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Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods

This book addresses contemporary statistical inference issues when no or minimal assumptions on the nature of studied phenomenon are imposed. Information theory methods play an important role in such scenarios. The approaches discussed include various high-dimensional regression problems, time serie...

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Bibliographic Details
Main Author: Mielniczuk, Jan
Format: eBook
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Consistent Selection
Information Theory
Consistency
Subgaussianity
Generalized Information Criterion
Tail Dependency
N/a
Privacy
Nonparametric Variable Selection Criteria
Maximum Likelihood Estimation
Minimum Distance Estimation
Mechanical Engineering And Materials / Bicssc
Model Misspecification
Multivariate Analysis
Cife
Time Series
History Of Engineering And Technology / Bicssc
Statistical Learning Theory
Prediction Methods
Gaussian Mixture
Cmi
Semiparametric Regression
Generative Tree Model
Influence Functions
Information Measures
Technology: General Issues / Bicssc
Penalized Estimation
Markov Blanket
Conditional Mutual Information
Jmi
Random Predictors
Network Estimation
Parameter Estimation
Adaptive Splines
Nonstationarity
Synthetic Data Transformation
Misclassification Risk
Variable Selection Consistency
Joint Mutual Information Criterion
B-splines
Loss Function
High-dimensional Time Series
Estimation
Entropy
Misspecification
Learning Systems
Robustness
High-dimensional Regression
Kernel Estimation
Right-censored Data
Archimedean Copula
Conditional Infomax Feature Extraction
Extreme-value Copula
Change Points
Supervised Classification
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:This book addresses contemporary statistical inference issues when no or minimal assumptions on the nature of studied phenomenon are imposed. Information theory methods play an important role in such scenarios. The approaches discussed include various high-dimensional regression problems, time series and dependence analyses.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (226 p.)
ISBN:books978-3-0365-4298-0
9783036542973
9783036542980

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