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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  [1611 journals]
  • 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
      Pages: n/a - n/a
      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
      Pages: n/a - n/a
      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
      Pages: n/a - n/a
      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
    • Pages: i - iii
      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
      Pages: n/a - n/a
      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
       
  • 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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