On the performance of kernel estimators for high-dimensional, sparse binary data

DOI10.1006/jmva.1993.1019zbMath0766.62019OpenAlexW2069847260MaRDI QIDQ1209886

Birgit Grund

Publication date: 16 May 1993

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.1993.1019

zbMATH Keywords

mean squared errorKullback-Leibler lossfrequency estimatorkernel-type estimatorsquared errordata sparsenessdata dimensiondistribution smoothnesseffectiveness of cross-validationhigh-dimensional binary distributionssmoothing methods for sparse binary datause of dimensionality

Mathematics Subject Classification ID

Density estimation (62G07)

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Nonparametric estimation of regression functions with both categorical and continuous data ⋮ A nonparametric test for equality of distributions with mixed categorical and continuous data ⋮ Smoothing categorical data ⋮ Sparse consistency and smoothing for multinomial data ⋮ Nonparametric estimation of distributions with categorical and continuous data ⋮ Nonparametric estimation of the anisotropic probability density of mixed variables ⋮ Testing the missing at random assumption in generalized linear models in the presence of instrumental variables ⋮ A consistent model specification test with mixed discrete and continuous data ⋮ NONPARAMETRIC ESTIMATION OF REGRESSION FUNCTIONS WITH DISCRETE REGRESSORS ⋮ Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models

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