Tag: estimator
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SCOPE: Spectral Concentration by Distributionally Robust Joint Covariance-Precision Estimation
SCOPE: Spectral Concentration by Distributionally Robust Joint Covariance-Precision Estimation arXiv:2511.14146v1 Announce Type: new Abstract: We propose a distributionally robust formulation for simultaneously estimating the covariance matrix and the precision matrix of a random vector.The proposed model minimizes the worst-case weighted sum of the Frobenius loss of the covariance estimator and Stein’s loss of the precision…
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A Honest Cross-Validation Estimator for Prediction Performance
A Honest Cross-Validation Estimator for Prediction Performance arXiv:2510.07649v1 Announce Type: new Abstract: Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the…
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CINDES: Classification induced neural density estimator and simulator
CINDES: Classification induced neural density estimator and simulator arXiv:2510.00367v1 Announce Type: new Abstract: Neural network-based methods for (un)conditional density estimation have recently gained substantial attention, as various neural density estimators have outperformed classical approaches in real-data experiments. Despite these empirical successes, implementation can be challenging due to the need to ensure non-negativity and unit-mass constraints,…
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Convex Regression with a Penalty
Convex Regression with a Penalty arXiv:2509.19788v1 Announce Type: new Abstract: A common way to estimate an unknown convex regression function $f_0: Omega subset mathbb{R}^d rightarrow mathbb{R}$ from a set of $n$ noisy observations is to fit a convex function that minimizes the sum of squared errors. However, this estimator is known for its tendency to…
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Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives arXiv:2506.20114v1 Announce Type: new Abstract: Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an…
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Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series arXiv:2506.05354v1 Announce Type: cross Abstract: Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving…
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Bayes and Biased Estimators Without Hyper-parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator
Bayes and Biased Estimators Without Hyper-parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator arXiv:2503.11854v1 Announce Type: new Abstract: Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often perform better than the maximum likelihood estimator in terms of minimizing mean squared…
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A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation
A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation arXiv:2503.08902v1 Announce Type: new Abstract: Mutual Information (MI) is a crucial measure for capturing dependencies between variables, but exact computation is challenging in high dimensions with intractable likelihoods, impacting accuracy and robustness. One idea is to use an auxiliary neural network to train an MI…