Write in a clean editor, then format for Stat in one click — DocuGuru applies the official Wiley template with author–year references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Stat is a peer-reviewed journal published by Wiley, covering Statistical Methods and Inference, Statistical Methods and Bayesian Inference, Advanced Statistical Methods and Models.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Stat 12 (3): 45–58.
Formats any DOI in Stat style. No sign-up. |
| Publishes research in | Statistical Methods and Inference Statistical Methods and Bayesian Inference Advanced Statistical Methods and Models Bayesian Methods and Mixture Models Statistical Distribution Estimation and Applications |
| ISSN | 2049-1573 |
| Citation impact (2-yr) | 0.76 |
| h-index | 28 |
| i10-index | 127 |
| Total citations | 4,661 |
| Article processing charge | $3,550 |
| Top institutions publishing here | North Carolina State University |
| Journal website | onlinelibrary.wiley.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.
A treatment regime maps observed patient characteristics to a recommended treatment. Recent technological advances have increased the quality, accessibility, and volume of patient-level data; consequently, there is a growing need for powerful and flexible estimators of an optimal treatment regime that can be used with either observational or randomized clinical trial data. We propose a…
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…
D&R is a new statistical approach to the analysis of large complex data. The data are divided into subsets. Computationally, each subset is a small dataset. Analytic methods are applied to each of the subsets, and the outputs of each method are recombined to form a result for the entire data. Computations can be run…
A new method to measure nonlinear dependence between two variables is described using mutual information to analyse the separate linear and nonlinear components of dependence. This technique, which gives an exact value for the proportion of linear dependence, is then compared with another common test for linearity, the Brock, Dechert and Scheinkman test. Copyright ©…
We consider dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated times and at each time a functional observation (curve) is recorded. We propose a novel parsimonious modeling framework for repeatedly observed functional observations that allows to extract low dimensional features. The proposed methodology accounts for the…
