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Most Important Statistical Ideas of the Past 50 Years
https://arxiv.org/pdf/2012.00174.pdf
Created by
Anna Menacher
⟶ Updated 18 Jan 2021 ⟶
List of edits
Comments
Robby
And Mockus (2012) links to a book from 1989.
17 Jun 2022
Robby
Expectation propagation (Heskes et al. 2005) seems misplaced (too far to the left, also says it was 23 years ago) too.
17 Jun 2022
Robby
Good & Gaskins 1971 seems misplaced in the chart
17 Jun 2022
Events
Causal Inference (Heckman and Pinto (2015))
Bootstrap (Efron (1979))
Bootstrap (Efron and Tibshirani (1993))
Bootstrap (Geisser (1975))
Problems in plane sampling (Quenouille (1949))
Bias and confidence in not quite large samples (Tukey (1958))
Cross-validation (Stone (1974))
Confidence limits on phylogenies (Felsenstein (1985))
Prior and posterior predictive checking (Box (1980))
Causal Inference (Imbens and Angrist (1994))
Causal Inference (Greenland and Robins (1986))
Causal Inference (Cronbach (1975))
Causal Inference (Rosenbaum and Rubin (1983))
Causal Inference (Pearl (2009))
Causal Inference (Neyman (1923))
Causal Inference (Welch (1937))
Causal Inference (Rubin (1974))
Causal Inference (Haavelmo (1943))
Prior and posterior predictive checks (Rubin (1984))
Simulation-based calibration (Talts et al. (2020))
Regularization (Good and Gaskins (1971)
Rich models with MRFs (Besag (1974))
Splines (Wahba and Wold (1975))
Splines (Wahba (1978))
Gaussian Processes (O'Hagan (1978))
Classification and regression trees (Breiman et al. (1984))
Neural Networks Werbos (1981)
Neural Networks (Rumelhart et al. (1986))
Bayesian Back-Propagation (Buntine and Weigend (1991))
Backpropagation Networks (MacKay (1992))
Bayesian training of backpropagation networks (Neal (1992))
Wavelet Shrinkage (Donoho and Johnstone (1994))
Alternatives to OLS (Dempster et al. (1977))
LASSO (Tibshirani (1996))
The horseshoe estimator for sparse signals (Carvalho et al. (2010))
Support Vector Machines (Cortes and Vapnik (1995))
Theory of SVMs (Vapnik (1998))
Deep Neural Networks (Bengio et al. (2015)) (Schmidhuber (2015)
Estimation of sparse structure (Hastie et al. (2015))
Nonnegative matrix factorization (Paatero and Tapper (1994))
Nonlinear Dimensionality Reduction (Lee and Verleysen (2007))
Generative Adversarial Networks (Goodfellow et al. (2014))
Autoencoders (Goodfellow et al. (2016))
Stacking (Wolpert (1992)
Bayesian Model Averaging (Hoeting et al. (1999))
Boosting (Freund et al. (1997))
Gradient Boosting (Friedman (2001))
Random Forests (Breiman (2001))
Partial Pooling (Hendersen et al. (1959))
Partial Pooling (Stein (1955))
Theory of partial pooling (James and Stein (1960)
Multilevel models (psychology) (Novick et al. (1972))
Multilevel models (pharmacology) (Sheiner et al. (1972))
Multilevel models (survey sampling) (Fay and Herriot (1972))
Bayes estimates for the linear model (Lindley and Smith (1972))
The role of exchangeability in inference (Lindley and Novick (1981))
Limiting the risk of Bayes and empirical Bayes estimators (Efron and Morris (1971, 1972)
Multilevel models + structured data (Liang and Zeger (1986))
Multilevel models + structured data (Lax and Philips (2012))
Information-theoretic justification of multivariate parameters (Donoho (1995))
EM algorithm (Dempster et al. (1977))
EM algorithm (Meng and van Dyk (1997))
Gibbs sampler (Geman and Geman (1984))
Gibbs sampler (Gelfand and Smith (1990))
Particle filters (Kitagawa (1993))
Particle filters (Gordon et al. (1993))
Particle filters (Del Moral (1996))
Variational inference (Jordan et al. (1999))
Expectation propagation (Minka (2001))
Expectation Propagation (Heskes et al. (2005))
Metropolis algorithm (Hastings (1970))
Hybrid Monte Carlo (Duane et al. (1987))
Approximate Bayesian Computation (Rubin (1984))
Approximate Bayesian Computation (Tavaré et al. (1997))
Approximate Bayesian Computation (Marin et al. (2012))
Statistical decision functions (Wald (1949))
Decision theory via utility maximization (Savage (1954))
The problem of multiple comparisons (Tukey (1953))
The analysis of variance (Scheffé (1959))
Empirical Bayes analysis (Robbins (1959))
Empirical Bayes analysis (Robbins (1964))
Bayesian decision theory (Berger (1985))
False discovery rate analysis (Benjamini and Hochberg (1995))
Decision theory + Human Decision Making (Kahneman et al. (1982))
Decision theory + Human Decision Making (Gigerenzer and Todd (1999))
Bayesian optimization (Mockus (1974))
Bayesian optimization (Mockus (2012))
Reinforcement Learning (Sutton and Barto (2018))
Artificial Inteligence: Go (Silver et al. (2017))
Robust inference (Tukey (1960))
Robust Inference (Stigler (2010))
Theory of robust inference (Huber (1972))
Robust Standard Error (White (1980))
Partial Identification (Manski (1990))
M-open world (Bernardo and Smith (1994))
Sources of Error (Greenland (2005))
Model Evaluation (Navarro (2019))
Exploratory Data Analysis (Tukey (1977))
The Visual Display of Quantitative Information (Tufte (1983))
S (Chambers et al. (1983))
Exploratory Data Analysis (Tukey (1962))
Open-ended data exploration (Cleveland (1985))
50 years of data science (Donoho (2017))
Graphical methods instead of correlations in medicine (Bland and Altman (1986))
Formalize EDA (Gelman (2003))
ggplot2 (Wickham (2016))
Visualization in Bayesian Workflow (Gabry et al. (2019))
Bayesian workflow (Gelman et al. (2020))
