62Gxx Nonparametric inference
This subtopic introduces the core ideas in nonparametric inference, including foundational concepts, standard methods, and the main questions used to organize the area. Typical uses include building mathematical background, framing related research problems, and supporting applications in neighboring fields where these concepts provide useful structure.
Specific topics
62G05 Nonparametric estimation
Overview
Nonparametric estimation. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks
62G07 Density estimation
Overview
Density estimation. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks
62G08 Nonparametric regression
Overview
Nonparametric regression. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks
62G09 Nonparametric resampling methods
Overview
Nonparametric resampling methods. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks
62G10 Nonparametric hypothesis testing
Overview
Nonparametric hypothesis testing. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks
62G15 Nonparametric tolerance and confidence regions
Overview
Nonparametric tolerance and confidence regions. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks
62G20 Asymptotic properties of nonparametric inference
Overview
Asymptotic properties of nonparametric inference. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks
62G30 Order statistics; empirical distribution functions
Overview
Order statistics; empirical distribution functions. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks
62G32 Statistics of extreme values; tail inference
Overview
Statistics of extreme values; tail inference. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks
62G35 Robustness, nonparametric
Overview
Robustness, nonparametric. This topic covers nonparametric inference, emphasizing methods that avoid strict finite-dimensional parametric assumptions.
Related Wikipedia Page
Wikipedia: Nonparametric statistics
Useful Links
Key Ideas
- distribution-free and robust procedures
- smoothing and rank-based techniques
- asymptotics in flexible model classes
Typical Uses
Used when model structure is uncertain or high flexibility is needed for inference.
Applications
- Robust data analysis
- Density and function estimation
- Machine learning and exploratory inference
References
Recommended Textbooks