Journal of the Royal Statistical Society Series C (Applied Statistics) Template
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About the Journal of the Royal Statistical Society Series C (Applied Statistics) format
Journal of the Royal Statistical Society Series C (Applied Statistics) is a peer-reviewed journal published by Oxford University Press, covering Advanced Statistical Methods and Models, Statistical Methods and Bayesian Inference, Statistical Methods and Inference.
| Publisher | Oxford University Press |
|---|---|
| Reference style | Author–year (OUP) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', Journal of the Royal Statistical Society Series C (Applied Statistics), 12(3), pp. 45–58.
Formats any DOI in the closest standard style — Journal of the Royal Statistical Society Series C (Applied Statistics) has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Advanced Statistical Methods and Models Statistical Methods and Bayesian Inference Statistical Methods and Inference Bayesian Methods and Mixture Models Statistical Methods in Clinical Trials |
| ISSN | 0035-9254 |
| Citation impact (2-yr) | 1.49 |
| h-index | 192 |
| i10-index | 1,970 |
| Total citations | 204,658 |
| Top institutions publishing here | Lancaster University |
| Journal website | academic.oup.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Journal of the Royal Statistical Society Series C (Applied Statistics) per year
Citation impact of Journal of the Royal Statistical Society Series C (Applied Statistics) by publication year
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Most-cited papers in Journal of the Royal Statistical Society Series C (Applied Statistics)
Non‐parametric techniques are introduced for the change‐point problem. Exact and approximate results are obtained for testing the null hypothesis of no change. The methods are illustrated by the analysis of three sets of data illustrating the techniques for zero–one observations, Binomial observations and continuous observations. Some comparisons are made with methods based on cusums.
Summary A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response…
SUMMARY The technique set out in the paper, CHAID, is an offshoot of AID (Automatic Interaction Detection) designed for a categorized dependent variable. Some important modifications which are relevant to standard AID include: built-in significance testing with the consequence of using the most significant predictor (rather than the most explanatory), multi-way splits (in contrast to…