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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  [1606 journals]
  • 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
  • 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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