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Stat

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ISSN (Online) 2049-1573
Published by John Wiley and Sons

[1587 journals]

Variable selection in generalized functional linear models
- Authors: Jan Gertheiss; Arnab Maity, Ana‐Maria Staicu
  Pages: n/a - n/a
  Abstract: Modern research data, where a large number of functional predictors is collected on few subjects are becoming increasingly common. In this paper we propose a variable selection technique, when the predictors are functional and the response is scalar. Our approach is based on adopting a generalized functional linear model framework and using a penalized likelihood method that simultaneously controls the sparsity of the model and the smoothness of the corresponding coefficient functions by adequate penalization. The methodology is characterized by high predictive accuracy, and yields interpretable models, while retaining computational efficiency. The proposed method is investigated numerically in finite samples, and applied to a diffusion tensor imaging tractography data set and a chemometric data set. Copyright © 2013 John Wiley & Sons, Ltd.
  PubDate: 2013-05-09T00:52:25.956569-05:
  DOI: 10.1002/sta4.20
Combined analysis of correlated data when data cannot be pooled
- Authors: Elinor M. Jones; Nuala A. Sheehan, Amadou Gaye, Philippe Laflamme, Paul Burton
  Pages: n/a - n/a
  Abstract: In genetic epidemiology studies, associations between individual genetic variants and phenotypes of interest are generally weak requiring large samples to estimate effects and to address complex statistical questions. Such sample sizes are often only achievable by pooling data from multiple studies; effects of interest can then be investigated through an individual‐level meta‐analysis (ILMA) on the pooled data, or by conducting a conventional study‐level meta‐analysis (SLMA). However, pooling individual‐level research data for an ILMA is not always possible, and researchers may be compelled to conduct an SLMA instead, restricting the sharing to non‐disclosing summary statistics. In certain settings, an individual‐level analysis can be conducted without pooling the data from the different studies. It has already been shown that when data are horizontally partitioned between studies, i.e. data are collected on the same variables in each study but any given study participant appears in one study only, it is possible to fit a generalised linear model in this way. In the present paper, we demonstrate that an individual‐level generalised estimating equations meta‐analysis can be achieved in an analogous manner. This extends the scope of ILMA without data pooling to problems involving correlated and clustered responses. Copyright © 2013 John Wiley & Sons, Ltd.
  PubDate: 2013-04-28T21:59:16.644582-05:
  DOI: 10.1002/sta4.19
A point process model for tornado report climatology
- Authors: Dmitriy Karpman; Marco A.R. Ferreira, Christopher K. Wikle
  Pages: 1 - 8
  Abstract: We propose a point process model with multiplicative risk for the study of tornado reports in the United States. In particular, we implement a rigorous statistical procedure to evaluate whether tornado report counts are significantly related to topographic variability. The model we propose also includes flexible nonparametric components for spatial and seasonality effects. We apply the proposed model and methodology to the analysis of tornado report data from 1953 to 2010 in the United States. Our analysis shows that in addition to the spatial and seasonal effects, the topographic variability is an important component of tornado risk. Copyright © 2013 John Wiley & Sons, Ltd.
  PubDate: 2013-01-14T04:01:25.742853-05:
  DOI: 10.1002/sta4.14
Aligning some Nicholson sheep‐blowfly data sets with system input periods
- Authors: David R. Brillinger
  Pages: 9 - 21
  Abstract: During the 1950s the Australian entomologist Alexander Nicholson studied a sheep pest, lucilia cuprina, (L cuprina), the sheep‐blowfly. In laboratory experiments blowfly populations were set up in cages. They were supplied with necessary food and water and every other day counts were made of the numbers in their various stages of development. The experiments went on for over a year. Various statistical studies have been carried out on their data. Sadly, the bulk of the data appears to be lost. Recently this author made the discovery of total population counts for ten Nicholson experiments. These data were in a collection of copies of index cards he made during a trip to Australia in 1977. In eight of the experiments the input food was varied cyclically in sawtooth fashion, each experiment having a different period of application. However, and what is the concern of this article, which data set went with which period of application remains unclear. In the present study use is made of periodograms, spectrograms and seasonal adjustment to seek a one‐to‐one correspondence between series and period. The estimate constructed is consistent under smoothing and limiting conditions. It is time domain based, but confirmed by periodogram and spectrogram computation. Copyright © 2013 John Wiley & Sons, Ltd.
