To this end, this study develops a nonparametric wind pressure interpolation method based on unbiased conditional kernel density estimation. Based on the sample data, this method is able to directly predict the Probabilistic Density Function (PDF) of the wind pressure coefficient features (e.g., ...
这里K 是kernel function,一般要能够在取值上反映出 x0 和Xi 距离上的关系。如果规定 m(x):=E[Kh(y−Y)|X=x] , 就可以得到相应的估计值。 2. NW estimator 如果只将 m(x) 展开到 m(x0) ,那么就可以得到相应的NW estimator。 Reference: Jan G. De Gooijer, Dawit Zerom (2003). On condit...
This paper compares the predictive performance of widely used parametric and semiparametric estimators with results obtained from nonparametric kernel conditional density estimation with likelihood cross-validated bandwidth selection and mixed data type. The results are striking. The predictive performance of ...
参与比较的SOTA有the nearest neighbor kernel conditional density estimation (NNKCDE, Dalmasso et al. (2020)), the conditional kernel density estimation (CKDE, implemented in the R package np, Hall et al. (2004)), and the basis expansion method FlexCode (Izbicki et al., 2017)). 而GCDS在Te...
Next we compare our approach with two different methodologies for the estimation of ρ(x|z∗) for a given a value z∗. The first methodology estimates ρ(x|z∗) through classical kernel density estimation on the set {xi,zi} of N closest points zi to z∗. We call this nearest ...
To further enhance performance, we provide efficient strategies to optimize the remaining kernel hyperparameters. In conditional density estimation tasks, our NN-CME hybrid achieves competitive performance and often surpasses existing deep learning-based methods. Lastly, we showcase its remarkable ...
We first consider the conditional density whose estimation is based on the choice of weights. Recall that, in the case of complete data, a well-known kernel estimator of the regression function is based on the Nadaraya–Watson weights1 ≕Win(x)=K(x−Xihn)∑j=1nK(x−Xjhn)≕1nh...
hdrcde: Highest Density Regions and Conditional Density EstimationThe R package hdrcde provides tools for computing highest density regions in one and two dimensions, kernel estimates of univariate density functions conditional on one covariate, and multimodal regression....
The consistency and asymptotic normality of such estimators is demonstrated for a broad class of models in which response and covariate vectors can take both discrete and continuous values and incorportates a wide set of choices for kernel-based conditional density estimation. It also establishes the...
This very general framework is core to modern kernel probabilistic methods, including kernel two-sample testing (Gretton et al. 2007), kernel Bayesian inference (Fukumizu et al. 2013), density estimation (Kanagawa and Fukumizu 2014; Song et al. 2008), component analysis (Muandet et al. ...