Bing Li
Bing Li
Professor of Statistics, Pennsylvania State University
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Improving generalised estimating equations using quadratic inference functions
A Qu, BG Lindsay, B Li
Biometrika 87 (4), 823-836, 2000
On directional regression for dimension reduction
B Li, S Wang
Journal of the American Statistical Association 102 (479), 997-1008, 2007
Dimension reduction for conditional mean in regression
RD Cook, B Li
The Annals of Statistics 30 (2), 455-474, 2002
Contour regression: a general approach to dimension reduction
B Li, H Zha, F Chiaromonte
The Annals of Statistics 33 (4), 1580-1616, 2005
Successive direction extraction for estimating the central subspace in a multiple-index regression
X Yin, B Li, RD Cook
Journal of Multivariate Analysis 99 (8), 1733-1757, 2008
Sufficient dimension reduction in regressions with categorical predictors
F Chiaromonte, RD Cook, B Li
Annals of Statistics, 475-497, 2002
Envelope models for parsimonious and efficient multivariate linear regression
RD Cook, B Li, F Chiaromonte
Statistica Sinica, 927-960, 2010
Dimension reduction for nonelliptically distributed predictors
B Li, Y Dong
The Annals of Statistics 37 (3), 1272-1298, 2009
On dimension folding of matrix-or array-valued statistical objects
B Li, MK Kim, N Altman
The Annals of Statistics 38 (2), 1094-1121, 2010
Dimension reduction in regression without matrix inversion
RD Cook, B Li, F Chiaromonte
Biometrika 94 (3), 569-584, 2007
On a projective resampling method for dimension reduction with multivariate responses
B Li, S Wen, L Zhu
Journal of the American Statistical Association 103 (483), 1177-1186, 2008
Principal support vector machines for linear and nonlinear sufficient dimension reduction
B Li, A Artemiou, L Li
The Annals of Statistics 39 (6), 3182-3210, 2011
Sufficient dimension reduction: Methods and applications with R
B Li
CRC Press, 2018
Potential functions and conservative estimating functions
B Li, P McCullagh
The Annals of Statistics 22 (1), 340-356, 1994
Dimension reduction for non-elliptically distributed predictors: second-order methods
Y Dong, B Li
Biometrika 97 (2), 279-294, 2010
Statistical inference in massive data sets
R Li, DKJ Lin, B Li
Applied Stochastic Models in Business and Industry 29 (5), 399-409, 2013
Sufficient dimension reduction based on an ensemble of minimum average variance estimators
X Yin, B Li
The Annals of Statistics, 3392-3416, 2011
Dimension reduction for the conditional mean in regressions with categorical predictors
B Li, RD Cook, F Chiaromonte
The Annals of Statistics 31 (5), 1636-1668, 2003
A general theory for nonlinear sufficient dimension reduction: Formulation and estimation
KY Lee, B Li, F Chiaromonte
The Annals of Statistics 41 (1), 221-249, 2013
Combining eigenvalues and variation of eigenvectors for order determination
W Luo, B Li
Biometrika 103 (4), 875-887, 2016
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