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Nonparametric tests for multi-parameter M-estimators
John E. Kolassa
, John Robinson
School of Arts and Sciences, Statistics
Institute for Quantitative Biomedicine (IQB)
Research output
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Article
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peer-review
1
Scopus citations
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Dive into the research topics of 'Nonparametric tests for multi-parameter M-estimators'. Together they form a unique fingerprint.
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Business & Economics
Nonparametric Test
100%
M-estimator
85%
Resampling
65%
Test Statistic
59%
Tail Probability
56%
Bootstrap
53%
Approximation
30%
Likelihood Ratio
29%
Exponential Family
29%
Saddlepoint
25%
Nonlinear Regression
25%
Generalized Linear Model
24%
Bootstrap Method
23%
Parametric Model
22%
Bootstrapping
21%
Linear Regression
19%
Justification
14%
Mathematics
M-estimator
66%
Non-parametric test
63%
Test Statistic
48%
Tail Probability
44%
Resampling
41%
Bootstrap
34%
Saddlepoint Approximation
24%
Bootstrapping
21%
Nonlinear Regression
21%
Bootstrap Method
20%
Generalise
20%
Likelihood Ratio
20%
Exponential Family
20%
Generalized Linear Model
19%
Parametric Model
19%
Justification
18%
Linear regression
18%
Identically distributed
17%
Observation
12%
Standards
10%
Approximation
9%
Theorem
7%
Model
6%