Modified likelihood ratio test in finite mixture models with a structural parameter
By Jiahua Chen, John D. Kalbfleisch. 2005
53 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/67b11360f125c64d64f5f2143937cfd75134a243
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
- Pengfei Li
- Jiahua Chen
- Sandra Jeanne Vollmer
- Hajo Holzmann
- Runchu Zhang
- Xianming Tan
- Hiroyuki Kasahara
- Katsumi Shimotsu
In Statistics and Probability
- Pengfei Li
- Jiahua Chen
- Sandra Jeanne Vollmer
- Hajo Holzmann
- Runchu Zhang
- Xianming Tan
- Hiroyuki Kasahara
- Katsumi Shimotsu
Citations in the corpus
- Hypothesis test for normal mixture models: The EM approach (2009, background): “Among several existing results, the modified likelihood ratio test (MLRT) in Chen and Kalbfleisch (2005) is known to have an accurate asymptotic upper bound.”
- A likelihood ratio test for bimodality in two-component mixtures with application to regional income distribution in the EU (2008, methodology): “with a structural parameter as investigated in Chen and Kalbfleisch (2005) .”
- INFERENCE FOR NORMAL MIXTURES IN MEAN AND VARIANCE (2008, methodology): “In particular, the normal mixture in both mean and variance was first applied to crab data in Pearson (1894), and is the most popular model for analysis of quantitative trait loci, see Roeder (1994), Chen and Chen (2003), Chen and Kalbfleisch (2005), and Tadesse, Sha and Vannucci (2005).”