Developments and Challenges in Mixture Models, Bump Hunting and Measurement Error Models
نویسنده
چکیده
Bumps, components, clusters and atypical structures from real data often lead to scientific discoveries or reveal interesting phenomena of a population. They are important in astronomy, biology, data mining, bioinformatics and in applications to virtually all natural and social sciences. The wide interest in such structures has in the last decade led to significant developments in each of these areas: mixture models for component hunting; nonparametric methods for bump or mode hunting; methods for cluster and structure hunting; and Bayesian computational methods for model selection and latent variable mixture models. Additionally, data often come with measurement errors or incomplete information. These problems add additional challenges to component, bump and cluster hunting and lead to another area of active research. Image sharpening can be also considered as an inferential problem involving measurement error models.
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Developments and Challenges in Mixture Models, Bump Hunting and Measurement Error Models
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