Contributions of empirical and quantile processes to the asymptotic theory of goodness-of-fit tests

By Eustasio del Barrio, Juan Antonio Cuesta-Albertos, Carlos Matrán, S. Csörgö, C. Cuadras, Tertius de Wet, Evarist Gin'e, Richard A. Lockhart, Axel Munk, Winfried Stute. 2000

79 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/42ccab499bf325a7662e2d62e6fe62aa13fa35fc

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In Economics and Econometrics

1. Bin Yu
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3. Hanzhong Liu
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In Finance

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In Statistics and Probability

1. Victor M. Panaretos
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8. Evarist Gin'e

Citations in the corpus

• Asymptotics for L2 functionals of the empirical quantile process, with applications to tests of fit based on weighted Wasserstein distances (2005, methodology): “…only for the normal distribution (de Wet and Venter 1972), whereas case 3 was known explicitly for the exponential, logistic and Gumbel distributions (McLaren and Lockhart 1987), and del Barrio et al. (2000) contains an example of case 4 (this example motivated us to consider this case).”
• Empirical optimal transport on countable metric spaces: Distributional limits and statistical applications (2017, background): “, Eberle (2014); Rudolf and Schweizer (2015)), for point process approximations (Barbour and Brown, 1992; Schuhmacher, 2009) or bootstrap convergence (Bickel and Freedman, 1981).”
• Statistical Aspects of Wasserstein Distances (2018, result): “In symbols: Similar results were obtained independently in del Barrio et al. (2000), where one can also find a nice survey of other goodness-of-fit tests.”
• Karhunen-Loève Expansions for Weighted Wiener Processes and Brownian Bridges via Bessel Functions (2003, unknown): “One of the motivations for the study of the above random variables comes from the theory of nonparametric tests of goodness of fit (Refer to del Barrio, Cuesta-Albertos and Matran ([5]), Durbin ([12]), and the references therein).”
• Asymptotic Properties of Lasso+mLS and Lasso+Ridge in Sparse High-dimensional Linear Regression (2013, methodology): “To control d2(F, Fn), we use the following result obtained by [16]: let →ω denote weak convergence, Lemma 9 (del Barrio et al. (2000)).”