Visualization of Anomalies Using Mixture Models

Visualization of Anomalies Using Mixture Models

Anomaly detection is important to learn from major past events and to prepare for future crises. We propose a new anomaly detection method that visualizes multivariate data in a 2or 3-dimensional space based on the probability of belonging to a mixture component and the probability of not belonging to any components. It helps to visually understand not only the magnitude of anomalies but also t...

Model Selection for Mixture Models Using Perfect Sample

We have considered a perfect sample method for model selection of finite mixture models with either known (fixed) or unknown number of components which can be applied in the most general setting with assumptions on the relation between the rival models and the true distribution. It is, both, one or neither to be well-specified or mis-specified, they may be nested or non-nested. We consider mixt...

Cure Fraction Models Using Mixture and Non-mixture Models

We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Twomodels are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a lifetime data set.

Detection, Classification and Visualization of Anomalies using Generalized Entropy Metrics

Default Analysis of Mixture Models using Expected

Consider observations Y , distributed according to a mixture of densities Y P k j=1 w j f(j j); where 0 w j 1, P w j = 1, k and j correspond to unknown parameters of the mixture. In the a Bayesian framework, it is not possible to perform a default statistical analysis of the mixture using non-proper priors, N , for the component parameters, since the posterior distribution of these do not exist...