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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  [1609 journals]
  • Random effects model for bias estimation: higher‐order asymptotic
           inference
    • Authors: Andrew L. Rukhin
      Abstract: A common issue in physical, chemical and biometrical applications is to validate a laboratory's method. For that purpose, a lab performs measurements on a certified reference material with a given coverage interval. These reference materials are a major tool for assuring quality and reliability of results obtained by a lab in analysis and testing. Assuming that the measurand is random with a normal distribution whose parameters are obtained from the reference material certificate, new remarkably accurate confidence intervals for the bias are derived. These procedures are based on modern higher‐order asymptotic statistical methods. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
      PubDate: 2015-05-31T21:02:51.971205-05:
      DOI: 10.1002/sta4.82
       
  • Multivariate spatial hierarchical Bayesian empirical likelihood methods
           for small area estimation
    • Authors: Aaron T. Porter; Scott H. Holan, Christopher K. Wikle
      Abstract: Recent advances in small area estimation incorporating both explicit spatial autocorrelation and empirical likelihood techniques have produced estimates with greater precision. Furthermore, the multivariate Fay–Herriot models take advantage of within‐location correlation between multiple outcomes for a set of small areas. We extend the Fay–Herriot model by utilizing empirical likelihood techniques to the spatially explicit multivariate setting. We then model the five‐year period estimates from the American Community Survey (2006–10) of percent of unemployed individuals and percent of families in poverty for the counties of Missouri. We demonstrate bivariate reduction in leave‐one‐out median absolute deviation over an approximately equivalently specified parametric model. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-05-04T22:50:51.111952-05:
      DOI: 10.1002/sta4.81
       
  • A new weighted likelihood approach
    • Authors: Adhidev Biswas; Tania Roy, Suman Majumder, Ayanendranath Basu
      Abstract: In this paper, we propose a new weighted likelihood procedure. Here, the weights are suitably calibrated functions of appropriately described residuals at each data point. The residuals describe the match (or mismatch) between the empirical distribution function and the model distribution function. If the match is high, the observation is considered to be a regular observation. But for large (in magnitude) residuals, there is a mismatch, and the corresponding likelihood score function may require downweighting in order to obtain a robust solution. As there is little or no downweighting for observations where there is no evidence of mismatch, asymptotically, we expect that there will be no downweighting under the pure model leading to highly efficient estimators. On the other hand, properly calibrated weight functions that penalize the observations with large residuals will lead to highly robust solutions under model misspecification and the presence of outliers. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-04-21T02:13:36.208644-05:
      DOI: 10.1002/sta4.80
       
  • Optimal sample planning for system state analysis with partial data
           collection
    • Authors: Martin Heller; Jan Hannig, Malcolm R. Leadbetter
      Abstract: We develop optimal and computationally practical procedures to minimize uncertainty concerning the presence of dangerous levels of a contaminant within a building when neither replication nor complete data collection is feasible. More generally, we address inference about the state of a finite system when the state is related to information collected over components of the system when only partial data collection is feasible. When there is no correlation between sample locations, a simple random sample or maximum a priori trait presence would provide optimal sampling choices. When complicated probability models describe trait manifestation, the need to collect only partial data precludes a full fitting of complicated models, and one must rely heavily on prior information naturally leading to a Bayesian approach. Herein, we introduce a computationally efficient heuristic algorithm to simultaneously find optimal sample locations and decision rule parameterizations and then show that it drastically outperforms both random selection and maximum a priori methods. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-03-27T04:04:11.619384-05:
      DOI: 10.1002/sta4.79
       
  • Non‐parametric Bayes to infer playing strategies adopted in a
           population of mobile gamers
    • Authors: Seppo Virtanen; Mattias Rost, Matthew Higgs, Alistair Morrison, Matthew Chalmers, Mark Girolami
      Abstract: Analysis of trace logging data collections of interactions of a heterogenous and diverse population of consumers of digital software with mobile devices provides unprecedented possibilities for understanding how software is actually used and for finding recurring patterns of software usage over the population that are exhibited to a greater or lesser degree in each individual software user. In this work, we consider an elementary mobile game played by a population of mobile gamers and collect pieces of game sessions over an extended period, resulting in a collection of users' trace logs for multiple sessions. We develop a simple, yet flexible, non‐parametric Bayes approach to infer playing strategies adopted in the population from the logged traces of game interactions. We demonstrate that our approach finds interpretable strategies and provides good predictive performance compared with alternative modelling assumptions using a non‐parametric Bayes framework. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-03-04T03:59:11.414744-05:
      DOI: 10.1002/sta4.75
       
