Results 81 to 90 of about 10,083 (253)
Ergodicity, lack thereof, and the performance of reservoir computing with memristive networks
Nano ExpressNetworks composed of nanoscale memristive components, such as nanowire and nanoparticle networks, have recently received considerable attention because of their potential use as neuromorphic devices.
Valentina Baccetti +3 more
doaj +1 more source
On L1-weak ergodicity of markov processes
, 2013 In the present paper we investigate the L1-weak ergodicity of nonhomogeneous discrete Markov processes with general state spaces. Note that the L1-weak ergodicity is weaker than well-known weak ergodicity.
Mukhamedov, Farrukh
core +1 more source
A Comparative Review of Specification Tests for Diffusion Models
International Statistical Review, EarlyView.Summary Diffusion models play an essential role in modelling continuous‐time stochastic processes in the financial field. Therefore, several proposals have been developed in the last decades to test the specification of stochastic differential equations.
A. López‐Pérez +3 more
wiley +1 more source
Ergodic theorem, ergodic theory, and statistical mechanics [PDF]
Proceedings of the National Academy of Sciences, 2015 This perspective highlights the mean ergodic theorem established by John von Neumann and the pointwise ergodic theorem established by George Birkhoff, proofs of which were published nearly simultaneously in PNAS in 1931 and 1932. These theorems were of great significance both in mathematics and in statistical mechanics.
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On Integral Priors for Multiple Comparison in Bayesian Model Selection
International Statistical Review, EarlyView.Summary Noninformative priors constructed for estimation purposes are usually not appropriate for model selection and testing. The methodology of integral priors was developed to get prior distributions for Bayesian model selection when comparing two models, modifying initial improper reference priors. We propose a generalisation of this methodology to
Diego Salmerón +2 more
wiley +1 more source
Statistical inference for nth-order mixed fractional Brownian motion with polynomial drift
Modern Stochastics: Theory and ApplicationsThe mixed model with polynomial drift of the form $X(t)=\theta \mathcal{P}(t)+\alpha W(t)+\sigma {B_{H}^{n}}(t)$ is studied, where ${B_{H}^{n}}$ is the nth-order fractional Brownian motion with Hurst index $H\in (n-1,n)$ and $n\ge 2$, independent of the ...
Mohamed El Omari
doaj +1 more source
On the stability of nonlinear ARMA models [PDF]
In the present paper we study the stability of a class of nonlinear ARMA models. We derive a sufficient condition to ensure the geometric ergodicity and we apply it to a very general threshold ARMA model imposing a mild assumption on the ...
Fonseca Giovanni
core
Monetary and Macroprudential Policies under Dollar‐Denominated Foreign Debt
Journal of Money, Credit and Banking, EarlyView.Abstract This paper studies monetary and macroprudential policies in a small open economy that borrows from abroad in foreign currency. The model features a novel mechanism in which exchange rate depreciation triggered by a borrowing constraint is amplified through balance of payments adjustments, increasing the real burden of foreign debt and causing ...
HIDEHIKO MATSUMOTO
wiley +1 more source
Robust CDF‐Filtering of a Location Parameter
Journal of Time Series Analysis, EarlyView.ABSTRACT This paper introduces a novel framework for designing robust filters associated with signal plus noise models having symmetric observation density. The filters are obtained by a recursion where the innovation term is a transform of the cumulative distribution function of the residuals.
Leopoldo Catania +2 more
wiley +1 more source
ON ERGODICITY AND UNIDIMENSIONALITY
Kyushu Journal of Mathematics, 1994 This note does not have any mathematical content of its own but rather consists of a list of various results (for specific examples) about dimension of a measure. A Borel measure \(\mu\) on a metric space \(X\) is \(\alpha\)-dimensional \((\dim\mu= \alpha)\) if and only if \[ \liminf_{r\to 0} {\log\mu(B_r(x))\over \log r}= \alpha,\;\;\mu\text{-a.e.} \]
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