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Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods
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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.
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Keywords
- adaptive splines
- archimedean copula
- B-splines
- change points
- CIFE
- CMI
- conditional infomax feature extraction
- conditional mutual information
- consistency
- consistent selection
- Entropy
- estimation
- extreme-value copula
- gaussian mixture
- generalized information criterion
- generative tree model
- high-dimensional regression
- high-dimensional time series
- History of engineering & technology
- influence functions
- information measures
- Information theory
- JMI
- joint mutual information criterion
- kernel estimation
- learning systems
- loss function
- Markov blanket
- maximum likelihood estimation
- Mechanical engineering & materials
- minimum distance estimation
- misclassification risk
- misspecification
- model misspecification
- multivariate analysis
- n/a
- network estimation
- nonparametric variable selection criteria
- nonstationarity
- Parameter estimation
- penalized estimation
- prediction methods
- privacy
- random predictors
- right-censored data
- robustness
- semiparametric regression
- statistical learning theory
- subgaussianity
- supervised classification
- synthetic data transformation
- tail dependency
- Technology, engineering, agriculture
- Technology: general issues
- time series
- variable selection consistency
Links
DOI: 10.3390/books978-3-0365-4298-0Editions