Name: Advances in multivariate data analysis
ISBN: 9783540208891

Book information

Authors: Classification Group of SIS. Meeting verified identity
First published: 2004
Editions known: 2
Publishers: Springer
Languages: eng
ISBN-13: 9783540208891
Main topics: Mathematical Finance
Also covers: Financial Data Engineering, Time Series Analysis, Deep Learning for Finance
Related site topics: Machine Learning for Market Forecasting, Market Simulation & Agent-Based Models, NLP & LLMs in Finance, Stochastic Control & Optimal Stopping

Overview

Advances in multivariate data analysis is a book by Classification Group of SIS. Meeting, first published in 2004. In this catalogue it is classified mainly under Mathematical Finance, with secondary coverage of Financial Data Engineering, Time Series Analysis and Deep Learning for Finance.

Audience and depth. The rule-based model places its mathematical difficulty at advanced level (7/10); readers are typically expected to know calculus, linear algebra, machine learning, probability theory, statistics and stochastic processes.

Orientation. The theory-versus-practice balance could not be determined from metadata. The archive holds 6 papers whose topics and vocabulary overlap with this book; those links are inferred (see the research connections below).

How to read this page. Topics, difficulty, prerequisites and orientation are model-derived (rule-based, rule_based_v1) from bibliographic metadata such as title, subject headings and edition data. They were not produced by reading the book and are not a review. Relevance confidence is 83% and difficulty confidence is 95%; the catalogue lists no ratings or awards.

Research assessment

Rule-based and model-derived from metadata (title, subject headings, edition data). These are not reviews or ratings; see the evidence below.

Quant-finance relevance	62/100 confidence 83% Score 62.0/100. Strongest: quant_relevance 0.83, technical_depth 0.71; weakest: practical_applicability 0.12. Evidence: subjects:'mathematical finance'.
Mathematical difficulty	Advanced (7/10) confidence 95%
Prerequisites	calculus, linear algebra, machine learning, probability theory, statistics, stochastic processes
Metadata quality	95%

Difficulty evidence (8 signals)

advanced_statistics matched in subjects: “multivariate analysis”
econometrics matched in ol_description: “time series”
intro_statistics matched in subjects: “statistics for”
machine_learning matched in ol_description: “neural networks”
mathematical_finance_core matched in subjects: “mathematical finance”
probability_basics matched in ol_description: “distributions”
statistical_inference matched in ol_description: “regression”
statistical_inference matched in subjects: “statistics”

Research connections

Related papers on this site

Similarity of research area (shared vocabulary, topics and methods). Not citations: the paper does not necessarily cite this book.

Mathematical foundations of information economics Topic similarity inferred similarity 0.41
Preventing Household Bankruptcy: The One-Third Rule in Financial Planning with Mathematical Validation and Game-Theoretic Insights Topic similarity inferred similarity 0.40
Generative Neural Operators of Log-Complexity Can Simultaneously Solve Infinitely Many Convex Programs Related research area inferred similarity 0.40
Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms Methodological overlap inferred similarity 0.31
Deep Learning for Solving and Estimating Dynamic Macro-Finance Models Methodological overlap inferred similarity 0.31
Simultaneously Solving FBSDEs and their Associated Semilinear Elliptic PDEs with Small Neural Operators Topic similarity inferred similarity 0.30

External resources

Open Library
Search on Amazon (ISBN-13 9783540208891) (search link; may be an affiliate link)

Relationship graph

Solid lines are verified facts (authorship from persistent identifiers, editions); dashed lines are inferred by rules or similarity. Open in the full explorer.

Interactive relationship graph (requires JavaScript). The relationships are also listed as text below it once loaded.