Generalization for slowly mixing processes

04/28/2023
by   Andreas Maurer, et al.
0

A bound uniform over various loss-classes is given for data generated by stationary and phi-mixing processes, where the mixing time (the time needed to obtain approximate independence) enters the sample complexity only in an additive way. For slowly mixing processes this can be a considerable advantage over results with multiplicative dependence on the mixing time. The admissible loss-classes include functions with prescribed Lipschitz norms or smoothness parameters. The bound can also be applied to be uniform over unconstrained loss-classes, where it depends on local Lipschitz properties of the function on the sample path.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/04/2011

Estimating β-mixing coefficients

The literature on statistical learning for time series assumes the asymp...
research
11/15/2021

Joint FCLT for the Sample Quantile and Measures of Dispersion for Functionals of Mixing Processes

In this paper, we establish a joint (bivariate) functional central limit...
research
12/26/2015

Statistical Learning under Nonstationary Mixing Processes

We study a special case of the problem of statistical learning without t...
research
07/23/2010

Uniform Approximation and Bracketing Properties of VC classes

We show that the sets in a family with finite VC dimension can be unifor...
research
06/13/2021

Inferring the mixing properties of an ergodic process

We propose strongly consistent estimators of the ℓ_1 norm of the sequenc...
research
09/18/2018

Gram Charlier and Edgeworth expansion for sample variance

In this paper, we derive a valid Edgeworth expansions for the Bessel cor...
research
06/13/2020

Convergence of the empirical two-sample U-statistics with β-mixing data

We consider the empirical two-sample U-statistic with strictly β-mixing ...

Please sign up or login with your details

Forgot password? Click here to reset