Help
Login
Register
Home
Search
The New England Journal of Statistics in Data Science
Submit your article
Information
Become a Peer-reviewer
Journal home
To appear
Current issue
All issues
More
Journal home
To appear
Current issue
All issues
Search
Detailed search
Keywords:
ATOMIC principles
 
Detailed search
Title
Author
Types
Biomedical Research
Cancer Research
Editorial
Engineering Science
Machine Learning And Data Mining
NextGen
Software
Spatial And Environmental Statistics
Statistical Methodology
Abstract
Keywords
p-value
α-investing
62P10
A/B testing
Adaptive design
Adding new arms
Adverse drug reaction
Alpha saving
Anomaly detection
Approximate Bayesian inference
AR(p) models
ATOMIC principles
Autoregressive models
Bayes factor
Bayesian
Bayesian D-optimal design
Bayesian Analysis
Bayesian envelope model
Bayesian hierarchical model
Bayesian hierarchical models
Bayesian hypothesis testing
Bayesian inference
Bayesian model averaging
Bayesian Modeling
Bayesian Quadrature
Behavioral Statistics
BERT
Beta-Binomial prior
Big data analysis
BiLSTM
Biomarker
Bounded Normal Mean
Calibrated Bayesian hierarchical model
Calibration validation
Case-control studies
Categorical data analysis
Censored data
Chemical mixture
Cluster analysis
Clustering
Coherence
Collapsed Gibbs
Comment
Complexity prior
Computational inference
Confidence distribution
conjugate prior
Contamination Class
Convenience sample
Convexity
Cournot’s principle
Covariance selection
Covariate adjustment
Covariates adjustment
Covid-19
Crossover
Cure model
CUSUM
Damped exponential correlation
Data Matrix
Data Science
Data visualization
Descriptive statistics
Design of experiments
Diagnostic imaging drug clinical trial
Dietary patterns
Dirichlet process
Discrete and constrained optimization
Distributional difference
Dose optimization
Dose response Models
Dropout
Drug safety
E-optimal design
E-value
E-values
E1684
Effect Sparsity
Efficacy
Efficiency
EM algorithm
Email campaign
English Premier League
Estimand
Ethics
Exact
Experimental Design
Experimentation
Factor analysis
Factorial design
Factorial designs
Fairness in machine learning
False-discovery rate
Family-wise error rate
FDR control
Feature selection
Functional Data Analysis
Fundamental principle of testing by betting
G-computation
Game-theoretic probability
Game-theoretic statistics
Gaussian process
Generalization error
generalized linear model
Goodness-of-fit
Goodness-of-fit measure
Graphical Gaussian Process
Graphical models
Health care network communities
Health inequities
Hierarchical Spatial Process Models
High dimensional
Highest posterior model
HIV-dynamic model
Hypothesis testing
Imaging genetics
Implicit function theorem
Imputation
Incomplete data
Incomplete reporting
Indeterminate mechanism
Interaction
Intercurrent event
Interference
Interim analysis
Interpretability
Interval design
Isotonic regression
Jensen’s Inequality
Joint species distribution models
K-12 Mathematical Education
Kalman filter
Kelly betting
Kriging model
Latent factor analysis
Latent variables
Least Favorable Prior
Likelihood
Likelihood principle
Linear mixed effects models
Linear mixed-effects model
Linear Model
Locally D-optimal design
Logic regression
Logistic regression
Low Signal-to-Noise Ratio
Lung Cancer
Machine learning
MAGEC
Margin justification
Marginalization
Master protocol
Maximin optimal design
Maximin tolerated dose
Maximum Mean Discrepancy
MCMC
Mental health
Meta-analysis
Metrics
Michaelis-Menten model
Missing completely at random
Missing data
Mixture of finite mixtures
MNIST dataset
Model comparison
Model selection
Model space prior
Model Validation
Model-based clustering
Modularity
Most effective dose
Multi-study factor analysis
Multiarm trial
Multiple auxiliary sets
Multiple comparisons
Multiple imputation
Multiple toxicity grades
Multiple treatments allocation
Multiple-objective optimal design
Multiplicity
Multivariate dependencies
Multivariate regression
multivariate responses
Multivariate testing
Natural language processing
Nature-inspired metaheuristic algorithm
NESS
Network effect modeling
Network flows
New Journal
Node size
Non-inferiority
noninformative prior
Nonparametric
Nonparametric hypothesis testing
Object Centering
Odds ratios
One-minimum power
Online testing
Optimal Design
Optimization on Stiefel manifold
Optional continuation
Optional stopping
Ordered multinomial
Outerval
Parametric bootstrap
Partial envelope
Particle interaction
Patient-sharing network
Penalized likelihood
Phase I
Pitman’s asymptotic relative efficiency
Platform trial
point-exchange
Poisson process
Poisson regression
Power
Precision agriculture
Prediction
Principled Corner Cutting (PC2)
Probability forecast
Probability forecasting
Probability of decision
Probit model
Proportional
Proportional hazards
Proposal
Quality-guaranteed Statistics
Quick-and-dirty Bayes Theorem
Radical
Randomness
Reduced rank regression
Reducing subspace
Regression random forest
Research Replicability and Reliability
Response adaptive randomizations
Richard von Mises
Robust profile clustering
Rule-based design
Scalable computation
Selfish Test
Semi-definite program
Sequential change detection
Sequential hypothesis testing
Set-valued prediction
Shape-restricted splines
Shared control
Shiryaev-Roberts
Shrinkage
Simulated annealing
Simultaneous decision error
Simultaneous envelope
Sliced designs
Small training sample
Social networks
Soft Elimination
Space-filling design
Spatial dependence
Spatial point patterns
Spatial process models
Spatiotemporal
Species richness
Spike and Slab Priors
sports statistics
Statistical independence
Statistical inference
Statistical-Mechanistic Modeling
Student’s t-distribution
Subsampling
Supersaturated Design
Supervised dimension reduction
Surrogate method
Surrogate residual
Survey
Survival model
SUTVA
Symmetry
Tail Probability
Taylor Series
The EM algorithm
Tissue image scoring
Toxicity
Toxicity burden
Trait Centering
Transfer learning
Tumor microenvironment
Tweedie Double Generalized Linear Models
Type I error
Unbalanced Design
Uncertainty quantification
Variable Selection
Ville’s inequality
Visualization
Wavelet Regression
Zellner’s g-prior
Γ-minimaxity
Published
Pages
Volumes
Issues
DOI
Affiliation
Search
Search results
1
Order by:
Title
First page
Publication date
Type
Select:
All
None
Download:
Citation
PDF
XML
Radical and Not-So-Radical Principles and Practices: Discussion of Meng
Ronald L. Wasserstein
Allen L. Schirm
Nicole A. Lazar
https://doi.org/10.51387/22-NEJSDS6A
Pub. online:
25 Oct 2022
Type:
Statistical Methodology
Open Access
Journal:
The New England Journal of Statistics in Data Science
Volume 1, Issue 1 (2023), pp. 35–38
Citation
PDF
XML
Abstract
We highlight points of agreement between Meng’s suggested principles and those proposed in our 2019 editorial in
The American Statistician
. We also discuss some questions that arise in the application of Meng’s principles in practice.
Items per page
10
25
50
Export citation
Copy and paste formatted citation
Formatted citation
Placeholder
Citation style
AMS -- Americal Mathematical Society
APA -- American Psychological Association 6th ed.
Chicago -- The Chicago Manual of Style 17th ed.
Download citation in file
Export format
BibTeX
RIS
Authors
Placeholder
Share
RSS
To top