severini@northwestern.edu
Professor of Statistics
Ph.D., 1987, University of Chicago
My research focuses
on two areas of statistical theory and methodology: likelihood-based
statistical methods and inference in models with infinite-dimensional
parameters. Likelihood-based statistical methods include maximum
likelihood estimation and tests and confidence regions based
on the likelihood ratio statistic. My work in this area is
concerned with higher-order asymptotic approximations to the
distributions of likelihood-based statistics and with the
construction and properties of marginal and conditional likelihood
functions. Models with infinite-dimensional parameters include
models containing an unknown regression function and models
containing an unknown distribution or density function. My
recent work in this area involves inference in nonparametric
linear models with endogenous regressors and the relationship
between models with an unknown regression function as a parameter
and random effects models.
Some recent publications:
- Modified Estimating
Equations, Biometrika, 89 (2002), 333-343.
- A Simplified Approach
to Computing Efficiency Bounds in Semiparametric Models (with
G. Tripathi) Journal of Econometrics 102 (2001), 23-66.
- Likelihood Methods
in Statistics, published by Oxford University Press (2000).
- The Likelihood Ratio Approximation to the Conditional Distribution of the maximum Likelihood Estimator in the Discrete Case Biometrika 87 (2000), 939-945.
