrian Baddeley; Gopalan Nair, Suman Rakshit, Greg McSwiggan
      Pages: 68 - 78
      Abstract: Statistical methodology for analysing patterns of points on a network of lines, such as road traffic accident locations, often assumes that the underlying point process is “stationary” or “correlation-stationary.” However, such processes appear to be rare. In this paper, popular procedures for constructing a point process are adapted to linear networks: many of the resulting models are no longer stationary when distance is measured by the shortest path in the network. This undermines the rationale for popular statistical methods such as the K-function and pair correlation function. Alternative strategies are proposed, such as replacing the shortest-path distance by another metric on the network. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-02-08T18:20:31.12866-05:0
      DOI: 10.1002/sta4.135
  • A second look at inference for bivariate Skellam distributions
    • Authors: Sidi Allal Aissaoui; Christian Genest, Mhamed Mesfioui
      Pages: 79 - 87
      Abstract: Two bivariate extensions of the Skellam distribution were introduced in 2014 by a subset of the present authors, who also proposed moment estimators for the dependence parameters in these models. The limiting distribution of these estimators is established here, and their asymptotic efficiency is compared with that of the corresponding maximum likelihood estimators. © 2017 The
      Authors . Stat Published by John Wiley & Sons Ltd.
      PubDate: 2017-02-16T17:45:25.574195-05:
      DOI: 10.1002/sta4.136
  • A procedure to detect general association based on concentration of ranks
    • Authors: Pratyaydipta Rudra; Yihui Zhou, Fred A. Wright
      Pages: 88 - 101
      Abstract: In modern high-throughput applications, it is important to identify pairwise associations between variables and desirable to use methods that are powerful and sensitive to a variety of association relationships. We describe RankCover, a new non-parametric association test of association between two variables that measures the concentration of paired ranked points. Here, “concentration” is quantified using a disk-covering statistic similar to those employed in spatial data analysis. Considerations from the theory of Boolean coverage processes provide motivation, as well as an R2-like quantity to summarize strength of association. Analysis of simulated and real datasets demonstrates that the method is robust and often powerful in comparison with competing general association tests. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-02-16T17:50:29.948074-05:
      DOI: 10.1002/sta4.138
  • Accurate logistic variational message passing: algebraic and numerical
    • Authors: Tui H. Nolan; Matt P. Wand
      Pages: 102 - 112
      Abstract: We provide full algebraic and numerical details required for fitting accurate logistic likelihood regression-type models via variational message passing with factor graph fragments. Existing methodology of this type involves the Jaakkola–Jordan device, which is prone to poor accuracy. We examine two alternatives: the Saul–Jordan tilted bound device and conjugacy enforcement via multivariate normal prespecification of a key message. Both of these approaches appear in related literature. Our contributions facilitate immediate implementation within variational message passing schemes. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-03-09T03:50:30.04134-05:0
      DOI: 10.1002/sta4.139
  • Issue Information
    • Pages: 1 - 3
      Abstract: No abstract is available for this article.
      PubDate: 2016-12-14T23:15:39.084732-05:
      DOI: 10.1002/sta4.117
  • Inferring population size: extending the multiplier method to incorporate
           multiple traits with a likelihood-based approach
    • Authors: Vivian Yun Meng; Paul Gustafson
      Pages: 4 - 13
      Abstract: Estimating population size is an important task for resource planning and policy making. One method is the “multiplier method” that uses information about a binary trait to infer the size of a population. This paper presents a likelihood-based estimator that generalizes the multiplier method to accommodate multiple traits as well as any number of categories in a trait. To provide guidelines for study design, we quantify the advantage of using multiple traits (multiple multipliers) by studying the estimator's asymptotic standard deviation (ASD). Inclusion of multiple traits reduces the ASD most effectively when the traits are uncorrelated and of low prevalence (roughly less than 10%), but the amount of reduction in ASD diminishes when the number of traits becomes large. A Bayesian implementation of our method is applied to both simulated data and real data pertaining to an injection-drug user population. Copyright © 2016 John Wiley & Sons, Ltd.
      PubDate: 2016-12-09T00:35:24.919233-05:
      DOI: 10.1002/sta4.131
  • Time-varying rankings with the Bayesian Mallows model
    • Authors: Derbachew Asfaw; Valeria Vitelli, Øystein Sørensen, Elja Arjas, Arnoldo Frigessi
      Pages: 14 - 30
      Abstract: We present new statistical methodology for analysing rank data, where the rankings are allowed to vary in time. Such data arise, for example, when the assessments are based on a performance measure of the items, which varies in time, or if the criteria, according to which the items are ranked, change in time. Items can also be absent when the assessments are made, because of delayed entry or early departure, or purely randomly. In such situations, also the dimension of the rank vectors varies in time. Rank data in a time-dependent setting thus lead to challenging statistical problems. These problems are further complicated, from the perspective of computation, by the large dimension of the sample space consisting of all permutations of the items. Here, we focus on introducing and developing a Bayesian version of the Mallows rank model, suitable for situations in which the ranks vary in time and the assessments can be incomplete. The consequent missing data problems are handled by applying Bayesian data augmentation within Markov chain Monte Carlo. Our method is also adapted to the task of future rank prediction. The method is illustrated by analysing some aspects of a data set describing the academic performance, measured by a series of tests, of a class of high school students over a period of 4 years. Copyright © 2016 John Wiley & Sons, Ltd.
      PubDate: 2016-12-28T01:05:33.790261-05:
      DOI: 10.1002/sta4.132
  • Wiley-Blackwell Announces Launch of Stat – The ISI's Journal for the
           Rapid Dissemination of Statistics Research
    • PubDate: 2012-04-17T04:34:14.600281-05:
      DOI: 10.1002/sta4.1
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