We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamics in a computationally efficient and parameter tuning-free iterative framework known as SPICE (Sparse Iterative Covariance Estimation). The …
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48 results for “dynamics estimation”
This paper improves parameter estimation for autonomous systems with unmodeled dynamics.
problem Accurate parameter estimation for risk-aware autonomous systems with unmodeled dynamics.
method Spectral lines-based approach for estimating parameters of dynamic models, allowing deterministic unmodeled dynamics.
result The proposed method leads to non-asymptotic bounds on parameter estimation error, robust to unmodeled dynamics, and matches existing literature in ideal conditions.
DOODL learns shared spectral dynamics across related dynamical systems.
problem Learning independent dynamical operators for each system limits discovery of shared structure.
method DOODL learns a dictionary of characteristic spectral dynamics on a manifold of related systems.
result DOODL achieves errors one to two orders of magnitude lower than independent operator estimation methods.
The paper introduces a dynamic MVP model using high-frequency financial data.
problem Capturing the dynamics of minimum variance portfolio weights in financial markets.
method Imposes autoregressive structure on MVP processes and uses CLIME and LASSO for estimation.
result Proposes DR-MVP model with established asymptotic properties.
TAKDE optimizes kernel density estimation for real-time dynamic processes.
problem Real-time density estimation in applications like computer vision and signal processing.
method Derives asymptotic mean integrated squared error (AMISE) upper bound for 'sliding window' kernel density estimator and proposes TAKDE as a novel, theoretically optimal estimator.
result TAKDE outperforms other dynamic density estimators in terms of test log-likelihood and runtime.
Crossing penalised CAViaRq-fin.ST
Paper proposes a method to estimate multiple dynamic quantiles jointly.
problem Limited joint estimation of multiple dynamic quantiles.
method Introduces a crossing penalty objective function for joint estimation.
result Validation through Monte Carlo experiments and empirical application on FTSE100 shows effectiveness.
Paper models non-linear dynamics from time series data.
problem Modeling non-linear dynamical systems from time series data.
method Introduces latent state modeling and a novel alternating minimization algorithm.
result LaNoLem achieves competitive performance in dynamics estimation and prediction.
Estimates time-varying parameters from two OLS estimates.
problem Time-varying linear regression with hidden dynamics.
method Combines two OLS estimates for stable linear dynamics.
result Finite sample guarantee on estimation error.
Proposes a model for identifying edges in low-rank dynamical networks.
problem Inability of conventional methods to handle low-rank dynamical networks.
method Low rank dynamical network model with causal Wiener filtering.
result Consistent method for estimating all network edges.
The paper develops methods to estimate optimal treatment sequences under policy constraints.
problem Estimating the best sequence of treatments over multiple stages for individuals.
method Empirical welfare maximization approach, solving treatment assignment sequentially or simultaneously.
result Established convergence rates and upper bounds for estimation methods.
Study models forest transitions with deep learning for parameter estimation.
problem Complex dynamics of forest, agricultural, and abandoned lands.
method Developed a stochastic differential equation model and used deep learning for parameter estimation.
result Deep learning approach estimates model parameters from time-series data.
Estimates system parameters from a single observation using kernel-based score.
problem Estimating parameters of a dynamical system from a high-dimensional signal.
method Kernel-based score to compare temporal dependencies between signal and model.
result Accuracy and efficiency demonstrated on chaotic systems.
A new method reduces complexity in estimating dynamic choice models.
problem Estimating structural parameters in dynamic discrete choice models using behavioral data.
method Two-stage approach: inverse reinforcement learning for Q-function estimation, state selection via clustering, and maximum likelihood estimation with nested fixed-point algorithm.
result The method mitigates the curse of dimensionality and provides finite-sample bounds on estimation error.
Method estimates dynamic treatment effects using machine learning and g-estimation.
problem Estimating treatment effects over time with multiple treatments and potential future outcomes.
method Double/debiased machine learning framework for dynamic treatment effects, extending Neyman orthogonal cross-fitted -estimation.
result Provides finite sample guarantees and allows for non-linear effect heterogeneity and high-dimensional parameterizations.
We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density. Observations drawn from dynamical systems are not independent and moreover, usual mixing concepts may not be appropriate for measuring the dependence among these ob…
Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionalsecon.EM
New method for estimating treatment effects without complex propensity models.
problem Estimating treatment effects in dynamic treatment regimes.
method Recursive Riesz representer estimation for de-biasing corrections.
result Directly estimates de-biasing corrections without auxiliary models.
