Moment-Based Approximations of Distributions Using Mixtures: Theory and Applications
By Bruce G. Lindsay, Ramani S. Pilla, P. Basak. 2000
72 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/4e89f0a28f53daab1ffeefd488c9820df374dc42
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
- A. P. Dawid
- Shyam Visweswaran
- Monica Musio
- Kun Zhang
- Eric V. Strobl
- Laura Ventura
- Bernhard Schölkopf
- Jan Peters
In Statistics and Probability
- Laura Ventura
- Monica Musio
- Kun Zhang
- A. P. Dawid
- Shyam Visweswaran
- Eric V. Strobl
- Judea Pearl
- S. Geneletti
Citations in the corpus
- Hausdorff moment problem: Reconstruction of distributions (2008, unknown): “Lindsay et al. (2000) proposed to approximate the M-determinate cdf F by a finite mixture of gamma distributions.”
- Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery (2017, methodology): “The sequence is complicated and beyond the scope of this paper, but we refer the reader to (Lindsay et al., 2000) for details.”
- Minimum Scoring Rule Inference (2014, background): “Matching of moments up to higher order can also be considered, as in Satterthwaite (1946) and Wood (1989); see also Lindsay et al. (2000).”
- ADJUSTING COMPOSITE LIKELIHOOD RATIO STATISTICS (2009, unknown): “See also Lindsay, Pilla, and Basak (2000).”