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Logistic regression is used thousands of times a day to fit data, predict future outcomes, and assess the statistical significance of explanatory variables.
Properties of sufficiency and statistical tests
Maurice S Bartlett · 1937
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The large-sample distribution of the likelihood ratio for testing composite hypotheses
Samuel S Wilks · 1938
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A general distribution theory for a class of likelihood criteria
George Box · 1949
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Michael R Sampford · 1953
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On the distribution of the likelihood ratio
Herman Chernoff · 1954
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A general method for approximating to the distribution of likelihood ratio criteria
DN Lawley · 1956
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David L. Hanson and Farroll T. Wright · 1971
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Peter J Huber · 1973
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Improved likelihood ratio statistics for generalized linear models
Gauss M Cordeiro · 1983
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Asymptotic behavior of M-estimators of p p regression parameters when p 2 / n p^{2}/n is large. i. consistency
Stephen Portnoy · 1984
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Asymptotic behavior of M-estimators of p p regression parameters when p 2 / n p^{2}/n is large; ii. normal approximation
Stephen Portnoy · 1985
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Asymptotic behavior of the empiric distribution of m-estimated residuals from a regression model with many parameters
Stephen Portnoy · 1986
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Asymptotic behavior of likelihood methods for exponential families when the number of parameters tends to infinity
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Updating the inverse of a matrix
William W Hager · 1989
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Asymptotics with increasing dimension for robust regression with applications to the bootstrap
Enno Mammen · 1989
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Peter McCullagh and James A Nelder · 1989
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A decomposition for the likelihood ratio statistic and the bartlett correction–a bayesian argument
Peter J Bickel and JK Ghosh · 1990
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Bartlett correction factors in logistic regression models
Lawrence H Moulton, Lisa A Weissfeld, and Roy T St Laurent · 1993
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Bartlett corrections for one-parameter exponential family models
Gauss M Cordeiro, Franciso Cribari-Neto, Elisete CQ Aubin, and Silvia LP Ferrari · 1995
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On bartlett and bartlett-type corrections francisco cribari-neto
Francisco Cribari-Neto and Gauss M Cordeiro · 1996
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On parameters of increasing dimensions
Xuming He and Qi-Man Shao · 2000
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Asymptotic statistics
A. W. Van der Vaart · 2000
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Generalized likelihood ratio statistics and Wilks phenomenon
Jianqing Fan, Chunming Zhang, and Jian Zhang · 2001
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Testing statistical hypotheses
Erich L Lehmann and Joseph P Romano · 2006
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Maximum likelihood estimation in logistic regression models with a diverging number of covariates
Hua Liang, Pang Du, et al · 2012
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Penalized maximum likelihood estimation and effective dimension
Vladimir Spokoiny · 2012
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Topics in random matrix theory
Terence Tao · 2012
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2012
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High-dimensional Wilks phenomena in some exponential random graph models
Ting Yan, Yuanzhuang Li, Jinfeng Xu, Yaning Yang, and Ji Zhu · 2012
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Nonparametric inference with generalized likelihood ratio tests
Jianqing Fan and Jiancheng Jiang · 2007
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The probabilistic method (3rd edition)
Noga Alon and Joel H Spencer · 2008
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Mills’ ratio: monotonicity patterns and functional inequalities
Árpád Baricz · 2008
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Cheng Yong Tang and Chenlei Leng · 2010
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Alan Agresti and Maria Kateri · 2011
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Noureddine El Karoui · 2013
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Living on the edge: Phase transitions in convex programs with random data
Dennis Amelunxen, Martin Lotz, Michael B McCoy, and Joel A Tropp · 2014
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An introduction to Bartlett correction and bias reduction
Gauss M Cordeiro and Francisco Cribari-Neto · 2014
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Proximal algorithms
Neal Parikh and Stephen Boyd · 2014
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Panning for gold: Model-free knockoffs for high-dimensional controlled variable selection
Emmanuel Candès, Yingying Fan, Lucas Janson, and Jinchi Lv · 2016
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On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators
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Nonuniformity of p-values can occur early in diverging dimensions
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Supplemental materials for “the likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled chi-square”
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