L
Leonhard Held
Researcher at University of Zurich
Publications - 277
Citations - 13209
Leonhard Held is an academic researcher from University of Zurich. The author has contributed to research in topics: Bayesian probability & Bayesian inference. The author has an hindex of 47, co-authored 247 publications receiving 10897 citations. Previous affiliations of Leonhard Held include Ludwig Maximilian University of Munich & Prevention Institute.
Papers
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Book
Gaussian Markov Random Fields: Theory and Applications
Håvard Rue,Leonhard Held +1 more
TL;DR: This volume is essential reading for statisticians working in spatial theory and its applications, as well as quantitative researchers in a wide range of science fields where spatial data analysis is important.
Journal ArticleDOI
Redefine statistical significance
Daniel J. Benjamin,James O. Berger,Magnus Johannesson,Magnus Johannesson,Brian A. Nosek,Brian A. Nosek,Eric-Jan Wagenmakers,Richard A. Berk,Kenneth A. Bollen,Björn Brembs,Lawrence D. Brown,Colin F. Camerer,David Cesarini,David Cesarini,Christopher D. Chambers,Merlise A. Clyde,Thomas D. Cook,Thomas D. Cook,Paul De Boeck,Zoltan Dienes,Anna Dreber,Kenny Easwaran,Charles Efferson,Ernst Fehr,Fiona Fidler,Andy P. Field,Malcolm R. Forster,Edward I. George,Richard Gonzalez,Steven N. Goodman,Edwin J. Green,Donald P. Green,Anthony G. Greenwald,Jarrod D. Hadfield,Larry V. Hedges,Leonhard Held,Teck-Hua Ho,Herbert Hoijtink,Daniel J. Hruschka,Kosuke Imai,Guido W. Imbens,John P. A. Ioannidis,Minjeong Jeon,James Holland Jones,Michael Kirchler,David Laibson,John A. List,Roderick J. A. Little,Arthur Lupia,Edouard Machery,Scott E. Maxwell,Michael A. McCarthy,Don A. Moore,Stephen L. Morgan,Marcus R. Munafò,Shinichi Nakagawa,Brendan Nyhan,Timothy H. Parker,Luis R. Pericchi,Marco Perugini,Jeffrey N. Rouder,Judith Rousseau,Victoria Savalei,Felix D. Schönbrodt,Thomas Sellke,Betsy Sinclair,Dustin Tingley,Trisha Van Zandt,Simine Vazire,Duncan J. Watts,Christopher Winship,Robert L. Wolpert,Yu Xie,Cristobal Young,Jonathan Zinman,Valen E. Johnson,Valen E. Johnson +76 more
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Posted Content
Redefine Statistical Significance
Daniel J. Benjamin,James O. Berger,Magnus Johannesson,Brian A. Nosek,Eric-Jan Wagenmakers,Richard A. Berk,Kenneth A. Bollen,Björn Brembs,Lawrence D. Brown,Colin F. Camerer,David Cesarini,Christopher D. Chambers,Merlise A. Clyde,Thomas D. Cook,Paul De Boeck,Zoltan Dienes,Anna Dreber,Kenny Easwaran,Charles Efferson,Ernst Fehr,Fiona Fidler,Andy P. Field,Malcom Forster,Edward I. George,Tarun Ramadorai,Richard Gonzalez,Steven N. Goodman,Edwin J. Green,Donald P. Green,Anthony G. Greenwald,Jarrod D. Hadfield,Larry V. Hedges,Leonhard Held,Teck Hau Ho,Herbert Hoijtink,James Holland Jones,Daniel J. Hruschka,Kosuke Imai,Guido W. Imbens,John P. A. Ioannidis,Minjeong Jeon,Michael Kirchler,David Laibson,John A. List,Roderick J. A. Little,Arthur Lupia,Edouard Machery,Scott E. Maxwell,Michael A. McCarthy,Don A. Moore,Stephen L. Morgan,Marcus R. Munafò,Shinichi Nakagawa,Brendan Nyhan,Timothy H. Parker,Luis R. Pericchi,Marco Perugini,Jeffrey N. Rouder,Judith Rousseau,Victoria Savalei,Felix D. Schönbrodt,Thomas Sellke,Betsy Sinclair,Dustin Tingley,Trisha Van Zandt,Simine Vazire,Duncan J. Watts,Christopher Winship,Robert L. Wolpert,Yu Xie,Cristobal Young,Jonathan Zinman,Valen E. Johnson +72 more
TL;DR: This article proposed to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005, which is the threshold used in this paper.
Journal ArticleDOI
Bayesian auxiliary variable models for binary and multinomial regression
Christopher Holmes,Leonhard Held +1 more
TL;DR: A simple technique using joint updating that improves the performance of the conventional probit regression algorithm and is shown how the logistic method is easily extended to multinomial regression models.
Journal ArticleDOI
Predictive model assessment for count data.
TL;DR: Proposals include a nonrandomized version of the probability integral transform, marginal calibration diagrams, and proper scoring rules, such as the predictive deviance, for the evaluation of probabilistic forecasts and the critique of statistical models for count data.