Cover Image

A Statistical Framework for Learning Preferences from the Past

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sadhukhan, Tamojit, Banerjee, Moulinath, Maulik, Krishanu, Roy, Parthanil
Format:	Preprint
Published:	2026
Subjects:	Methodology Probability Applications 62G05 (Primary) 62M05, 62M20, 60G25 (Secondary)
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Frequentist forecasting in regime-switching models with extended Hamilton filter
by: Okuyama, Kento, et al.
Published: (2025)

Conditional distributions for the nested Dirichlet process via sequential imputation
by: Donald, Evan, et al.
Published: (2025)

Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression
by: Das, Abhinav, et al.
Published: (2024)

Mixed orthogonality graphs for continuous-time state space models and orthogonal projections
by: Fasen-Hartmann, Vicky, et al.
Published: (2023)

On the optimal prediction of extreme events in heavy-tailed time series with applications to solar flare forecasting
by: Verma, Victor, et al.
Published: (2024)

Nonparametric Inference on Dose-Response Curves Without the Positivity Condition
by: Zhang, Yikun, et al.
Published: (2024)

Wishart kernel density estimation for strongly mixing time series on the cone of positive definite matrices
by: Belzile, Léo R., et al.
Published: (2025)

Some are observed, all leave traces: whole-population modeling of French elite civil servants' career paths
by: Voldoire, Théo, et al.
Published: (2023)

Inverting Poisson-Laguerre tessellations
by: van der Jagt, Thomas, et al.
Published: (2026)

Minimax estimation of the structure factor of spatial point processes
by: Mastrilli, Gabriel
Published: (2025)

Poisson hulls
by: Last, Günter, et al.
Published: (2022)

"Sound and Fury": Nonlinear Functionals of Volatility Matrix in the Presence of Jump and Noise
by: Chen, Richard Y.
Published: (2024)

Likelihood inference for exponential-trawl processes
by: Shephard, Neil, et al.
Published: (2015)

Explaining the unexplainable: leveraging extremal dependence to characterize the 2021 Pacific Northwest heatwave
by: Zhang, Likun, et al.
Published: (2023)

Lancaster correlation -- a new dependence measure linked to maximum correlation
by: Holzmann, Hajo, et al.
Published: (2023)

Longitudinal Network Models and Permutation-Uniform Markov Chains
by: Schwartz, William K., et al.
Published: (2021)

Backward Smoothing versus Fixed-Lag Smoothing in Particle Filters
by: Kitagawa, Genshiro
Published: (2026)

Design-Conditional Prior Elicitation for Dirichlet Process Mixtures: A Unified Framework for Cluster Counts and Weight Control
by: Lee, JoonHo
Published: (2026)

Direct Estimation of Schrödinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees
by: Mazhar, Othmane, et al.
Published: (2026)

The limit joint distributions of some statistics used in testing the quality of random number generators
by: Savelov, M. P.
Published: (2025)

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

Two approaches to multiple canonical correlation analysis for repeated measures data
by: Górecki, Tomasz, et al.
Published: (2025)

Measuring General Associations in Time Series: An Adaptation and Empirical Evaluation of the CODEC Coefficient in Determining Autoregressive Dynamics
by: Montaño, Juan Pablo, et al.
Published: (2025)

An Aldous-Hoover type representation for row exchangeable arrays
by: Donald, Evan, et al.
Published: (2025)

Spatio-temporal patterns of diurnal temperature: a random matrix approach I-case of India
by: Bhattacharjee, Madhuchhanda, et al.
Published: (2024)

Eliciting core spatial association from spatial time series: a random matrix approach
by: Bhattacharjee, Madhuchhanda, et al.
Published: (2026)

Nonparametric Bayesian volatility learning under microstructure noise
by: Gugushvili, Shota, et al.
Published: (2018)

Higher-Order Efficient Estimators: A Review and Simulation-Based Benchmark Study
by: Wang, Zeyi, et al.
Published: (2026)

Low-Rank and Sparse Drift Estimation for High-Dimensional Lévy-Driven Ornstein--Uhlenbeck Processes
by: Palaisti, Marina
Published: (2026)

Deep Neural Networks for Doubly Robust Estimation with Nonprobability Survey Samples
by: Dai, Yufang, et al.
Published: (2026)

Nonparametric estimation of the jump rate in mean field interacting systems of neurons
by: Duarte, Aline, et al.
Published: (2025)

Bayesian inference for partial orders from random linear extensions: power relations from 12th Century Royal Acta
by: Nicholls, Geoff K., et al.
Published: (2022)

A hybrid-Hill estimator enabled by heavy-tailed block maxima
by: Neves, Claudia, et al.
Published: (2025)

Nonparametric Bayesian inference for stochastic processes with piecewise constant priors
by: Belomestny, Denis, et al.
Published: (2023)

On lead-lag estimation of non-synchronously observed point processes
by: Shiotani, Takaaki, et al.
Published: (2026)

Emperical Study on Various Symmetric Distributions for Modeling Time Series
by: Kitagawa, Genshiro
Published: (2024)

River flow modelling using nonparametric functional data analysis
by: Quintela-del-Río, Alejandro, et al.
Published: (2024)

Seasonal and Periodic Patterns in US COVID-19 Mortality using the Variable Bandpass Periodic Block Bootstrap
by: Valachovic, Edward, et al.
Published: (2024)

Periodicity in New York State COVID-19 Hospitalizations Leveraged from the Variable Bandpass Periodic Block Bootstrap
by: Ahmad, Asmaa, et al.
Published: (2024)

Estimation of the invariant measure of a multidimensional diffusion from noisy observations
by: Maillet, Raphaël, et al.
Published: (2024)