Maximum likelihood estimation of mixture densities for binned and truncated multivariate data

Machine Learning

Volumn 47, Issue 1, 2002, Pages 7-34

  Cadez, Igor V ^a   Smyth, Padhraic ^a   McLachlan, Geoff J ^b   McLaren, Christine E ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF QUEENSLAND   (Australia)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Binned; EM; Histogram; Iron deficiency anemia; KL distance; Mixture model; Truncated

Indexed keywords

COMPUTATION THEORY; COMPUTER SIMULATION; DATA REDUCTION; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION;

MIXTURE DENSITIES;

LEARNING SYSTEMS;

EID: 0036532779     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1013679611503     Document Type: Article

References (22)

1. Iron deficiency anemia and the productivity of adult males in Indonesia
2. Hierarchical models for screening of iron deficiency anemia
3. Maximum likelihood from incomplete data via the EM algorithm
4. Iron-deficiency anaemia and its effect on worker productivity and activity patterns
5. Query by image and video content
6. Maximum likelihood estimation from grouped and truncated data with finite normal mixture models
7. Multi-dimensional selectivity estimation using compressed histogram information
8. Wavelet-based histograms for selectivity estimation
9. Fitting mixture models to grouped and truncated data via the EM algorithm
10. Mixture models in haematology: A series of case studies
11. Analysis of the volume of red blood cells: Application of the expectation-maximization algorithm to grouped data from the doubly-truncated lognormal distribution
12. Detection of two-component mixtures of lognormal distributions in grouped, doubly truncated data: Analysis of red blood cell volume distributions
13. Iron deficiency in infancy and childhood
14. Colour indexing