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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
We give both nonparametric estimates and the maximum likelihood estimates (MLEs) undernormality, and their covariances.
📌 3 Key Takeaways
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Objective & Hypothesis:Suppose that we wish to estimate the mean µ and the covariance Cof a random p-vector X with p > 2, but we can only sample from thevector X two of its p components at a time.
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Key Findings & Data:Suppose that we wish to estimate the mean µ and the covariance Cof a random p-vector X with p > 2, but we can only sample from thevector X two of its p components at a time.
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Conclusion & Impact:We give both nonparametric estimates and the maximum likelihood estimates (MLEs) undernormality, and their covariances.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Maximum likelihood estimation · Multivariate normal · Nonparametric estimation
Abstract
Suppose that we wish to estimate the mean µ and the covariance Cof a random p-vector X with p > 2, but we can only sample from thevector X two of its p components at a time. We give both nonparametric estimates and the maximum likelihood estimates (MLEs) undernormality, and their covariances.
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Cite This Article
WİTHERS, C. S., NADARAJAH, S. (2022). Multivariate Estimation from "Two Variables at a Time" Observations. Hacettepe Journal of Mathematics and Statistics, Cilt: 42 - Sayı: 2, 189–197. https://ajindex.com/articles/530418