• Developing a better theoretical understanding of the asymptotic theory of statistical experiments, in connection with statistical smoothing and curve estimation and nonparametric inference for stochastic processes
• Gaussian and Poisson approximation of nonparametric experiments in the Le Cam sense
• Constructive realization of equivalence
• Asymptotic risk bounds for density estimation and nonparametric regression
• Nonparametric models for point processes
• Diffusion processes and autoregression
• Functional limit theorems for empirical processes
• Statistical treatment of inverse and ill-posed problems

Asymptotic equivalence of density estimation and Gaussian white noise, Ann. Stat. 24 (1996), 2399–2430.

Asymptotic equivalence for nonparametric generalized linear models (with I. Grama), Probability Theory and Related Fields 111 (1998), 167–214.

Diffusion limits for nonparametric autoregression (with G. Milstein), Probability Theory and Related Fields 112 (1998), 167–214.

The asymptotic minimax constant for sup-norm loss in nonparametric density estimation (with A. Korostelev), Bernoulli 5 (6) (1999), 1099–1118.

Minimax risk: Pinsker bound; in Encyclopedia of Statistical Sciences, Vol. 3 (S.Kotz, ed.), John Wiley, New York, 1999, pp. 451–460.