Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Vincent Dorie"'
Publikováno v:
Entropy, Vol 24, Iss 12, p 1782 (2022)
A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating a
Externí odkaz:
https://doaj.org/article/1136357fb60e4960a89e7b0014b5afba
Publikováno v:
Handbook of Matching and Weighting Adjustments for Causal Inference ISBN: 9781003102670
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd128a728a91621e6efdbb51e1cb6706
https://doi.org/10.1201/9781003102670-20
https://doi.org/10.1201/9781003102670-20
Publikováno v:
Translational Psychiatry, Vol 9, Iss 1, Pp 1-12 (2019)
Translational Psychiatry
Translational Psychiatry
Similar environmental risk factors have been implicated in different neuropsychiatric disorders (including major psychiatric and neurodegenerative diseases), indicating the existence of common epigenetic mechanisms underlying the pathogenesis shared
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1659efad713643ec62df18f16192eabf
http://link.springer.com/article/10.1038/s41398-018-0358-5
http://link.springer.com/article/10.1038/s41398-018-0358-5
Publikováno v:
Observational Studies. 5:52-70
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b57ecf518e87d6a748f65cd220d3c07
https://doi.org/10.1353/obs.2019.0002
https://doi.org/10.1353/obs.2019.0002
Publikováno v:
Statist. Sci. 34, no. 1 (2019), 43-68
Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However, this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f63ec8ef0829b703ca047a45714d1cc
https://projecteuclid.org/euclid.ss/1555056030
https://projecteuclid.org/euclid.ss/1555056030
Publikováno v:
Statist. Sci. 34, no. 1 (2019), 94-99
Response to discussion of Dorie (2017), in which the authors of that piece express their gratitude to the discussants, rebut some specific criticisms, and argue that the limitations of the 2016 Atlantic Causal Inference Competition represent an excit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4594948f52cad4c2b53085c9aa402d19
https://doi.org/10.1214/18-sts688
https://doi.org/10.1214/18-sts688
Publikováno v:
Journal of Educational and Behavioral Statistics. 40:136-157
When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cbdd49c17a854b9471cdd152fa3befc7
https://doi.org/10.3102/1076998615570945
https://doi.org/10.3102/1076998615570945
Publikováno v:
Psychometrika. 78:685-709
Group-level variance estimates of zero often arise when fitting multilevel or hierarchical linear models, especially when the number of groups is small. For situations where zero variances are implausible a priori, we propose a maximum penalized like
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::330b4fcc0ceff17396156791f4c7b278
https://doi.org/10.1007/s11336-013-9328-2
https://doi.org/10.1007/s11336-013-9328-2
Publikováno v:
Statistics in Medicine
When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c376db9f3034febe29e56052d21e471
https://pubmed.ncbi.nlm.nih.gov/27139250
https://pubmed.ncbi.nlm.nih.gov/27139250