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The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, master's students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful.
This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurancedata analytics with applications to P&C, life and health insurance.
The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, master's students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful.
This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurancedata analytics with applications to P&C, life and health insurance.
Provides an exhaustive and self-contained presentation of tree-based methods applied to insurance
Gives a rigorous statistical analysis of tree-based methods
Fills a gap in the literature on artificial intelligence techniques applied to insurance
Written by actuaries for actuaries
Based on more than a decade of lectures and consulting projects on the topic, by the three authors
Offers several case studies in P&C insurance
Erscheinungsjahr: | 2020 |
---|---|
Fachbereich: | Allgemeines |
Genre: | Mathematik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Seiten: | 240 |
Reihe: | Springer Actuarial Lecture Notes |
Inhalt: |
x
228 S. 62 s/w Illustr. 6 farbige Illustr. 228 p. 68 illus. 6 illus. in color. |
ISBN-13: | 9783030575557 |
ISBN-10: | 3030575551 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: |
Denuit, Michel
Trufin, Julien Hainaut, Donatien |
Auflage: | 1st ed. 2020 |
Hersteller: |
Springer International Publishing
Springer International Publishing AG Springer Actuarial Lecture Notes |
Maße: | 235 x 155 x 14 mm |
Von/Mit: | Michel Denuit (u. a.) |
Erscheinungsdatum: | 17.11.2020 |
Gewicht: | 0,371 kg |
Provides an exhaustive and self-contained presentation of tree-based methods applied to insurance
Gives a rigorous statistical analysis of tree-based methods
Fills a gap in the literature on artificial intelligence techniques applied to insurance
Written by actuaries for actuaries
Based on more than a decade of lectures and consulting projects on the topic, by the three authors
Offers several case studies in P&C insurance
Erscheinungsjahr: | 2020 |
---|---|
Fachbereich: | Allgemeines |
Genre: | Mathematik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Seiten: | 240 |
Reihe: | Springer Actuarial Lecture Notes |
Inhalt: |
x
228 S. 62 s/w Illustr. 6 farbige Illustr. 228 p. 68 illus. 6 illus. in color. |
ISBN-13: | 9783030575557 |
ISBN-10: | 3030575551 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: |
Denuit, Michel
Trufin, Julien Hainaut, Donatien |
Auflage: | 1st ed. 2020 |
Hersteller: |
Springer International Publishing
Springer International Publishing AG Springer Actuarial Lecture Notes |
Maße: | 235 x 155 x 14 mm |
Von/Mit: | Michel Denuit (u. a.) |
Erscheinungsdatum: | 17.11.2020 |
Gewicht: | 0,371 kg |