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New estimators for Gaussian location models detailed in arXiv paper

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods for estimating log-density ratios in Gaussian location models, focusing on scenarios with a common covariance matrix. The study introduces both a regularized variational estimator and spectral estimators, deriving their high-dimensional asymptotic equivalents. These methods are compared through experiments, indicating that the variational estimator performs better with more observations, while the spectral estimator is favored when fewer observations are available due to its lower variance. AI

RANK_REASON The cluster contains a single academic paper detailing new statistical methods. [lever_c_demoted from research: ic=1 ai=0.7]

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New estimators for Gaussian location models detailed in arXiv paper

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Francis Bach (SIERRA) ·

    Regularized Variational and Spectral Log-Density-Ratio Estimation in the Gaussian Location Model

    arXiv:2607.01895v1 Announce Type: cross Abstract: We study ridge-regularized log-density-ratio estimation in the Gaussian location model with a common covariance matrix. By affine invariance, the model is written as q $\sim$ N(0, I), p $\sim$ N($\Delta$, I), with linear features,…

  2. arXiv stat.ML TIER_1 English(EN) · Francis Bach ·

    Regularized Variational and Spectral Log-Density-Ratio Estimation in the Gaussian Location Model

    We study ridge-regularized log-density-ratio estimation in the Gaussian location model with a common covariance matrix. By affine invariance, the model is written as q $\sim$ N(0, I), p $\sim$ N($Δ$, I), with linear features, where $Δ$ is a mean vector. The variational estimator …

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