Methods and tools for causal discovery and causal inference
Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
High-dimensional statistics with a view toward applications in biology
We review statistical methods for high-dimensional data analysis and pay particular
attention to recent developments for assessing uncertainties in terms of controlling false …
attention to recent developments for assessing uncertainties in terms of controlling false …
[图书][B] Statistical foundations of data science
Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …
statistical models, contemporary statistical machine learning techniques and algorithms …
Springer series in statistics
The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
High-dimensional additive modeling
We propose a new sparsity-smoothness penalty for high-dimensional generalized additive
models. The combination of sparsity and smoothness is crucial for mathematical theory as …
models. The combination of sparsity and smoothness is crucial for mathematical theory as …
Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
Page 1 The Annals of Statistics 2013, Vol. 41, No. 1, 342–369 DOI: 10.1214/13-AOS1087 © …
Page 1 The Annals of Statistics 2013, Vol. 41, No. 1, 342–369 DOI: 10.1214/13-AOS1087 © …
Statistical significance in high-dimensional linear models
P Bühlmann - 2013 - projecteuclid.org
We propose a method for constructing p-values for general hypotheses in a high-
dimensional linear model. The hypotheses can be local for testing a single regression …
dimensional linear model. The hypotheses can be local for testing a single regression …
A model-averaging approach for high-dimensional regression
T Ando, KC Li - Journal of the American Statistical Association, 2014 - Taylor & Francis
This article considers high-dimensional regression problems in which the number of
predictors p exceeds the sample size n. We develop a model-averaging procedure for high …
predictors p exceeds the sample size n. We develop a model-averaging procedure for high …
[HTML][HTML] Endogeneity in high dimensions
Most papers on high-dimensional statistics are based on the assumption that none of the
regressors are correlated with the regression error, namely, they are exogenous. Yet …
regressors are correlated with the regression error, namely, they are exogenous. Yet …
[HTML][HTML] Fifty years of semi-dwarf spring wheat breeding at CIMMYT: Grain yield progress in optimum, drought and heat stress environments
Grain yield progress over 50 years of spring wheat breeding at the International Maize and
Wheat Improvement Center (CIMMYT) was determined in field trials conducted during five …
Wheat Improvement Center (CIMMYT) was determined in field trials conducted during five …