  PubDate: 2013-02-04T08:17:32.303456-05:
  DOI: 10.1002/sta4.13
On the assessment of multivariate and multisite measurement systems
- Authors: Michele Scagliarini
  Pages: 22 - 33
  Abstract: The multivariate Measurement Systems Analysis (MSA) approval criteria proposed in literature are specifically designed for assessing measurement systems made up of a single instrument. Therefore, they may encounter difficulties in assessing multisite measurement systems where there are multiple instruments in parallel. In this work, we propose a method for assessing such complex measurement systems. Since a key assumption in multisite measurement systems is that all instruments are expected to have the same level of precision, we base our proposal on the comparison of the precisions of multivariate instruments by means of a statistical test. A simulation study is performed in order to evaluate the performances of the proposed method. The results show that the illustrated approach is effective for assessing complex measurement systems and can be useful for reducing the costs for performing multivariate MSA. Copyright © 2013 John Wiley & Sons, Ltd.
  PubDate: 2013-01-31T07:31:59.809187-05:
  DOI: 10.1002/sta4.16
Approximate Bayesian computation via regression density estimation
- Authors: Yanan Fan; David J. Nott, Scott A. Sisson
  Pages: 34 - 48
  Abstract: Approximate Bayesian computation (ABC) methods, which are applicable when the likelihood is difficult or impossible to calculate, are an active topic of current research. Most current ABC algorithms directly approximate the posterior distribution, but an alternative, less common strategy is to approximate the likelihood function. This has several advantages. First, in some problems, it is easier to approximate the likelihood than to approximate the posterior. Second, an approximation to the likelihood allows reference analyses to be constructed based solely on the likelihood. Third, it is straightforward to perform sensitivity analyses for several different choices of prior once an approximation to the likelihood is constructed, which needs to be done only once. The contribution of the present paper is to consider regression density estimation techniques to approximate the likelihood in the ABC setting. Our likelihood approximations build on recently developed marginal adaptation density estimators by extending them for conditional density estimation. Our approach facilitates reference Bayesian inference, as well as frequentist inference. The method is demonstrated via a challenging problem of inference for stereological extremes, where we perform both frequentist and Bayesian inference. Copyright © 2013 John Wiley & Sons, Ltd.
  PubDate: 2013-02-05T01:02:37.576056-05:
  DOI: 10.1002/sta4.15
Metrics for SiZer map comparison
- Authors: Jan Hannig; Thomas C.M. Lee, Cheolwoo Park
  Pages: 49 - 60
  Abstract: SiZer is a powerful visualization tool for uncovering real structures masked in noisy data. It produces a two‐dimensional plot, the so‐called SiZer map, to help the data analyst to carry out this task. Since its first proposal, many different extensions and improvements have been developed, including robust SiZer, quantile SiZer, and various SiZers for time series data, just to name a few. Given these many SiZer variants, one important question is, how can one evaluate the quality of a SiZer map produced by any one of these variants' The primary goal of this article aims to answer this question by proposing two metrics for quantifying the discrepancy between any two SiZer maps. With such metrics, one can systematically calculate the distance between a “true” SiZer map and a SiZer map produced by any one of the SiZer variants. Consequently, one can select a “best” SiZer variant for the problem at hand by selecting the variant that produces SiZer maps that are, on average, closest to the “true” SiZer map. Copyright © 2013 John Wiley & Sons, Ltd.
  PubDate: 2013-02-10T21:38:35.408827-05:
  DOI: 10.1002/sta4.17
Variational inference for marginal longitudinal semiparametric regression
- Authors: Marianne Menictas; Matt P. Wand
  Pages: 61 - 71
  Abstract: We derive a variational inference procedure for approximate Bayesian inference in marginal longitudinal semiparametric regression. Fitting and inference is much faster than existing Markov chain Monte Carlo approaches. Numerical studies indicate that the new methodology is very accurate for the class of models under consideration. Copyright © 2013 John Wiley & Sons, Ltd.
  PubDate: 2013-02-11T02:19:06.447722-05:
  DOI: 10.1002/sta4.18
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