for Journals by Title or ISSN
for Articles by Keywords
help
Followed Journals
Journal you Follow: 0
 
Sign Up to follow journals, search in your chosen journals and, optionally, receive Email Alerts when new issues of your Followed Journals are published.
Already have an account? Sign In to see the journals you follow.
Journal Cover Stat
  [SJR: 0.985]   [H-I: 5]   [1 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Online) 2049-1573
   Published by John Wiley and Sons Homepage  [1605 journals]
  • Covariate selection for multilevel models with missing data
    • Authors: Miguel Marino; Orfeu M. Buxton, Yi Li
      Abstract: Missing covariate data hamper variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods that are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data are present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyse the Healthy Directions–Small Business cancer prevention study, which evaluated a behavioural intervention programme targeting multiple risk-related behaviours in a working-class, multi-ethnic population. Copyright © 2017 John Wiley & Sons, Ltd.
      PubDate: 2017-01-08T18:50:26.241275-05:
      DOI: 10.1002/sta4.133
       
  • Time-varying rankings with the Bayesian Mallows model
    • Authors: Derbachew Asfaw; Valeria Vitelli, Øystein Sørensen, Elja Arjas, Arnoldo Frigessi
      Abstract: We present new statistical methodology for analysing rank data, where the rankings are allowed to vary in time. Such data arise, for example, when the assessments are based on a performance measure of the items, which varies in time, or if the criteria, according to which the items are ranked, change in time. Items can also be absent when the assessments are made, because of delayed entry or early departure, or purely randomly. In such situations, also the dimension of the rank vectors varies in time. Rank data in a time-dependent setting thus lead to challenging statistical problems. These problems are further complicated, from the perspective of computation, by the large dimension of the sample space consisting of all permutations of the items. Here, we focus on introducing and developing a Bayesian version of the Mallows rank model, suitable for situations in which the ranks vary in time and the assessments can be incomplete. The consequent missing data problems are handled by applying Bayesian data augmentation within Markov chain Monte Carlo. Our method is also adapted to the task of future rank prediction. The method is illustrated by analysing some aspects of a data set describing the academic performance, measured by a series of tests, of a class of high school students over a period of 4 years. Copyright © 2016 John Wiley & Sons, Ltd.
      PubDate: 2016-12-28T01:05:33.790261-05:
      DOI: 10.1002/sta4.132
       
  • Issue Information
    • Pages: 1 - 3
      Abstract: No abstract is available for this article.
      PubDate: 2016-12-14T23:15:39.084732-05:
      DOI: 10.1002/sta4.117
       
  • Inferring population size: extending the multiplier method to incorporate
           multiple traits with a likelihood-based approach
    • Authors: Vivian Yun Meng; Paul Gustafson
      Pages: 4 - 13
      Abstract: Estimating population size is an important task for resource planning and policy making. One method is the “multiplier method” that uses information about a binary trait to infer the size of a population. This paper presents a likelihood-based estimator that generalizes the multiplier method to accommodate multiple traits as well as any number of categories in a trait. To provide guidelines for study design, we quantify the advantage of using multiple traits (multiple multipliers) by studying the estimator's asymptotic standard deviation (ASD). Inclusion of multiple traits reduces the ASD most effectively when the traits are uncorrelated and of low prevalence (roughly less than 10%), but the amount of reduction in ASD diminishes when the number of traits becomes large. A Bayesian implementation of our method is applied to both simulated data and real data pertaining to an injection-drug user population. Copyright © 2016 John Wiley & Sons, Ltd.
      PubDate: 2016-12-09T00:35:24.919233-05:
      DOI: 10.1002/sta4.131
       
  • 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
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.159.158.180
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2016