n times the empirical biases of the Shamos estimator under the standard normal distribution, where n is the sample size and n is from 1 to 100. Note that the Fisher-consistent Shamos estimator is used, but it is not unbiased with a finite sample. The empirical biases are obtained using the extensive Monte Carlo simulation with 1E07 replicates.
n.times.eBias.of.shamosn.times.eBias.of.shamos returns a vector of 100 values.
This data frame contains 100 values.
Park, C., H. Kim, and M. Wang (2019). Finite-sample properties of robust location and scale estimators. arXiv:1908.00462.
Shamos, M. I. (1976). Geometry and statistics: Problems at the interface. In Traub, J. F., editor, Algorithms and Complexity: New Directions and Recent Results, pages 251--280. Academic Press, New York.
L<U+00E8>vy-Leduc, C., Boistard, H., Moulines, E., Taqqu, M. S., and Reisen, V. A. (2011). Large sample behaviour of some well-known robust estimators under long-range dependence. Statistics, 45, 59--71.