Density Estimation

By Simon J. Sheather. 2004

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Citations in the corpus

Robust Estimators in High Dimensions without the Computational Intractability (2016, neutral): “Our results fit in the framework of density estimation, a classical problem in statistics with a rich history and extensive literature (see e.g., [27], [28], [29], [30], [31]).”
Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication (2018, background): “The problem has a rich, and vast literature (see e.g. [6, 39, 17, 18], and many others), with the primary goal of statistical eﬃciency, namely minimizing the sample complexity for estimation, which is the ﬁrst resource we consider.”
Measurement Error in the Bank of Italy's Survey of Household Income and Wealth (2008, unknown): “The number of responding households per interviewer is distributed as shown in Figure 2 (non-parametric density estimate performed with an Epanechnikov kernel and Silverman (1986) optimal bandwidth).”
Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models (2017, methodology): “We use Silverman’s (1986) rule-of-thumb bandwidth with c = 1.”
Bootstrapping Sequential Change-Point Tests (2008, methodology): “Plot of the estimated density of the run length For the density estimation we use the standard R procedure which uses a Gaussian kernel, where the bandwidth is chosen according to Silvermans rule of thumb ([11], p.”
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation (2016, background): “While k-NN distance based bandwidth are routine in practical usage [21], the main finding of this work is that they also turn out to be the “correct" mathematical choice for the purpose of asymptotically unbiased estimation of an integral functional such as the entropy: − ∫ f(x) log f(x); we briefly”
Spatial and temporal variability of wheat grain yield and quality in a Mediterranean environment: A multivariate geostatistical approach (2012, methodology): “…clasification techniques are reported in scientific literature, among hich there are those based on minimization of within-unit varince (Fraisse et al., 2001; Silverman, 1986; Stafford et al., 1998) and on-parametric techniques based on multivariate density estimaion (Guastaferro et al., 2010).”
Causality detection based on information-theoretic approaches in time series analysis (2007, methodology): “Kernel density estimator introduced by Silverman [215] in one-dimensional space is deﬁned where h is the kernel width parameter.”
Background and foreground modeling using nonparametric kernel density estimation for visual surveillance (2002, methodology): “Kernel density estimators asymptotically converge to any density function with sufficient samples [1], [2].”
Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures (2016, background): “, [67], [61]) that date back to Karl Pearson.”
Estimating Population Mean Power Under Conditions of Heterogeneity and Selection for Significance (2020, methodology): “Since the true density is unknown, we use a kernel density estimate (Silverman, 1986) as implemented in R’s density function, with the default settings.”
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2012, background): “For further reading on general density estimation techniques the reader is invited to explore the following material [5, 93].”
Option-Implied Risk-Neutral Distributions and Risk Aversion (2004, unknown): “©2004, The Research Foundation of AIMRTM 35 Söderlind (2000) started out with the fitted risk-neutral distribution, which is based on the mixtures of normal distributions.”
Defining probability density for a distribution of random functions (2010, methodology): “For an introduction to kernel density estimation [see, for example, Silverman (1986) and Wand and Jones (1995)].”