Cover Image

A Novel Theoretical Framework for Exponential Smoothing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bernardi, Enrico, Lanconelli, Alberto, Lauria, Christopher S. A.
Format:	Preprint
Published:	2024
Subjects:	Methodology Probability Machine Learning 65K05, 62F12
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Non trivial optimal sampling rate for estimating a Lipschitz-continuous function in presence of mean-reverting Ornstein-Uhlenbeck noise
by: Bernardi, Enrico, et al.
Published: (2024)

Inverse Probability Weighting: from Survey Sampling to Evidence Estimation
by: Datta, Jyotishka, et al.
Published: (2022)

Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis
by: Lambert, Guerlain, et al.
Published: (2026)

Unbiased least squares regression via averaged stochastic gradient descent
by: Kahalé, Nabil
Published: (2024)

On sample complexity for covariance estimation via the unadjusted Langevin algorithm
by: Nakakita, Shogo
Published: (2026)

Leveraging semantic similarity for experimentation with AI-generated treatments
by: Shi, Lei, et al.
Published: (2025)

Asymptotic expansion of the drift estimator for the fractional Ornstein-Uhlenbeck process
by: Tudor, Ciprian A., et al.
Published: (2024)

Nested Pseudo-GMM Estimation of Demand for Differentiated Products
by: Aguirregabiria, Victor, et al.
Published: (2026)

Estimation Method under Three-Parameter Generalized Exponential Model: Consistency, Uniqueness and its Applications
by: Prajapat, Kiran, et al.
Published: (2024)

Parameter Estimation in Stochastic Differential Equations via Wiener Chaos Expansion and Stochastic Gradient Descent
by: Delgado-Vences, Francisco, et al.
Published: (2026)

Inference for high-dimensional linear expectile regression with de-biased method
by: Li, Xiang, et al.
Published: (2024)

A Modified Bayesian Criterion for Model Selection in Mixed and Hierarchical Frameworks
by: Ramirez, Diogenes de Jesus, et al.
Published: (2026)

A copula-based rank histogram ensemble filter
by: Subrahmanya, Amit N., et al.
Published: (2025)

Bayesian experimental design: grouped geometric pooled posterior via ensemble Kalman methods
by: Yang, Huchen, et al.
Published: (2026)

Central Limit Theorem for the $σ$-antithetic multilevel Monte Carlo method
by: Alaya, Mohamed Ben, et al.
Published: (2020)

Perfect Clustering in Nonuniform Hypergraphs
by: Chan, Ga-Ming Angus, et al.
Published: (2025)

Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
by: Zhong, Shijie, et al.
Published: (2024)

Moment-Based Inference for Regression with Latent Dirichlet Covariates
by: Jiang, Ziyu
Published: (2026)

Statistical Inference for Quasi-Infinitely Divisible Distributions via Fourier Methods
by: Panov, Vladimir, et al.
Published: (2025)

Fast Mixing of Data Augmentation Algorithms: Bayesian Probit, Logit, and Lasso Regression
by: Lee, Holden, et al.
Published: (2024)

Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation
by: Tan, Hong Ye, et al.
Published: (2024)

Mean--Variance Risk-Aware Bayesian Optimal Experimental Design for Nonlinear Models
by: Shen, Wanggang, et al.
Published: (2026)

Fused Multinomial Logistic Regression Utilizing Summary-Level External Machine-learning Information
by: Dai, Chi-Shian, et al.
Published: (2026)

Asymptotics for non-degenerate multivariate $U$-statistics with estimated nuisance parameters under the null and local alternative hypotheses
by: Desgagné, Alain, et al.
Published: (2024)

Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis
by: Chen, Qiao, et al.
Published: (2024)

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
by: Aich, Agnideep, et al.
Published: (2025)

A Multi-fidelity Estimator of the Expected Information Gain for Bayesian Optimal Experimental Design
by: Coons, Thomas E., et al.
Published: (2025)

When to Trust Confidence Thresholding: Calibration Diagnostics for Pseudo-Labelled Regression
by: Kurbucz, Marcell T.
Published: (2026)

Modeling Stock Returns and Volatility Using Bivariate Gamma Generalized Laplace Law
by: Kozubowski, Tomasz J., et al.
Published: (2026)

Bayesian Covariance Uncertainty for Adaptive Pilot-Sampling Termination in Multi-fidelity Uncertainty Quantification
by: Coons, Thomas E., et al.
Published: (2025)

Covariance Expressions for Multi-Fidelity Sampling with Multi-Output, Multi-Statistic Estimators: Application to Approximate Control Variates
by: Dixon, Thomas O., et al.
Published: (2023)

Theoretical guarantees for neural control variates in MCMC
by: Belomestny, Denis, et al.
Published: (2023)

Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
by: Shen, Wanggang, et al.
Published: (2021)

Sequential Monte Carlo approximations of Wasserstein--Fisher--Rao gradient flows
by: Crucinio, Francesca R., et al.
Published: (2025)

Statistical Inference with Stochastic Gradient Methods under $ϕ$-mixing Data
by: Liu, Ruiqi, et al.
Published: (2023)

Statistical comparison of Hidden Markov Models via Fragment Analysis
by: Hernandez-Suarez, Carlos M., et al.
Published: (2025)

On consistency of Bayesian parameter estimations for a class of ergodic Markov models
by: Nurieva, A. I., et al.
Published: (2022)

Three approaches to supervised learning for compositional data with pairwise logratios
by: Coenders, Germa, et al.
Published: (2021)

Inferring the dependence graph density of binary graphical models in high dimension
by: Chevallier, Julien, et al.
Published: (2024)

A Reduced Basis Decomposition Approach to Efficient Data Collection in Pairwise Comparison Studies
by: Jiang, Jiahua, et al.
Published: (2025)