Education:

B.Sc. Cornell University, 1979
M.A., M.Phil, Ph.D., Yale University, 1984
  
Professional Background:

UNC-Chapel Hill (1984- )

Awards:

1997 Recipient of the Tanner Award for Excellence in Undergraduate Teaching
  
Selected Publication:

Boundary estimation (with C. Krishnamoorthy), Journal of the American Statistical Association, 87 (1992), 430-438.

Nonparametric estimation of the moments of a general statistic computed from spatial data (with M. Sherman), Journal of the American Statistical Association, 89 (1994), 496-500.

Nonparametric change-point estimation for data from an ergodic sequence (with S. Lele), Theory of Probability and its Applications, 38 (1994), 726-733.

Replicate histograms (with M. Sherman), Journal of the American Statistical Association, 91 (1996), 566-576.

Matched-block bootstrap for dependent data (with K. Do, P. Hall, T. Hesterberg, H. Kunsch), Bernoulli (1997), (to appear).

Research Interests:

Carlstein's main research interests are in methods of nonparametric statistical inference, that is, methods which do not require the user to know what particular distribution or model produced the data at hand. Such methods are needed when the statistician lacks prior knowledge of the underlying data-generating process, or when the statistician wants a robust corroborator for results from a parametric analysis of the data. He is especially interested in nonparametric estimation of change-points and boundaries, and of sampling distributions (via resampling). A change-point is the time at which observations in a sequence cease to arise from the "old" distribution and begin to arise from a "new" distribution; nonparametric estimation of change-points is important in quality control and in epidemiology. When observations are on a grid, as in image-analysis or geological data, a boundary may partition the observations into homogeneous groups; this boundary can be estimated nonparametrically using methods analogous to the change-point estimators.In order to make statistical inferences, one needs information about the sampling distribution of the statistic at hand. Although in many situations the sampling distribution is known to be approximately normal, there are many other cases where the sampling distribution cannot be derived theoretically, and may be quite non-normal, for example if the statistic is extremely complicated or if the observations are not independent. Resampling methods, such as the jackknife and the bootstrap, allow the statistician to nonparametrically estimate sampling distributions in these difficult situations, essentially by re-using the observed data.