From kernels to mixtures

Technometrics

Volumn 43, Issue 3, 2001, Pages 323-335

  Scott, D W ^a   Szewczyk, W F ^a  

^a Rice University   (United States)

Author keywords

BIC; Gaussian mixture models; Kernel density estimation; Model simplification

Indexed keywords

ALGORITHMS; MATHEMATICAL MODELS; VECTORS; VELOCITY;

GAUSSIAN MIXTURE MODELS; KERNEL DENSITY ESTIMATION; KERNELS; MODEL SIMPLIFICATION;

ESTIMATION;

EID: 0035419513     PISSN: 00401706     EISSN: None     Source Type: Trade Journal    
DOI: 10.1198/004017001316975916     Document Type: Article

References (24)

1. An alternative method of cross-validation for the smoothing of density estimates
2. On fitting mixture models
3. Density estimation and bump-hunting by the penalized likelihood method exemplified by the scattering and meteorite data
4. Bayes factors
5. Least-squares mixture decomposition
6. Exact mean integrated squared error
7. A tutorial on hidden Markov models and selected applications in speech recognition
8. Automatic speaker recognition using Gaussian mixture speaker models
9. Density estimation with confidence sets exemplified by superclusters and voids in the galaxy
10. Empirical choice of histograms and kernel density estimators
11. Cross-validation of multivariate densities
12. 2 error
13. Remarks on fitting and interpreting mixture models
14. The stochastic mode tree and clustering
15. Biased and unbiased cross-validation in density estimation
16. Bump hunting problems: A new test
17. Mixture and bumps: Errors in measurement