R3 on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(37)

R3(References on References on References) on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(37)

R3 on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(0)
https://qiita.com/kaizen_nagoya/items/a8eac9afbf16d2188901

What are the most important statistical ideas of the past 50 years?
Andrew Gelman, Aki Vehtari
https://arxiv.org/abs/2012.00174

# References 
# 37
Donoho, D. L, and Johnstone, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455.

https://statweb.stanford.edu/~imj/WEBLIST/1994/isaws.pdf

## References on 37
## 37.1
􏰤􏰅􏰦 BICKEL􏰁 P􏰃 J􏰃 􏰞􏰅􏰋􏰢􏰇􏰟􏰃 Minimax estimation of a normal mean sub ject to doing well at a p oint􏰃, In Recent Advances in Statistics 􏰞M􏰃 H􏰃 Rizvi􏰁 J􏰃 S􏰃 Rustagi􏰁 and D􏰃 Siegmund􏰁 eds􏰃􏰟􏰁 Academic Press􏰁 New York􏰁 􏰉􏰅􏰅􏰩􏰉􏰆􏰢􏰃

https://www.semanticscholar.org/paper/MINIMAX-ESTIMATION-OF-THE-MEAN-OF-A-NORMAL-SUBJECT-Bickel/ce26224eb5e97073b39738b30fc41b6f14adb13b

### References on 37.1
### 37.1.1
Limiting the Risk of Bayes and Empirical Bayes Estimators—Part I: The Bayes Case
B. Efron, C. Morris
Mathematics
1971
Abstract The first part of this article considers the Bayesian problem of estimating the mean, θ, of a normal distribution when the mean itself has a normal prior. The usual Bayes estimator for this… Expand
### 37.1.2
The use of Previous Experience in Reaching Statistical Decisions
J. L. Hodges, E. Lehmann
Mathematics
1952
Instead of minimizing the maximum risk it is proposed to re-strict attention to decision procedures whose maximum risk does not exceed the minimax risk by more than a given amount. Subject to this… Expand
### 37.1.3
Robust Estimation of a Location Parameter
P. J. Huber
Mathematics
1964
This paper contains a new approach toward a theory of robust estimation; it treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and… Expand
### 37.1.4
On robust estimation of the location parameter
F. R. Forst
Computer Science
### 37.1.5
A Natural Identity for Exponential Families with Applications in Multiparameter Estimation
H. Hudson
Mathematics
1978
### 37.1.6
FISHER INFORMATION AND THE PITMAN ESTIMATOR OF A LOCATION PARAMETER
S. Port, C. J. Stone
Mathematics
1974
### 37.1.7
" Nonoptimality of Preliminary Test Estimators for the Mean of a Multivariate Normal Distribution
" Ann . Math . Statist .
1972
### 37.1.8
Non-Optimality of Preliminary-Test Estimators for the Mean of a Multivariate Normal Distribution
S. Sclove, C. Morris, R. Radhakrishnan
Mathematics
1972
### 37.1.9
Admissible Estimators, Recurrent Diffusions, and Insoluble Boundary Value Problems
L. Brown
Mathematics
1971
### 37.1.10
Generalized Bayes Solutions in Estimation Problems
J. Sacks
Mathematics
1963