This work addresses identifiability in sequential data with switching dynamics, introducing a new estimator.
problem Identifiability of sequential data with regime-switching dynamics under flexible assumptions.
method Introduces SDS, a flow-based estimator for exact likelihood optimization.
result Demonstrates improved disentanglement and more accurate forecasting compared to VAE-based estimators.
Develops unbiased estimation method using underdamped Langevin dynamics.
problem Estimating expectations of non-negative Lebesgue density probability measures.
method Underdamped Langevin dynamics, time-discretized versions, doubly randomized estimation.
result Proves finite variance and expected/finite cost of the proposed estimator.
Langevin dynamics fails to produce accurate samples even with small score function errors.
problem Robustness of Langevin dynamics to score function errors.
method Analysis of Langevin dynamics and score function errors.
result Langevin dynamics produces a distribution far from the target distribution in TV distance even with small errors in the score function.
Estimates RL data for dynamic treatment effects using GMM.
problem Estimating dynamic treatment effects from RL data with nonstationary behavior policies.
method Weighted GMM approach to stabilize variance in adaptive RL settings.
result Valid hypothesis testing and confidence regions for dynamic treatment effects.
New method speeds up learning of complex dynamical systems.
problem Efficiently learning large-scale dynamical systems from finite data.
method Random projections (sketching) to boost kernel-based Koopman operator estimators.
result The proposed estimators maintain accuracy while significantly reducing computation time.
GARCH-UGH improves VaR estimation for financial risk management.
problem Dynamic estimation of extreme VaR in financial time series.
method AR-GARCH filtering followed by a bias-reduced extreme value estimator.
result GARCH-UGH estimates are more accurate than conventional methods.
Dynamic covariate balancing: estimating treatment effects over time with potential local projectionsecon.EM
Dynamic treatment effects estimated over time using covariate balancing.
problem Estimating treatment effects in panel data with dynamic treatments.
method Dynamic covariate balancing with potential local projections.
result Established inferential guarantees for the proposed method.
DeepBayes uses neural networks to efficiently estimate parameters in complex dynamical models.
problem Estimating parameters in stochastic, nonlinear dynamical models is challenging.
method DeepBayes leverages deep recurrent neural networks to learn an estimator that minimizes mean-squared error.
result DeepBayes achieves asymptotically equivalent performance to Bayesian estimation methods.
New estimator learns symmetric dynamics from few observations.
problem Learning parameters of stochastic linear dynamics from limited data.
method Method of moments estimator using observations.
result Achieves small maximum element-wise error on symmetric matrices.
Method estimates parameters for disease spread models robustly.
problem Estimating parameters for disease spread models.
method Statistical Learning applied to Approximate Bayesian Computation.
result Qualitative properties of disease evolution can be assessed.
We present a continuous-time maximum likelihood estimation methodology for credit rating transition probabilities, taking into account the presence of censored data. We perform rolling estimates of the transition matrices with exponential time weighting with varying horizons and discuss the underlying dynamics of trans…
The health state assessment and remaining useful life (RUL) estimation play very important roles in prognostics and health management (PHM), owing to their abilities to reduce the maintenance and improve the safety of machines or equipment. However, they generally suffer from this problem of lacking prior knowledge to …
New method identifies network dynamics and noise structure.
problem Estimating network and disturbance topologies in dynamic systems.
method Extended multi-step Sequential Linear Regression and Weighted Null Space Fitting methods.
result Consistent estimation of dynamic networks with reduced computational burden.
Dynamic CBDT improves treatment effect estimation in clinical data.
problem Estimating heterogeneous treatment effects in observational data with high accuracy and interpretability.
method Dynamic Regularized Causal Boosted Decision Trees (CBDT) integrating variance regularization and calibration.
result Significantly improved estimation accuracy and reliable coverage of true treatment effects.
Data-Efficient Non-Gaussian Semi-Nonparametric Density Estimation for Nonlinear Dynamical Systemsmath.OC
This paper presents a method for efficient density estimation in nonlinear systems.
problem Accurate representation of non-Gaussian distributions in nonlinear dynamical systems is challenging.
method Uses Seminonparametric (SNP) densities with probabilists' Hermite polynomial basis and Monte Carlo approximation for maximum likelihood estimation.
result Demonstrates that the method can accurately capture non-Gaussian density structure and compute quantiles using fewer samples than raw Monte Carlo.
The study analyzes the performance of a nonparametric estimator for dynamical systems.
problem Analyzing the performance of a nonparametric estimator for dynamical systems.
method Nonparametric least squares estimator (LSE) and information-theoretic methods.
result Rate-optimal error bounds for nonparametric hypotheses classes.
