Detecting the Presence of Mixing with Multiscale Maximum Likelihood

By Guenther Walther. 2002

83 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b2459988884f5eef092285d8e4137ec49fdbec4a

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In Statistics and Probability

Citations in the corpus

Inference for mixtures of symmetric distributions (2007, methodology): “eing symmetric. Ellis [5] considers the problem of deconvolving F =G⋆Q into a symmetric part G and a nonsymmetric part Q, but without the assumption that Q is a k-point distribution. Finally, Walther [14, 15] considers the problem not of estimation, but rather detection of the presence of mixing for”
Theoretical properties of the log-concave maximum likelihood estimator of a multidimensional density (2009, background): “Log-concavity has become an intensively-studied shape constraint for densities recently – see, for example, Walther (2002), Dümbgen, Hüsler and Rufibach (2007), Pal, Woodroofe and Meyer (2007), Dümbgen and Rufibach (2009), Balabdaoui, Rufibach and Wellner (2009).”
Estimation of a two‐component mixture model with applications to multiple testing (2012, background): “Walther (2001, 2002) discussed the homogeneity problem under the assumption that the densities are log-concave.”
Variable selection with error control: another look at stability selection (2011, background): “Log-concavity is a shape constraint that has received a large amount of attention recently (e.g. Walther (2002), Dümbgen and Rufibach (2009) and Cule et al. (2010)), and at first sight it seems reasonable in our context, because, if the summands in expression (4) were independent, then we would…”
Log-Concavity and Strong Log-Concavity: a review (2014, methodology): “Nonparametric estimation of log-concave densities was initiated by Walther (2002) in the context of testing for unimodality. For log-concave densities on R it has been explored in more detail by Dümbgen and Rufibach (2009), Bal-”
Smoothed log-concave maximum likelihood estimation with applications (2011, methodology): “isspeciﬁcation is not too large relative to the sampling variability of the estimator. 3 A new test of log-concavity Several tests of log-concavity have been proposed in the literature. An (1995) and Walther (2002) discuss various tests for univariate data, while Cule, Samworth and Stewart (2010) pr”
QUASI-CONCAVE DENSITY ESTIMATION (2010, background): “Maximum likelihood estimation of densities constrained to be log-concave has recently enjoyed a considerable vogue with important contributions of Walther (2001, 2002, 2008) Pal, Woodroofe and Meyer (2007), Rubach (2007), D umbgen and Rubach (2009), Balabdaoui, Rubach and Wellner (2009), Chang an”
Inference and Modeling with Log-concave Distributions (2009, methodology): “Moreover, it was shown in Walther (2002) that the nonparametric MLE of a univariate log-concave density exists and can be computed with readily available algorithms.”
Combining Mixture Components for Clustering (2010, unknown): “Our method seems to provide a simple alternative approach to the problem addressed by Walther (2002), in arbitrary dimensions.”
Recent Progress in Log-Concave Density Estimation (2017, methodology): “Indeed, the theory of log-concave density estimation has led to applications to a wide variety of problems, including the detection of the presence of mixing (Walther, 2002), filtering (Henningsson and Åström, 2006), tail index estimation”
Estimating a Polya frequency function$_2$ (2007, result): “Rufibach and Duembgen [11] and Walther [14] adopt a similar approach but obtain different results by different methods.”