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Journal of the Royal Statistical Society Series B (Statistical Methodology) is a peer-reviewed journal published by Oxford University Press, covering Statistical Methods and Inference, Advanced Statistical Methods and Models, Statistical Methods and Bayesian 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 B (Statistical Methodology), 12(3), pp. 45–58.
Formats any DOI in the closest standard style — Journal of the Royal Statistical Society Series B (Statistical Methodology) has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Statistical Methods and Inference Advanced Statistical Methods and Models Statistical Methods and Bayesian Inference Bayesian Methods and Mixture Models Optimal Experimental Design Methods |
| ISSN | 1369-7412 |
| Citation impact (2-yr) | 1.82 |
| h-index | 310 |
| i10-index | 2,570 |
| Total citations | 753,539 |
| Top institutions publishing here | University of Cambridge |
| Journal website | academic.oup.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses — the false discovery rate. This…
SUMMARY We propose a new method for estimation in linear models. The ‘lasso’ minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives…
Summary A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component…
Summary The analysis of censored failure times is considered. It is assumed that on each individual are available values of one or more explanatory variables. The hazard function (age-specific failure rate) is taken to be a function of the explanatory variables and unknown regression coefficients multiplied by an arbitrary and unknown function of time. A…
Summary We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out…
