All of Nonparametric Statistics¶
Wasserman, L. H. (2006). All of Nonparametric Statistics. Springer.
Cited by¶
2 citations across 2 artifacts.
Primes¶
- Distributional Assumption
- Nonparametric inference methods (e.g., permutation tests, bootstrap, kernel methods) perform inference with minimal or no explicit distributional assumption, though they make other structural assumptions (e.g., exchangeability, smoothness), as Wasserman (2006) catalogs in his treatment of nonparametric statistics.
Supported in partVerified against the publisher's abstract
The publisher's abstract confirms Wasserman's book surveys modern nonparametric topics, but it does not state that these methods minimise distributional assumptions or rest on exchangeability and smoothness.
“There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods.”
- Nonparametric inference methods (e.g., permutation tests, bootstrap, kernel methods) perform inference with minimal or no explicit distributional assumption, though they make other structural assumptions (e.g., exchangeability, smoothness), as Wasserman (2006) catalogs in his treatment of nonparametric statistics.
- Nonparametric Methods
- The assumption-relaxation in inferential validity
Supported in partVerified against the publisher's abstract
The book's own abstract names density estimation, nonparametric regression, bootstrapping and wavelets as its topics, backing the flexible-estimator and resampling half of the claim only.
“There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods.”
- The assumption-relaxation in inferential validity
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Registry ID ref:02f0cdd139a3 · see in the full table