n.times.eBias: Empirical biases (times n)
In rQCC: Robust Quality Control Chart
n.times.eBias R Documentation
Empirical biases (times n)
Description
n times the empirical biases of the median absolute deviation (MAD)
and Shamos estimators.
Usage
n.times.eBias.of.mad
n.times.eBias.of.shamos
Details
n times the empirical biases of the median absolute deviation (MAD)
and Shamos estimators
under the standard normal distribution, where n is the sample size
and n is from 1 to 100.
For the MAD estimator, mad{stats} is used.
For the Shamos estimator, the Fisher-consistent Shamos estimator, shamos{rQCC}, is used.
These estimators are not unbiased with a finite sample.
The empirical biases are obtained using the extensive Monte Carlo simulation
with 1E07 replicates.
Value
They return a vector of 100 values.
Author(s)
Chanseok Park and Min Wang
References
Park, C., H. Kim, and M. Wang (2022).
Investigation of finite-sample properties of robust location and scale estimators.
Communications in Statistics - Simulation and Computation,
51, 2619-2645.
doi: 10.1080/03610918.2019.1699114
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.
Lèvy-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.
rQCC documentation built on Dec. 28, 2022, 1:49 a.m.
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| n.times.eBias | R Documentation |
Empirical biases (times n)
Description
n times the empirical biases of the median absolute deviation (MAD) and Shamos estimators.
Usage
n.times.eBias.of.mad n.times.eBias.of.shamos
Details
n times the empirical biases of the median absolute deviation (MAD)
and Shamos estimators
under the standard normal distribution, where n is the sample size
and n is from 1 to 100.
For the MAD estimator, mad{stats} is used.
For the Shamos estimator, the Fisher-consistent Shamos estimator, shamos{rQCC}, is used.
These estimators are not unbiased with a finite sample. The empirical biases are obtained using the extensive Monte Carlo simulation with 1E07 replicates.
Value
They return a vector of 100 values.
Author(s)
Chanseok Park and Min Wang
References
Park, C., H. Kim, and M. Wang (2022).
Investigation of finite-sample properties of robust location and scale estimators.
Communications in Statistics - Simulation and Computation,
51, 2619-2645.
doi: 10.1080/03610918.2019.1699114
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.
Lèvy-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.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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