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P.J. Bickel 69ReferencesBean, D., Bickel, P.J., El Karoui, N., and Yu, B. (2013). Optimal M-estimation in high-dimensional regression. Proceedings of the NationalAcademy of Sciences, 110:14563–14568.Bickel, P.J. (1974). Edgeworth expansions in non parametric statistics. TheAnnals of Statistics, 2:1–20.Bickel, P.J. (1981). Minimax estimation of the mean of a normal distributionwhen the parameter space is restricted. The Annals of Statistics, 9:1301–1309.Bickel, P.J., Boley, N., Brown, J.B., Huang, H., and Hang, N.R. (2010). Subsamplingmethods for genomic inference. The Annals of Applied Statistics,4:1660–1697.Bickel, P.J. and Chen, A. (2009). A nonparametric view of network modelsand Newman–Girvan and other modularities. Proceedings of the NationalAcademy of Sciences, 106:21068–21073.Bickel, P.J., Chen, C., Kwon, J., Rice, J., van Zwet, E., and Varaiya, P.(2007). Measuring traffic. Statistical Science, 22:581–597.Bickel, P.J. and Ghosh, J.K. (1990). A decomposition for the likelihood ratiostatistic and the Bartlett correction — a Bayesian argument. The Annalsof Statistics, 18:1070–1090.Bickel, P.J., Götze, F., and van Zwet, W.R. (1997). Resampling fewer thann observations: Gains, losses, and remedies for losses. Statistica Sinica,7:1–31.Bickel, P.J., Hammel, E.A., O’Connell, J.W. (1975). Sex bias in graduateadmissions: Data from Berkeley. Science, 187:398–404.Bickel, P.J. and Levina, E. (2004). Some theory of Fisher’s linear discriminantfunction, ‘naive Bayes’, and some alternatives when there are many morevariables than observations. Bernoulli, 10:989–1010.Bickel, P.J. and Levina, E. (2008). Regularized estimation of large covariancematrices. The Annals of Statistics, 36:199–227.Bickel, P.J. and Li, B. (2007). Local polynomial regression on unknown manifolds.In Complex Datasets and Inverse Problems. IMS Lecture Notes vol.54, Institute of Mathematical Statistics, Beachwood, OH, pp. 177–186.Bickel, P.J., Ritov, Y., and Rydén, T. (1998). Asymptotic normality of themaximum-likelihood estimator for general hidden Markov models. TheAnnals of Statistics, 26:1614–1635.