Nonparametric estimation from correlated copies of a drifted process

Nicolas Marie

doi:10.1016/j.jmva.2026.105607

January 17, 2026Journal of Multivariate AnalysisOpen Access

Nonparametric estimation from correlated copies of a drifted process

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Key Points

Nonparametric estimation methods yield increased accuracy and reliability in analyzing drifted processes, which are subject to correlation.
The analysis demonstrates that incorporating correlated copies into empirical distributions significantly enhances estimation precision.
Observational analysis utilized stochastic processes to address correlation issues among random variables in drifted processes.
This study highlights the potential for improved statistical modeling techniques in dynamic systems where drift is a concern.

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Implication

Observational analysis reveals nonparametric estimation methods improve accuracy in correlated drifted processes, suggesting new advancements in statistical modeling.

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Cite This Study

Nicolas Marie (2026) studied this question.

synapsesocial.com/papers/69a75a2dc6e9836116a1fbef https://doi.org/10.1016/j.jmva.2026.105607

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Nonparametric estimation from correlated copies of a drifted process

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Nonparametric estimation from correlated copies of a drifted process

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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