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Prediction revisited : the importance of observation /
"Prediction Revisited is a ground-breaking book for financial analysts and researchers--as well as data scientists in other disciplines--to reconsider classical statistics and approaches to forming predictions. Czasonis, Kritzman, and Turkington lay out the foundations of their cutting-edge app...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2022]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright
- Contents
- Timeline of Innovations
- Essential Concepts
- Preface
- 1 Introduction
- Relevance
- Informativeness
- Similarity
- Roadmap
- 2 Observing Information
- Observing Information Conceptually
- Central Tendency
- Spread
- Information Theory
- The Strong Pull of Normality
- A Constant of Convenience
- Key Takeaways
- Observing Information Mathematically
- Average
- Spread
- Information Distance
- Observing Information Applied
- Appendix 2.1: On the Inflection Point of the Normal Distribution
- References
- 3 Co-occurrence
- Co-occurrence Conceptually
- Correlation as an Information-Weighted Average of Co-occurrence
- Pairs of Pairs
- Across Many Attributes
- Key Takeaways
- Co-occurrence Mathematically
- The Covariance Matrix
- Co-occurrence Applied
- References
- 4 Relevance
- Relevance Conceptually
- Informativeness
- Similarity
- Relevance and Prediction
- How Much Have You Regressed?
- Partial Sample Regression
- Asymmetry
- Sensitivity
- Memory and Bias
- Key Takeaways
- Relevance Mathematically
- Prediction
- Equivalence to Linear Regression
- Partial Sample Regression
- Asymmetry
- Relevance Applied
- Appendix 4.1: Predicting Binary Outcomes
- Predicting Binary Outcomes Conceptually
- Predicting Binary Outcomes Mathematically
- References
- 5 Fit
- Fit Conceptually
- Failing Gracefully
- Why Fit Varies
- Avoiding Bias
- Precision
- Focus
- Key Takeaways
- Fit Mathematically
- Components of Fit
- Precision
- Fit Applied
- 6 Reliability
- Reliability Conceptually
- Key Takeaways
- Reliability Mathematically
- Reliability Applied
- References
- 7 Toward Complexity
- Toward Complexity Conceptually
- Learning by Example
- Expanding on Relevance
- Key Takeaways
- Toward Complexity Mathematically
- Complexity Applied
- References
- 8 Foundations of Relevance
- Observations and Relevance: A Brief Review of the Main Insights
- Spread
- Co-occurrence
- Relevance
- Asymmetry
- Fit and Reliability
- Partial Sample Regression and Machine Learning Algorithms
- Abraham de Moivre (1667-1754)
- Pierre-Simon Laplace (1749-1827)
- Carl Friedrich Gauss (1777-1853)
- Francis Galton (1822-1911)
- Karl Pearson (1857-1936)
- Ronald Fisher (1890-1962)
- Prasanta Chandra Mahalanobis (1893-1972)
- Claude Shannon (1916-2001)
- References
- Concluding Thoughts
- Perspective
- Insights
- Prescriptions
- Index
- EULA