Bootstrap estimation and model selection for multivariate normal mixtures using parallel computing with graphics processing units
From MaRDI portal
Publication:5084919
DOI10.1080/03610918.2017.1311916OpenAlexW2603132148MaRDI QIDQ5084919
Takayuki Shiohama, Yoichi Miyata, Masanari Iida
Publication date: 29 June 2022
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2017.1311916
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bias and variance reduction techniques for bootstrap information criteria
- Editorial: Recent developments in mixture models (Hamburg, July 2001)
- Bayesian regularization for normal mixture estimation and model-based clustering
- Inference for multivariate normal mixtures
- An entropy criterion for assessing the number of clusters in a mixture model
- Consistent estimation of a mixing distribution
- Estimating the dimension of a model
- Bootstrapping log likelihood and EIC, an extension of AIC
- On the posterior distribution of the number of components in a finite mixture
- Information criteria and statistical modeling.
- Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
- <b>LIFESPAN DISTRIBUTION OF SIMD GROUPS ON A GPU ENGAGED IN A CLASS OF PROBABILISTIC COMPUTATION </b>
- The EM Algorithm and Extensions, 2E
- Model Selection and Model Averaging
- Strong consistency of the maximum likelihood estimator for finite mixtures of location-scale distributions when penalty is imposed on the ratios of the scale parameters
- How Biased is the Apparent Error Rate of a Prediction Rule?
- Generalised information criteria in model selection
- Practical Bayesian Density Estimation Using Mixtures of Normals
- A new look at the statistical model identification
This page was built for publication: Bootstrap estimation and model selection for multivariate normal mixtures using parallel computing with graphics processing units