77,384 research outputs found
Bias in Estimating Multivariate and Univariate Diffusions
Published in Journal of Econometrics, 2011, https://doi.org/10.1016/j.jeconom.2010.12.006</p
Fitting a function to time-dependent ensemble averaged data
Time-dependent ensemble averages, i.e., trajectory-based averages of some
observable, are of importance in many fields of science. A crucial objective
when interpreting such data is to fit these averages (for instance, squared
displacements) with a function and extract parameters (such as diffusion
constants). A commonly overlooked challenge in such function fitting procedures
is that fluctuations around mean values, by construction, exhibit temporal
correlations. We show that the only available general purpose function fitting
methods, correlated chi-square method and the weighted least squares method
(which neglects correlation), fail at either robust parameter estimation or
accurate error estimation. We remedy this by deriving a new closed-form error
estimation formula for weighted least square fitting. The new formula uses the
full covariance matrix, i.e., rigorously includes temporal correlations, but is
free of the robustness issues, inherent to the correlated chi-square method. We
demonstrate its accuracy in four examples of importance in many fields:
Brownian motion, damped harmonic oscillation, fractional Brownian motion and
continuous time random walks. We also successfully apply our method, weighted
least squares including correlation in error estimation (WLS-ICE), to particle
tracking data. The WLS-ICE method is applicable to arbitrary fit functions, and
we provide a publically available WLS-ICE software.Comment: 47 pages (main text: 15 pages, supplementary: 32 pages
On the generalization of linear least mean squares estimation to quantum systems with non-commutative outputs
The purpose of this paper is to study the problem of generalizing the
Belavkin-Kalman filter to the case where the classical measurement signal is
replaced by a fully quantum non-commutative output signal. We formulate a least
mean squares estimation problem that involves a non-commutative system as the
filter processing the non-commutative output signal. We solve this estimation
problem within the framework of non-commutative probability. Also, we find the
necessary and sufficient conditions which make these non-commutative estimators
physically realizable. These conditions are restrictive in practice.Comment: 31 page
Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach
It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV
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