Model-Based Clustering with Measurement or Estimation Errors

Clustering data with measurement errors

Traditional clustering methods assume that there is no measurement error, or uncertainty, associated with data. Often, however, real world applications require treatment of data that have such errors. In the presence of measurement errors, well-known clustering methods like k-means and hierarchical clustering may not produce satisfactory results. The fundamental question addressed in this paper...

Model-based Clustering with Noise: Bayesian Inference and Estimation

Bensmail, Celeux, Raftery and Robert (1997) introduced a new approach to cluster analysis based on geometric modeling based on the within-group covariance in a mixture of multivariate normal distributions using a fully Bayesian framework. This is a model-based methodology, where the covariance matrix structure is involved. Previously, similar structures were used (using a maximum likelihood app...

Corrected-loss estimation for quantile regression with covariate measurement errors.

We study estimation in quantile regression when covariates are measured with errors. Existing methods require stringent assumptions, such as spherically symmetric joint distribution of the regression and measurement error variables, or linearity of all quantile functions, which restrict model flexibility and complicate computation. In this paper, we develop a new estimation approach based on co...

Relative Potency Estimation in Direct Bioassay with Measurement Errors*

Consistent Estimation in Cox Proportional Hazards Model with Covariate Measurement Errors

The regular maximum partial likelihood estimator is biased when the covariates in the Cox proportional hazards model are measured with error, unless the measurement errors tend to zero. Although several alternative estimators have been proposed, theoretical justifications for them are lacking. We try to fill this gap by showing that the corrected maximum partial likelihood estimator proposed by...