## 37.2
􏰤􏰆􏰦 BREIMAN􏰁 L􏰃􏰁 FRIEDMAN􏰁 J􏰃H􏰃􏰁 OLSHEN􏰁 R􏰃A􏰃􏰁􏰜 STONE􏰁 C􏰃J􏰃 􏰞􏰅􏰋􏰢􏰇􏰟􏰃 CART􏰐 Classi􏰎cation and Regression Trees 􏰃 Wadsworth􏰐 CBelmont􏰁 CA􏰃
## 37.3
􏰤􏰇􏰦 BROCKMANN􏰁 M􏰃􏰁 GASSER􏰁 T􏰃􏰁 􏰜 HERRMANN􏰁 E􏰃 􏰞􏰅􏰋􏰋􏰆􏰟􏰃 Lo cally Adaptive Bandwidth Choice for Kernel Regression Estimators􏰃 To app ear􏰃 J􏰃 Amer􏰃 Statist􏰃 Assoc􏰃􏰃
## 37.4
􏰤􏰈􏰦 BROWN􏰁 L􏰃D􏰃 􏰜 LOW􏰁 M􏰃G􏰃 􏰞􏰅􏰋􏰋􏰇􏰟􏰃 Sup ere􏰏ciency and lack of adaptability in non􏰂 parametric functional estimation􏰃 To app ear􏰁 Annals of Statistics􏰃
## 37.5
􏰤􏰉􏰦 COHEN􏰁 A􏰃􏰁 DAUBECHIES􏰁 I􏰃􏰁 JAWERTH􏰁 B􏰃 􏰜 VIAL􏰁 P􏰃 􏰞􏰅􏰋􏰋􏰇􏰟􏰃 Multiresolution analysis􏰁 wavelets􏰁 and fast algorithms on an interval􏰃 Comptes Rendus Acad􏰃 Sci􏰃 Paris􏰞A􏰟􏰃 􏰇􏰅􏰊􏰃􏰁 􏰈􏰅􏰓􏰩􏰈􏰆􏰅􏰃316. 417-421
## 37.6
􏰤􏰊􏰦 CHUI􏰁 C􏰃K􏰃 􏰞􏰅􏰋􏰋􏰆􏰟􏰃􏰁 An Introduction to Wavelets􏰃 Academic Press􏰁 Boston􏰁 MA􏰃
## 37.7
􏰤􏰓􏰦 DAUBECHIES􏰁 I􏰃 􏰞􏰅􏰋􏰢􏰢􏰟􏰃 Orthonormal bases of compactly supp orted wavelets􏰃 Com􏰂 munications in Pure and Applied Mathematics 􏰈􏰅􏰁 Nov􏰃 􏰅􏰋􏰢􏰢􏰁 pp􏰃 􏰋􏰄􏰋􏰂􏰋􏰋􏰊􏰃
## 37.8
􏰤􏰢􏰦 DAUBECHIES􏰁 I􏰃 􏰞􏰅􏰋􏰋􏰆􏰟􏰃 Ten Lectures on Wavelets SIAM􏰐 Philadelphi a􏰃
## 37.9
􏰤􏰋􏰦 DAUBECHIES􏰁 I􏰃 􏰞􏰅􏰋􏰋􏰇􏰟􏰃 Orthonormal Bases of Compactly Supp orted Wavelets I I􏰐 Variations on a theme􏰃 SIAM J􏰃 Math􏰃 Anal􏰃􏰁 􏰆􏰈􏰁 􏰈􏰋􏰋􏰩􏰉􏰅􏰋􏰃
## 37.10
􏰤􏰅􏰄􏰦 EFROIMOVICH􏰁 S􏰃 YU􏰃 􏰜 PINSKER􏰁 M􏰃S􏰃 􏰞􏰅􏰋􏰢􏰈􏰟􏰃 A learning algorithm for non􏰂 parametric 􏰎ltering􏰃 Automat􏰃 i Telemeh􏰃 􏰅􏰅 􏰉􏰢􏰂􏰊􏰉 􏰞in Russian􏰟􏰃
􏰆􏰉## 37.11
􏰤􏰅􏰅􏰦 FRAZIER M􏰃􏰁 JAWERTH B􏰃􏰁 􏰜 WEISS G􏰃 􏰞􏰅􏰋􏰋􏰅􏰟􏰃 Littlewood􏰂Paley Theory and the study of function spaces􏰃 NSF􏰂CBMS Regional Conf􏰃 Ser in Mathematics􏰁 􏰓􏰋􏰃 American Math􏰃 So c􏰃􏰐 Providence􏰁 RI􏰃
## 37.12
􏰤􏰅􏰆􏰦 FRIEDMAN􏰁 J􏰃H􏰃 􏰜 SILVERMAN􏰁 B􏰃W􏰃 􏰞􏰅􏰋􏰢􏰋􏰟􏰃 Flexible Parsimonious Smo othing and Additive Mo deling􏰃 􏰞with discussion􏰟􏰃 Technometrics 􏰇􏰅􏰁 􏰇􏰩􏰇􏰋􏰃
