Estimation of Dependences Based on Empirical Data
E1153664
UNEXPLORED
Estimation of Dependences Based on Empirical Data is a foundational book in statistical learning theory that introduced key concepts underlying modern machine learning, including the principles that led to support vector machines.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Estimation of Dependences Based on Empirical Data canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T15361024 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Estimation of Dependences Based on Empirical Data Context triple: [Vladimir Vapnik, notableWork, Estimation of Dependences Based on Empirical Data]
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A.
On Estimation of a Probability Density Function and Mode
"On Estimation of a Probability Density Function and Mode" is a seminal statistical paper by Emanuel Parzen that develops kernel-based methods for nonparametric density and mode estimation.
-
B.
“Statistical Confluence Analysis by Means of Complete Regression Systems”
“Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
-
C.
Statistical Decision Functions
Statistical Decision Functions is a foundational work in decision theory and statistics that systematically develops the theory of optimal decision-making under uncertainty.
-
D.
Prediction and Regulation by Linear Least-Square Methods
"Prediction and Regulation by Linear Least-Square Methods" is a foundational monograph in stochastic control and time-series analysis that systematically develops linear least-squares techniques for prediction, filtering, and optimal regulation.
-
E.
Generalized method of moments
The generalized method of moments is an econometric estimation technique that uses sample moments to infer model parameters without requiring full specification of the underlying probability distribution.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Estimation of Dependences Based on Empirical Data Target entity description: Estimation of Dependences Based on Empirical Data is a foundational book in statistical learning theory that introduced key concepts underlying modern machine learning, including the principles that led to support vector machines.
-
A.
On Estimation of a Probability Density Function and Mode
"On Estimation of a Probability Density Function and Mode" is a seminal statistical paper by Emanuel Parzen that develops kernel-based methods for nonparametric density and mode estimation.
-
B.
“Statistical Confluence Analysis by Means of Complete Regression Systems”
“Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
-
C.
Statistical Decision Functions
Statistical Decision Functions is a foundational work in decision theory and statistics that systematically develops the theory of optimal decision-making under uncertainty.
-
D.
Prediction and Regulation by Linear Least-Square Methods
"Prediction and Regulation by Linear Least-Square Methods" is a foundational monograph in stochastic control and time-series analysis that systematically develops linear least-squares techniques for prediction, filtering, and optimal regulation.
-
E.
Generalized method of moments
The generalized method of moments is an econometric estimation technique that uses sample moments to infer model parameters without requiring full specification of the underlying probability distribution.
- F. None of above. chosen
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.