arXiv research

Efficient estimators for smooth Hilbert-valued parameters with theoretical guarantees.

Function-space MAP estimation leads to better generalization and robustness.

Dual Bayesian Affine Estimators for Wiener-type state-space models

New approach uses negative controls to estimate causal parameters without completeness conditions.

We consider inference about a scalar parameter under a non-parametric model based on a one-step estimator computed as a plug in estimator plus the empirical mean of an estimator of the parameter's influence function. We focus on a class of parameters that have influence function which depends on two infinite dimensiona…

MPF method improves parameter estimation in probabilistic models.

Proposes debiasing strategy for ill-posed regression problems.

Solves challenges in estimating parameters of softmax gating Gaussian mixture models.

Estimates parameters in a deviated Gaussian mixture model.

Supervised learning is an active research area, with numerous applications in diverse fields such as data analytics, computer vision, speech and audio processing, and image understanding. In most cases, the loss functions used in machine learning assume symmetric noise models, and seek to estimate the unknown function …

Develops methods for estimating constrained function-valued parameters in infinite-dimensional models.

A new method debiases multiple target parameters without IFs.

Optimal tuning for estimating ECC in proportional asymptotics.

Paper extends a method to estimate Hurst parameter for rough stochastic volatility models.

New insights into the top-K sparse softmax gating function for deep learning.

Proposes a new method for estimating non-pathwise differentiable functional parameters.

We study the parameter estimation problem for a varying index coefficient model in high dimensions. Unlike the most existing works that iteratively estimate the parameters and link functions, based on the generalized Stein's identity, we propose computationally efficient estimators for the high-dimensional parameters w…

Estimates system parameters from a single observation using kernel-based score.

This paper studies a class of exponential family models whose canonical parameters are specified as linear functionals of an unknown infinite-dimensional slope function. The optimal minimax rates of convergence for slope function estimation are established. The estimators that achieve the optimal rates are constructed …

The paper establishes bounds on the smoothness parameter in Gaussian process interpolation.

Simplifies efficient estimation via automatic differentiation and probabilistic programming.

New method improves parameter estimation in complex stochastic models.

Unified framework for automatic debiased machine learning for various statistical parameters.

A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe. Galaxy surveys provide the means to map out cosmic large-scale structure in thr…

A fast method for estimating radar amplitude density parameters.

LSBI approximates likelihood with linear functions for cosmological parameter estimation.

With an eye towards human-centered automation, we contribute to the development of a systematic means to infer features of human decision-making from behavioral data. Motivated by the common use of softmax selection in models of human decision-making, we study the maximum likelihood parameter estimation problem for sof…

Extends robust methods for causal inference, improving estimator performance.

New method for estimating parameters in inverse problems using double robustness.

We develop a new approach to learn the parameters of regression models with hidden variables. In a nutshell, we estimate the gradient of the regression function at a set of random points, and cluster the estimated gradients. The centers of the clusters are used as estimates for the parameters of hidden units. We justif…

The paper fits a seven-parameter GTS distribution to financial data.

Proposes a new estimator for causal mediation with continuous treatments.

A hybrid neural network improves robustness in estimating vehicle parameters from noisy data.

Proposes an exponentially increasing step-size for faster parameter estimation in statistical models.

This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PE…

Iterative methods for fitting a Gaussian Random Field (GRF) model via maximum likelihood (ML) estimation requires solving a nonconvex optimization problem. The problem is aggravated for anisotropic GRFs where the number of covariance function parameters increases with the dimension. Even evaluation of the likelihood fu…

This paper presents the asymptotic behavior of a linear instrumental variables (IV) estimator that uses a ridge regression penalty. The regularization tuning parameter is selected empirically by splitting the observed data into training and test samples. Conditional on the tuning parameter, the training sample creates …

This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expec…

Analyzes convergence rates for Gaussian-gated MoE model.

We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference, which involves as a nuisance the covariate-condit…

We prove an estimate for spherical functions $\varphi_λ(a)$ on $\mathrm{SL}(3,\mathbb{R})$, establishing uniform decay in the spectral parameter $λ$ when the group parameter $a$ is restricted to a compact subset of the abelian subgroup $\mathrm{A}$. In the case of $\mathrm{SL}(3,\mathbb{R})$, it improves a result by J.…

Develops a direct debiased machine learning framework using Bregman divergence.

The Schwartz-Smith model parameters are estimated using Kalman Filter with additional constraints.

Policy evaluation is a key process in reinforcement learning. It assesses a given policy using estimation of the corresponding value function. When using a parameterized function to approximate the value, it is common to optimize the set of parameters by minimizing the sum of squared Bellman Temporal Differences errors…

The paper improves parameter estimation for interest rate models using the CIR and CKLS frameworks.

Proposes a new estimator for weak instrumental variables in panel data models.

In this paper we study the problem of recovering a structured but unknown parameter ${\bfθ}^*$ from $n$ nonlinear observations of the form $y_i=f(\langle {\bf{x}}_i,{\bfθ}^*\rangle)$ for $i=1,2,\ldots,n$. We develop a framework for characterizing time-data tradeoffs for a variety of parameter estimation algorithms when…

New matching estimators correct bias in multivariate settings without smoothing parameters.