Luca Scrucca's Books

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

By: Luca Scrucca,Chris Fraley,T. Brendan Murphy,Raftery Adrian E.

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Model-Based Clustering, Classification, and Density Estimation Using mclust in R

By: Luca Scrucca,Chris Fraley,T. Brendan Murphy,Raftery Adrian E.

Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models. Key features of the book: An introduction to the model-based approach and the mclust R package A detailed description of mclust and the underlying modeling strategies An extensive set of examples, color plots, and figures along with the R code for reproducing them Supported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material Model-Based Clustering, Classification, and Density Estimation Using mclust in R is accessible to quantitatively trained students and researchers with a basic understanding of statistical methods, including inference and computing. In addition to serving as a reference manual for mclust, the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics, data science, clinical research, social science, and many other disciplines.

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269

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CRC Press

Published Date

ISBN 10

1000868346

ISBN 13

9781000868340

Statistical Analysis of Operational Risk Data

By: Giovanni De Luca,Danilo Carità,Francesco Martinelli

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Statistical Analysis of Operational Risk Data

By: Giovanni De Luca,Danilo Carità,Francesco Martinelli

This concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. The authors identify the risk classes by applying a pooling rule based on statistical tests of goodness-of-fit, use the theory of the mixture of distributions to analyze the loss severities, and apply copula functions for risk class aggregation. Lastly, they assess operational risk data in order to estimate the so-called capital-at-risk that represents the minimum capital requirement that a bank has to hold. The book is primarily intended for quantitative analysts and risk managers, but also appeals to graduate students and researchers interested in bank risks.

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Springer Nature

Published Date

ISBN 10

3030425800

ISBN 13

9783030425807

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

By: Luca Scrucca,Chris Fraley,T. Brendan Murphy,Raftery Adrian E.

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Model-Based Clustering, Classification, and Density Estimation Using mclust in R

By: Luca Scrucca,Chris Fraley,T. Brendan Murphy,Raftery Adrian E.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

269

Publisher

CRC Press

Published Date

ISBN 10

1000868346

ISBN 13

9781000868340

Book's by Luca Scrucca

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

Statistical Analysis of Operational Risk Data

Statistical Analysis of Operational Risk Data

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

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