arXiv research

New estimator accurately estimates mean of real-valued distributions without variance knowledge.

This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumpt…

Algorithm estimates common mean from Gaussian variables with unknown variances.

Improved heteroscedastic regression using neural networks with provably accurate mean estimates and calibrated variance.

A novel k-NN method estimates conditional mean and variance efficiently.

Paper optimizes MVE network convergence and regularization.

Optimizes survey design for private mean estimation with reduced variance.

Unified framework combines views and optimization for better portfolio management.

The paper identifies the minimum mean-variance spanning set and its importance in asset evaluation.

Faster convergence of kernel mean embeddings using variance information.

The paper provides concentration inequalities for Markov chain variance estimators.

Paper improves confidence intervals and variance estimation for deep learning models.

Paper improves Gumbel-Softmax estimator variance reduction.

Stein shrinkage improves BN robustness against adversarial attacks.

Gradient Boosted Mixed Models estimate mean and variance components for clustered data.

Improves Monte-Carlo simulations for consistent mean and variance.

Proposes a new framework to optimize portfolios with reduced estimation errors.

The paper develops estimators for variance in graph structures using fused lasso.

GBMixed boosts mixed models for clustered data, estimating mean and variance flexibly.

Sharp inequalities for matrix means with unknown variance.

Machine learning reduces variance in online experiment results.

Unified method for MMD variance estimation improves accuracy and computational efficiency.

Estimates Markov chain variance efficiently without storing samples.

Paper proposes a 1-bit mean estimation method with near-optimal sample complexity.

This study evaluates shrinkage estimators for improving mean and covariance in portfolio optimization.

The lasso has been studied extensively as a tool for estimating the coefficient vector in the high-dimensional linear model; however, considerably less is known about estimating the error variance in this context. In this paper, we propose the natural lasso estimator for the error variance, which maximizes a penalized …

In the paper, we consider three quadratic optimization problems which are frequently applied in portfolio theory, i.e, the Markowitz mean-variance problem as well as the problems based on the mean-variance utility function and the quadratic utility.Conditions are derived under which the solutions of these three optimiz…

Estimates mean and covariance for large, unbalanced stock returns panels.

Data-driven optimization improves mean-variance portfolios by penalizing norms.

Improved mean estimation for symmetric distributions with finite-sample guarantees.

Optimal B-robust estimate is constructed for multidimensional parameter in drift coefficient of diffusion type process with small noise. Optimal mean-variance robust (optimal V -robust) trading strategy is find to hedge in mean-variance sense the contingent claim in incomplete financial market with arbitrary informatio…

The paper estimates common mean of entangled Gaussians with bounded variances.

Simplifies risk minimization combining mean and standard deviation.

Estimates mean from heavy-tailed data without variance.

The paper connects semi-parametric estimates to European option pricing.

The study compares parametric and nonparametric models for estimating mean-variance mixtures and finds that nonparametric models perform better.

Paper improves statistical efficiency of median-of-means estimator for Byzantine robust distributed inference.

We consider the high-dimensional heteroscedastic regression model, where the mean and the log variance are modeled as a linear combination of input variables. Existing literature on high-dimensional linear regres- sion models has largely ignored non-constant error variances, even though they commonly occur in a variety…

Cooperative model disentangles data uncertainties.

Paper shows MoM is optimal under adversarial contamination for certain distributions.

This paper considers mean-variance optimization under uncertainty, specifically when one desires a sparsified set of optimal portfolio weights. From the standpoint of a Bayesian investor, our approach produces a small portfolio from many potential assets while acknowledging uncertainty in asset returns and parameter es…

In this paper we propose a novel variance reduction approach for additive functionals of Markov chains based on minimization of an estimate for the asymptotic variance of these functionals over suitable classes of control variates. A distinctive feature of the proposed approach is its ability to significantly reduce th…

We analyze the variance of Fisher information estimators in deep learning models.

It is well known that the out-of-sample performance of Markowitz's mean-variance portfolio criterion can be negatively affected by estimation errors in the mean and covariance. In this paper we address the problem by regularizing the mean-variance objective function with a weighted elastic net penalty. We show that the…

The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties. Being able to efficiently estimate the variance of this estimator is very helpful to vario…

A new approach to VAEs tackles variance shrinkage using quantile regression.

Study optimal portfolio selection using average and current profitability of risky assets.

Contrastive Divergence (CD) and Persistent Contrastive Divergence (PCD) are popular methods for training the weights of Restricted Boltzmann Machines. However, both methods use an approximate method for sampling from the model distribution. As a side effect, these approximations yield significantly different biases and…