ESTIMATING DENSITIES , QUANTILES , QUANTILE DENSITIES AND DENSITY QUANTILES

By C. M. 2004

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

1. R´emi Munos
2. Alexander Petersen
3. Linda Schulze Waltrup
4. H. Muller
5. Georg Ostrovski
6. Fabian Sobotka
7. Goeran Kauermann
8. Will Dabney

Citations in the corpus

• Autoregressive Quantile Networks for Generative Modeling (2018, background): “Although IQN does not directly model the log-likelihood of the data distribution, observe that we can still query the implied density at a point (Jones, 1992): .”
• Expectile and quantile regression—David and Goliath? (2015, methodology): “Jones (1994) shows that expectiles are in fact quantiles of a distribution function uniquely related to the distribution of y.”
• Functional data analysis for density functions by transformation to a Hilbert space (2016, methodology): “Occasionally of interest is the equivalent notion of the quantiledensity function q(t) = Q′(t) = d dt F−1(t) = [f (Q(t))]−1, from which we obtain f (x) = [q(F (x))]−1, where we use the notation of [30].”