Modal Regression
A statistical method whose regression target is the peak of the conditional probability density function, that is, the conditional mode. It is robust to skewed distributions, heavy tails and outliers and is typically implemented via kernel density estimation or empirical likelihood.
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Estimation in mode regression is usually implemented via kernel density estimation or empirical likelihood methods, and its statistical properties are more complex than those of mean regression, with the convergence rate depending on the curvature of the density near the peak.
The computational complexity of mode regression may be higher than that of mean regression, and its efficiency on large-scale tensors remains to be further verified.
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Estimation in mode regression is usually implemented via kernel density estimation or empirical likelihood methods, and its statistical properties are more complex than those of mean regression, with the convergence rate depending on the curvature of the density near the peak.
50%UnverifiedThe computational complexity of mode regression may be higher than that of mean regression, and its efficiency on large-scale tensors remains to be further verified.
50%