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Journal Cover   Stat
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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  [1607 journals]
  • 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
    • 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
  • 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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