6 citations · 10 across the 8 of their papers we have counts for
8 papers
Generative Predictive Distributions for Time Series
Jordi Llorens-Terrazas, Mika Meitz
We propose a flexible framework for modeling the predictive distributions of nonlinear, possibly multivariate time series. Our approach expresses a general predictive distribution…
Minimax asymptotics
Mika Meitz, Alexander Shapiro
In this paper, we consider asymptotics of the optimal value and the optimal solutions of parametric minimax estimation problems. Specifically, we consider estimators of the optimal…
Subgeometrically ergodic autoregressions with autoregressive conditional heteroskedasticity
Mika Meitz, Pentti Saikkonen
In this paper, we consider subgeometric (specifically, polynomial) ergodicity of univariate nonlinear autoregressions with autoregressive conditional heteroskedasticity (ARCH). The…
Statistical inference for generative adversarial networks and other minimax problems
Mika Meitz
This paper studies generative adversarial networks (GANs) from the perspective of statistical inference. A GAN is a popular machine learning method in which the parameters of two n…
Subgeometric ergodicity and -mixing
Mika Meitz, Pentti Saikkonen
It is well known that stationary geometrically ergodic Markov chains are -mixing (absolutely regular) with geometrically decaying mixing coefficients. Furthermore, for initial d…
Subgeometrically ergodic autoregressions
Mika Meitz, Pentti Saikkonen
In this paper we discuss how the notion of subgeometric ergodicity in Markov chain theory can be exploited to study stationarity and ergodicity of nonlinear time series models. Sub…
A mixture autoregressive model based on Student's -distribution
Mika Meitz, Daniel Preve, Pentti Saikkonen
A new mixture autoregressive model based on Student's -distribution is proposed. A key feature of our model is that the conditional -distributions of the component models are…
Testing for observation-dependent regime switching in mixture autoregressive models
Mika Meitz, Pentti Saikkonen
Testing for regime switching when the regime switching probabilities are specified either as constants (`mixture models') or are governed by a finite-state Markov chain (`Markov sw…