  • Unbiased regression estimation under correlated linkage errors
    • Authors: Gunky Kim; Raymond Chambers
      Abstract: Linkage errors can occur when probability‐based methods are used to link records from two or more distinct data sets corresponding to the same target population. Recent research on allowing for these errors when carrying out regression analysis based on linked data assumes that the linkage errors are independent when more than two data sets are used to generate these data. In this paper, we extend these results to accommodate the more realistic scenario of dependent linkage errors. Our simulation results show that an incorrect assumption of independent linkage errors can lead to insufficient linkage error bias correction, while an approach that allows for correlated linkage errors appears to overcome this problem. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-03-02T06:49:03.886206-05:
      DOI: 10.1002/sta4.76
       
  • Spanifold: spanning tree flattening onto lower dimension
    • Authors: Shoja'eddin Chenouri; Petr Kobelevskiy, Christopher G. Small
      Abstract: Dimensionality reduction and manifold learning techniques attempt to recover a lower‐dimensional submanifold from the data as encoded in high dimensions. Many techniques, linear or non‐linear, have been introduced in the literature. Standard methods, such as Isomap and local linear embedding, map the high‐dimensional data points into a low dimension so as to globally minimize a so‐called energy function, which measures the mismatch between the precise geometry in high dimensions and the approximate geometry in low dimensions. However, the local effects of such minimizations are often unpredictable, because the energy minimization algorithms are global in nature. In contrast to these methods, the Spanifold algorithm of this paper constructs a tree on the manifold and flattens the manifold in such a way as to approximately preserve pairwise distance relationships within the tree. The vertices of this tree are the data points, and the edges of the tree form a subset of the edges of the nearest‐neighbour graph on the data. In addition, the pairwise distances between data points close to the root of the tree undergo minimal distortion as the data are flattened. This allows the user to design the flattening algorithm so as to approximately preserve neighbour relationships in any chosen local region of the data. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-02-23T04:25:55.709429-05:
      DOI: 10.1002/sta4.74
       
  • Issue Information
    • Abstract: No abstract is available for this article.
      PubDate: 2015-02-16T02:36:03.549675-05:
      DOI: 10.1002/sta4.63
       
  • Correcting for non‐ignorable missingness in smoking trends
    • Authors: Juho Kopra; Tommi Härkänen, Hanna Tolonen, Juha Karvanen
      Abstract: Data missing not at random (MNAR) are a major challenge in survey sampling. We propose an approach based on registry data to deal with non‐ignorable missingness in health examination surveys. The approach relies on follow‐up data available from administrative registers several years after the survey. For illustration, we use data on smoking prevalence in Finnish National FINRISK study conducted in 1972–97. The data consist of measured survey information including missingness indicators, register‐based background information and register‐based time‐to‐disease survival data. The parameters of missingness mechanism are estimable with these data although the original survey data are MNAR. The underlying data generation process is modelled by a Bayesian model. The results indicate that the estimated smoking prevalence rates in Finland may be significantly affected by missing data. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-01-29T22:52:50.683143-05:
      DOI: 10.1002/sta4.73
       
  • On sparse representation for optimal individualized treatment selection
           with penalized outcome weighted learning
    • Authors: Rui Song; Michael Kosorok, Donglin Zeng, Yingqi Zhao, Eric Laber, Ming Yuan
      Pages: 59 - 68
      Abstract: As a new strategy for treatment, which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules for patients who have heterogeneous responses to treatment is one of the important areas in developing personalized medicine. As more and more information per individual is being collected in clinical studies and not all of the information is relevant for treatment discovery, variable selection becomes increasingly important in discovering individualized treatment rules. In this article, we develop a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. We show that the resulting estimator of the treatment rule is consistent and establish variable selection consistency and the asymptotic distribution of the estimators. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-03-06T01:43:09.313932-05:
      DOI: 10.1002/sta4.78
       
  • Visuanimation in statistics
    • Authors: Marc G. Genton; Stefano Castruccio, Paola Crippa, Subhajit Dutta, Raphaël Huser, Ying Sun, Sabrina Vettori
      Pages: 81 - 96
      Abstract: This paper explores the use of visualization through animations, coined visuanimation, in the field of statistics. In particular, it illustrates the embedding of animations in the paper itself and the storage of larger movies in the online supplemental material. We present results from statistics research projects using a variety of visuanimations, ranging from exploratory data analysis of image data sets to spatio‐temporal extreme event modelling; these include a multiscale analysis of classification methods, the study of the effects of a simulated explosive volcanic eruption and an emulation of climate model output. This paper serves as an illustration of visuanimation for future publications in Stat. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-04-14T02:35:06.1195-05:00
      DOI: 10.1002/sta4.77
       
