Robust Independence Tests with Finite Sample Guarantees for Synchronous
Stochastic Linear Systems
- URL: http://arxiv.org/abs/2308.02054v1
- Date: Thu, 3 Aug 2023 21:13:34 GMT
- Title: Robust Independence Tests with Finite Sample Guarantees for Synchronous
Stochastic Linear Systems
- Authors: Ambrus Tam\'as, D\'aniel \'Agoston B\'alint, Bal\'azs Csan\'ad Cs\'aji
- Abstract summary: The paper introduces robust independence tests with non-asymptotically guaranteed significance levels for linear time-invariant systems.
Our method provides bounds for the type I error probabilities that are distribution-free, i.e., the innovations can have arbitrary distributions.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The paper introduces robust independence tests with non-asymptotically
guaranteed significance levels for stochastic linear time-invariant systems,
assuming that the observed outputs are synchronous, which means that the
systems are driven by jointly i.i.d. noises. Our method provides bounds for the
type I error probabilities that are distribution-free, i.e., the innovations
can have arbitrary distributions. The algorithm combines confidence region
estimates with permutation tests and general dependence measures, such as the
Hilbert-Schmidt independence criterion and the distance covariance, to detect
any nonlinear dependence between the observed systems. We also prove the
consistency of our hypothesis tests under mild assumptions and demonstrate the
ideas through the example of autoregressive systems.
Related papers
- Learning Controlled Stochastic Differential Equations [61.82896036131116]
This work proposes a novel method for estimating both drift and diffusion coefficients of continuous, multidimensional, nonlinear controlled differential equations with non-uniform diffusion.
We provide strong theoretical guarantees, including finite-sample bounds for (L2), (Linfty), and risk metrics, with learning rates adaptive to coefficients' regularity.
Our method is available as an open-source Python library.
arXiv Detail & Related papers (2024-11-04T11:09:58Z) - Distributionally Robust Policy and Lyapunov-Certificate Learning [13.38077406934971]
Key challenge in designing controllers with stability guarantees for uncertain systems is the accurate determination of and adaptation to shifts in model parametric uncertainty during online deployment.
We tackle this with a novel distributionally robust formulation of the Lyapunov derivative chance constraint ensuring a monotonic decrease of the Lyapunov certificate.
We show that, for the resulting closed-loop system, the global stability of its equilibrium can be certified with high confidence, even with Out-of-Distribution uncertainties.
arXiv Detail & Related papers (2024-04-03T18:57:54Z) - Uncertainty Quantification via Stable Distribution Propagation [60.065272548502]
We propose a new approach for propagating stable probability distributions through neural networks.
Our method is based on local linearization, which we show to be an optimal approximation in terms of total variation distance for the ReLU non-linearity.
arXiv Detail & Related papers (2024-02-13T09:40:19Z) - Sequential Predictive Two-Sample and Independence Testing [114.4130718687858]
We study the problems of sequential nonparametric two-sample and independence testing.
We build upon the principle of (nonparametric) testing by betting.
arXiv Detail & Related papers (2023-04-29T01:30:33Z) - Stability Bounds for Learning-Based Adaptive Control of Discrete-Time
Multi-Dimensional Stochastic Linear Systems with Input Constraints [3.8004168340068336]
We consider the problem of adaptive stabilization for discrete-time, multi-dimensional systems with bounded control input constraints and unbounded disturbances.
We propose a certainty-equivalent control scheme which combines online parameter estimation with saturated linear control.
arXiv Detail & Related papers (2023-04-02T16:38:13Z) - Data-Driven Observability Analysis for Nonlinear Stochastic Systems [5.4511976387114895]
Distinguishability and observability are key properties of dynamical systems.
We show that both concepts are equivalent for a class of systems that includes linear systems.
We propose a statistical test to determine a threshold above which two states can be considered distinguishable with high confidence.
arXiv Detail & Related papers (2023-02-23T12:51:03Z) - Large-Sample Properties of Non-Stationary Source Separation for Gaussian
Signals [2.2557806157585834]
We develop large-sample theory for NSS-JD, a popular method of non-stationary source separation.
We show that the consistency of the unmixing estimator and its convergence to a limiting Gaussian distribution at the standard square root rate are shown to hold.
Simulation experiments are used to verify the theoretical results and to study the impact of block length on the separation.
arXiv Detail & Related papers (2022-09-21T08:13:20Z) - Non-Parametric Inference of Relational Dependence [17.76905154531867]
This work examines the problem of estimating independence in data drawn from relational systems.
We propose a consistent, non-parametric, scalable kernel test to operationalize the relational independence test for non-i.i.d. observational data.
arXiv Detail & Related papers (2022-06-30T03:42:20Z) - Nonparametric Conditional Local Independence Testing [69.31200003384122]
Conditional local independence is an independence relation among continuous time processes.
No nonparametric test of conditional local independence has been available.
We propose such a nonparametric test based on double machine learning.
arXiv Detail & Related papers (2022-03-25T10:31:02Z) - Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian
Noise [59.47042225257565]
We present a novel planning method that does not rely on any explicit representation of the noise distributions.
First, we abstract the continuous system into a discrete-state model that captures noise by probabilistic transitions between states.
We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP)
arXiv Detail & Related papers (2021-10-25T06:18:55Z) - Stability and Identification of Random Asynchronous Linear
Time-Invariant Systems [81.02274958043883]
We show the additional benefits of randomization and asynchrony on the stability of linear dynamical systems.
For unknown randomized LTI systems, we propose a systematic identification method to recover the underlying dynamics.
arXiv Detail & Related papers (2020-12-08T02:00:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.