A nonparametric estimate of a multivariate density function

By D. Loftsgaarden, C. Quesenberry. 1965

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In Statistics and Probability

Citations in the corpus

Measuring Data (2022, background): “Relevant measures for density in datasets include: General Data Measures KNN Density (Loftsgaarden and Quesenberry, 1965; Zhu et al., 2008): Average similarity between examples, which can be used for determining whether an unlabeled example is an outlier compared to other examples and finding patter”
Nonparametric kernel estimators for image classification (2012, methodology): “[5, 2, 24]).”
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions (2011, background): “Loftsgaarden and Quesenberry (1965) deﬁne the k - nn based density estimators of p and q at x as follows.”
Computational Optimal Transport (2018, methodology): “These methods typically make use of the distance between nearest neighbors [Loftsgaarden and Quesenberry, 1965], which is similar to locally adapting the bandwidth σ to the local sampling density.”
Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning (2020, methodology): “The aim of density estimation is to estimate the probability density function underlying the data, which is assumed to be i.i.d. Existing density estimation algorithms can be divided into two categories: (a) parametric or semi-parametric approaches such as Gaussian Mixture models (McLachlan and Kris”
Kernel bandwidth optimization in spike rate estimation (2009, background): “The idea of optimizing bandwidth at every instant was proposed by Loftsgaarden and Quesenberry (1965).”
Conditional extremes from heavy-tailed distributions: an application to the estimation of extreme rainfall return levels (2010, methodology): “We refer to (Loftsgaarden and Quesenberry 1965) for the first asymptotic properties of the nearest neighbor density estimator and to (Stone 1977) for the regression case.”
Routing in Socially Selfish Delay Tolerant Networks (2010, methodology): “Based on previous work in this area [20], we set K = √ n, where n is the number of samples.”
Demystifying fixed k-nearest neighbor information estimators (2016, background): “[32] Don O Loftsgaarden, Charles P Quesenberry, et al.”
Outlier Detection with Kernel Density Functions (2007, methodology): “be achieved for h(xi) = hdk(xi), where dk(¢) denotes the distance to the kth nearest neighbor of point xi. The usage of the kth nearest neighbor in kernel density estimation was flrst proposed in [ 38 ] (see also [37]).”
Study on density peaks clustering based on k-nearest neighbors and principal component analysis (2016, methodology): “This approach has been shown to be a powerful technique for density estimation [22] , clustering [23–25] and other ﬁelds.”
A weighted k-nearest neighbor density estimate for geometric inference (2011, background): “A series of experiments were conducted in order to compare the performance of our weighted estimate with that of the standard k -nearest neighbor estimate of Fix and Hodges [23] and Loftsgaarden and Quesenberry [31].”
PROJECTION PURSUIT DENSITY ESTIMATION (1984, methodology): “Examples of successful methods are the related techniques of kernel estimates (Parzen, 1962; Rosenblatt, 1971), near neighbor estimates (Loftsgaarden and Quesenberry, 1965) and splines (Boneva, Kendall, and Stefanov, 1971).”
Bayesian selection of continuous-time Markov chain evolutionary models. (2001, methodology): “The ability to simultaneously consider each constraint marginally is an advantage of our framework over previous molecular-clock tests and allows for testing of a local molecular clock (Hillis, Mable, and Moritz 1996; Huelsenbeck, Larget, and Swofford 2000) within a subset of taxa.”