  • A family of likelihood functions to make inferences about the reliability
           parameter for many stress‐strength distributions
    • Authors: José A. Montoya; Gudelia Figueroa
      Pages: 117 - 129
      Abstract: Many research papers in statistical literature address the estimation of the reliability parameter in stress‐strength models, considering different types of distributions for stress and for strength. We have found that for many of these distributions, their corresponding profile likelihood functions of the reliability parameter can be grouped in a family of likelihood functions, with a simple algebraic structure that facilitates making inferences about this parameter. The novel family of likelihood functions, proposed here, maximum likelihood estimation procedures and suitable reparameterizations, were used to obtain a simple closed‐form expression for the likelihood confidence interval of the reliability parameter. This new approach is particularly useful when small and/or unequal sample sizes are involved. Simulation studies for some distributions were carried out to illustrate the performance of the likelihood confidence intervals for the reliability parameter, and adequate coverage frequencies were obtained. The simplicity of our unifying proposal is shown here using three stress‐strength distributions that have been analysed individually in statistical literature. However, there are many distributions for which inferences about the reliability parameter could be easily obtained using the proposed family. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-05-27T02:16:01.221211-05:
      DOI: 10.1002/sta4.83
       
  • Modelling space–time varying ENSO teleconnections to droughts in
           North America
    • Authors: InKyung Choi; Bo Li, Hao Zhang, Yun Li
      Pages: 140 - 156
      Abstract: Teleconnection in atmospheric science refers to a significant correlation between climate anomalies in widely separated regions (typically thousands of kilometres), and it is often considered to be responsible for extreme weather conditions occurring simultaneously over large distances. In this paper, we study the influence of El Niño‐Southern Oscillation teleconnection on meteorological droughts represented by the Palmer severity drought index across North America from 1870 to 1990. We develop a flexible statistical framework based on spatial random effects to model the covariance (teleconnection) between winter (October–March) sea surface temperature in the tropical Pacific and summer (June–August) droughts in North America. Our model allows us to analyse the dynamic pattern of teleconnection over space and time, and results indicate that the influence of El Niño‐Southern Oscillation teleconnections on droughts varies spatially and temporally across North America. We further provide the time‐varying teleconnection estimates with their uncertainties for 12 subregions in North America. Copyright ©2015 John Wiley & Sons, Ltd.
      PubDate: 2015-06-09T19:30:07.090421-05:
      DOI: 10.1002/sta4.85
       
  • Preconditioning for classical relationships: a note relating ridge
           regression and OLS p‐values to preconditioned sparse penalized
           regression
    • Authors: Karl Rohe
      Pages: 157 - 166
      Abstract: When the design matrix has orthonormal columns, “soft thresholding” the ordinary least squares solution produces the Lasso solution. If one uses the Puffer preconditioned Lasso, then this result generalizes from orthonormal designs to full rank designs (Theorem 1). Theorem 2 refines the Puffer preconditioner to make the Lasso select the same model as removing the elements of the ordinary least squares solution with the largest p‐values. Using a generalized Puffer preconditioner, Theorem 3 relates ridge regression to the preconditioned Lasso; this result is for the high‐dimensional setting, p > n. Where the standard Lasso is akin to forward selection, Theorems 1, 2, and 3 suggest that the preconditioned Lasso is more akin to backward elimination. These results hold for sparse penalties beyond; for a broad class of sparse and non‐convex techniques (e.g. SCAD and MC+), the results hold for all local minima. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-06-09T19:30:52.330174-05:
      DOI: 10.1002/sta4.86
       
  • Covariance models on the surface of a sphere: when does it matter?
    • Authors: Jaehong Jeong; Mikyoung Jun
      Pages: 167 - 182
      Abstract: There is a growing interest in developing covariance functions for processes on the surface of a sphere because of the wide availability of data on the globe. Utilizing the one‐to‐one mapping between the Euclidean distance and the great circle distance, isotropic and positive definite functions in a Euclidean space can be used as covariance functions on the surface of a sphere. This approach, however, may result in physically unrealistic distortion on the sphere especially for large distances. We consider several classes of parametric covariance functions on the surface of a sphere, defined with either the great circle distance or the Euclidean distance, and investigate their impact upon spatial prediction. We fit several isotropic covariance models to simulated data as well as real data from National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis on the sphere. We demonstrate that covariance functions originally defined with the Euclidean distance may not be adequate for some global data. Copyright © 2015 John Wiley & Sons, Ltd.
      PubDate: 2015-06-10T20:22:26.94822-05:0
      DOI: 10.1002/sta4.84
       
  • 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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