Nonparametric Estimation and Symmetry Tests for Conditional Density Functions

By Rob J Hyndman, Qiwei Yao. 2002

166 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/683380918bef926f964235e0443fdcdec951c0c9

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Citations in the corpus

Modelling beyond regression functions: an application of multimodal regression to speed–flow data (2006, methodology): “Extensions to local linear estimators have been considered by Fan et al., (1996), Hyndman and Yao (2002) and Fan and Yim (2004).”
CONDITIONAL DENSITY ESTIMATION IN A REGRESSION SETTING (2007, background): “The interested reader can find a historical overview of this and related approaches in the books [5, 15, 34]; other relevant references are [2, 16, 17, 21, 22, 23, 26, 27, 28, 35].”
A Test for model specification of diffusion processes (2007, methodology): “density used in A¨ıt-Sahalia [1]. The local polynomial estimator introduced by Fan, Yao and Tong [19] can also be employed without altering the main results of this paper. It is known (Hydman and Yao [34]) that E{pˆ(y|x)−p(y|x)} = 1 2 σ2 kh 2 ∂2p(y|x) ∂x2 + ∂2p(y|x) ∂y2 +2 π′(x) π(x) ∂p(y|x) ∂x +o”
On Conditional Density Estimation (2003, methodology): “In this sense, our approach is computationally more feasible than that of HYNDMAN and YAO (2002).”
Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions (2013, background): “Nonparametric estimation of the latter has proven more formidable but has drawn the attention of a growing number of researchers (see, e.g., Bashtannyk and Hyndman 2001; Hyndman and Yao 2002; and others).”
Most Likely Transformations (2015, methodology): “This censoring was widely ignored in analyses of the Old Faithful data because most non-parametric kernel techniques cannot deal with censoring (see for example Hyndman and Yao 2002).”