Density estimation using inverse and reciprocal inverse Gaussian kernels

By Olivier Scaillet. 2004

193 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ff3debaba16044f01886c013f191d70ce1e66782

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Taoufik Bouezmarni
2. Janusz Kawczak
3. Xiaodong Jin
4. Tristan Senga Kiessé
5. Célestin C. Kokonendji
6. Carolina Marchant
7. Flávio Augusto Ziegelmann
8. Helton Saulo

In Statistics and Probability

1. Tristan Senga Kiessé
2. Célestin C. Kokonendji
3. Carolina Marchant
4. Taoufik Bouezmarni
5. Olivier Scaillet
6. Janusz Kawczak
7. Xiaodong Jin
8. Helton Saulo

Citations in the corpus

• Kernel density estimation for heavy-tailed distributions using the champernowne transformation (2005, background): “Scaillet [9] has recently studied non-parametric estimators for probability density functions which have support on the non-negative real line using alternative kernels.”
• A nonparametric method for estimating asymmetric densities based on skewed Birnbaum–Saunders distributions applied to environmental data (2013, methodology): “Following the same line, Scaillet (2004) introduced kernel estimators based on inverse Gaussian distributions.”
• Discrete associated kernels method and extensions (2011, methodology): “The second expression (2), that we will use in this paper, is from Chen [5,6] for adapting a “type of continuous kernel” generally asymmetric (such beta and gamma) to the support of f ; see also Scaillet [24] for inverse Gaussian and reciprocal inverse Gaussian kernels.”
• Miscellanea kernel-type density estimation on the unit interval (2007, background): “(Scaillet, 2004, proposes alternative inverse Gaussian and reciprocal inverse Gaussian kernels in the same framework.)”
• Birnbaum-Saunders and Lognormal Kernel Estimators for Modelling Durations in High Frequency Financial Data (2003, methodology): “The most popular choices include the beta kernel estimators in Brown (1999), the Gamma kernel estimators GAM1 & GAM2 in Chen (2000) and the inverse and the reciprocal inverse gaussian kernel estimators IG & RIG in Scaillet (2001). They are free of boundary bias and achieve the optimal rate of conver”
• CONSISTENCY OF ASYMMETRIC KERNEL DENSITY ESTIMATORS AND SMOOTHED HISTOGRAMS WITH APPLICATION TO INCOME DATA (2005, methodology): “Recently, Chen (2000) has proposed a gamma kernel estimator while Scaillet (2004) has introduced inverse Gaussian (IG) and reciprocal inverse Gaussian (RIG) estimators for densities dened on [0; +1).”