KalmanNet uses neural networks to improve state estimation in systems with unknown dynamics.
problem State estimation of systems with non-linear dynamics and partial information.
method KalmanNet integrates a recurrent neural network with the Kalman filter to handle non-linearities and model mismatches.
result KalmanNet outperforms classic filtering methods in systems with both mismatched and accurate domain knowledge.
New stability estimate for metric rigidity in hyperbolic dynamics.
problem Metric rigidity in hyperbolic dynamics.
method Radial source estimates in Hölder-Zygmund spaces for uniformly hyperbolic dynamics.
result Metrics with same marked length spectrum are isometric in -close metrics in any dimension .
We propose a neural superstatistics method to estimate dynamic cognitive models from time series data.
problem Memoryless cognitive models ignore parameter fluctuations, leading to inaccurate predictions.
method Developed a simulation-based deep learning method for Bayesian inference of superstatistical models.
result Deep learning method efficiently recovers time-varying and time-invariant parameters.
Dynamic treatment strategies on networks amplify policy impact through spillovers.
problem Effective dynamic treatment allocation in network settings.
method Q-Ising, a three-stage pipeline integrating Bayesian dynamic Ising model, treatment adoption histories, and offline reinforcement learning.
result Adaptive targeting outperforms static centrality benchmarks in Indian village microfinance networks and synthetic data.
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamicsstat.ML
Improved robust latent variable estimation for neural dynamics.
problem Inconsistent results due to noise and nonlinearity in existing models.
method Probabilistic approach to latent variable estimation in decomposed models.
result More accurate latent variable inference in nonlinear systems with diverse noise conditions.
New algorithm models satiation in recommender systems.
problem Satiation effects in user preferences not modeled by existing algorithms.
method Rebounding bandits, modeling satiation as time-invariant linear dynamical systems.
result Greedy policy optimal for identical deterministic dynamics; EEP algorithm for stochastic dynamics.
Estimates MLDS using tensor decomposition, improving upon existing methods.
problem Learning mixtures of linear dynamical systems from input-output data.
method Proposes a moment-based estimator using tensor decomposition.
result Improves sample complexity bounds for estimating MLDS.
Proposes a nonparametric model for dynamic team rankings.
problem Dynamic ranking of distinct teams over time.
method Kernel smoothing for nonparametric estimation in sparse settings.
result Time-varying oracle bounds for estimation and excess risk.
Bayesian method estimates dynamics from near-optimal trajectories.
problem Estimating dynamics from near-optimal expert trajectories in reinforcement learning.
method Constraint-based Bayesian approach integrating expert near-optimality.
result Significant improvements in decision-making and transfer success.
Paper introduces a novel method for dynamic covariance estimation with random forests.
problem Estimating high-dimensional dynamic covariance matrices with multiple covariates.
method Nonparametric approach using random forests.
result Uniform consistency theory and error rates established for high-dimensional scenarios.
Particle- and agent-based systems are a ubiquitous modeling tool in many disciplines. We consider the fundamental problem of inferring interaction kernels from observations of agent-based dynamical systems given observations of trajectories, in particular for collective dynamical systems exhibiting emergent behaviors w…
BoXHED boosts hazard estimation for dynamic health risk scores.
problem Analyzing time-varying health vitals for disease onset prediction.
method Gradient boosting for nonparametric hazard function estimation with time-dependent covariates.
result Novel interaction effects among risk factors identified in cardiovascular disease onset data.
Algorithm selects best model based on state, reducing costs.
problem Choosing the best model among many in different states of the world.
method Reinforcement learning algorithm to estimate optimal policy.
result Algorithm consistently selects optimal model based on covariates.
The paper uses double machine learning to estimate dynamic treatment effects robustly.
problem Estimating causal effects of dynamic treatments with time-varying covariates.
method Double machine learning with Neyman-orthogonal score functions for robustness.
result Asymptotic normality and -consistency of the estimators under specific conditions.
Finite-Sample Properties of Model Specification Tests for Multivariate Dynamic Regression Modelsecon.EM
Proposes a new test for validating multivariate dynamic regression models.
problem Inadequate exogeneity conditions for conventional model specification tests in dynamic systems.
method Develops a generalized Durbin estimator for multiple-equation systems with dynamic dependencies, and constructs Wald tests.
result Bootstrap-based Wald tests improve finite-sample size control and validate the null hypothesis in multifactor models.
Enhances Koopman operator estimation with intrinsic observables in RKHS.
problem Accurate estimation of Koopman operator and its spectrum.
method Jet Extended Dynamic Mode Decomposition (JetEDMD) leveraging RKHS jets.
result Proves JetEDMD's superiority with error bounds and convergence rate.