## 37.13
􏰤􏰅􏰇􏰦 FRIEDMAN􏰁 J􏰃H􏰃 􏰞􏰅􏰋􏰋􏰅􏰟􏰃 Multiple Additive Regression Splines 􏰞with discussion􏰟􏰃 An􏰂 nals of Statistics􏰁 􏰅􏰋􏰁 􏰅􏰩􏰊􏰓􏰃
## 37.14
􏰤􏰅􏰈􏰦 GEORGE􏰁 E􏰃 I􏰃 􏰜 Foster􏰁 D􏰃 P􏰃􏰞􏰅􏰋􏰋􏰄􏰟􏰃 The risk in􏰖ation of variable selection in re􏰂 gression􏰃 Technical Rep ort􏰁 University of Chicago􏰃
## 37.15
􏰤􏰅􏰉􏰦 LEPSKII􏰁 O􏰃V􏰃 􏰞􏰅􏰋􏰋􏰄􏰟􏰃 On one problem of adaptive estimation on white Gaussian
noise􏰃 Teor􏰃 Veoryatnost􏰃 i Primenen􏰃 􏰇􏰉 􏰈􏰉􏰋􏰂􏰈􏰓􏰄 􏰞in Russian􏰟􏰃 Theory of Probability and Appl􏰃 􏰇􏰉􏰁 􏰈􏰉􏰈􏰂􏰈􏰊􏰊 􏰞in English􏰟􏰃
## 37.16
􏰤􏰅􏰊􏰦 MALGOUYRES􏰁 G􏰃 􏰞􏰅􏰋􏰋􏰅􏰟􏰃 Ondelettes sur l􏰝Intervalle􏰐 algorithmes rapides􏰃 Pr􏰗epublications Mathematiques Orsay􏰃
## 37.17
􏰤􏰅􏰓􏰦 MEYER􏰁 Y􏰃 􏰞􏰅􏰋􏰋􏰄􏰟􏰃 Ondelettes et Op􏰗erateurs􏰐 I􏰃 Ondelettes Hermann et Cie􏰁 Paris􏰃
## 37.18
􏰤􏰅􏰢􏰦 MEYER􏰁 Yves 􏰞􏰅􏰋􏰋􏰅􏰟􏰃 Ondelettes sur l􏰝intervalle􏰃 Revista Matem􏰗atica Ibero􏰂Americana
## 37.19􏰓 􏰞􏰆􏰅􏰅􏰉
􏰤􏰅MILLER􏰁 A􏰃J􏰃 􏰞􏰅􏰋􏰢􏰈􏰟􏰃 Selection of subsets of regression variables 􏰞with discussion􏰟􏰃 J􏰃 R􏰃 Statist􏰃 Soc􏰃 A􏰃􏰁 􏰅􏰈􏰓􏰁􏰇􏰢􏰋􏰩􏰈􏰆􏰉􏰃
## 37.20
􏰤􏰆􏰄􏰦 MILLER􏰁 A􏰃J􏰃 􏰞􏰅􏰋􏰋􏰄􏰟􏰃 Subset Selection in Regression􏰃 Chapman and Hall􏰃 London􏰁 New York􏰃
## 37.21
􏰤􏰆􏰅􏰦MU􏰬LLER􏰁Hans􏰂Georg􏰜STADTMULLER􏰁Ulrich􏰃􏰞􏰅􏰋􏰢􏰓􏰟􏰃Variablebandwidthkernel estimators of regression curves􏰃 Ann􏰃 Statist􏰃􏰁 􏰅􏰉􏰞􏰅􏰟􏰁 􏰅􏰢􏰆􏰩􏰆􏰄􏰅􏰃
## 37.22
􏰤􏰆􏰆􏰦 TERRELL􏰁 G􏰃R􏰃 􏰜 SCOTT􏰁 D􏰃W􏰃 􏰞􏰅􏰋􏰋􏰆􏰟􏰃 Variable kernel density estimation􏰃 Annals of Statistics􏰃􏰁 􏰆􏰄􏰁 􏰅􏰆􏰇􏰊 􏰩 􏰅􏰆􏰊􏰉􏰃



# 参考資料(References)
Data Scientist の基礎(2)
https://qiita.com/kaizen_nagoya/items/8b2f27353a9980bf445c

岩波数学辞典　二つの版がCDに入ってお得
https://qiita.com/kaizen_nagoya/items/1210940fe2121423d777

岩波数学辞典
https://qiita.com/kaizen_nagoya/items/b37bfd303658cb5ee11e

アンの部屋（人名から学ぶ数学：岩波数学辞典）英語(24)
https://qiita.com/kaizen_nagoya/items/e02cbe23b96d5fb96aa1

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