Sample continuity with probability one for the estimator of impulse response function

The problem of estimation of a stochastic linear system has been a matter of active research for the last years. One of the simplest models considers a ‘black box’ with some input and a certain output. The input may be single or multiple and there is the same choice for the output. This generates a great amount of models that can be considered. The sphere of applications of these models is very extensive, ranging from signal processing and automatic control to econometrics (errors-in-variables models). In this paper a time-invariant continuous linear system is considered with a real-valued impulse response function. We assume that impulse function is square-integrable. Input signal is supposed to be Gaussian stationary stochastic process with known spectral density. A sample input–output crosscorrelogram is taken as an estimator of the response function. The conditions on sample continuousness with probability one for impulse response function are investigated.

A Simple Model of Attentional Blink

The attentional blink (AB) effect is the reduced ability of subjects to report a second target stimuli (T2) amonga rapidly presented series of non-target stimuli, when it appears within a time window of about 200-500 msafter a first target (T1). We present a simple dynamical systems model explaining the AB as resulting fromthe temporal response dynamics of a stochastic, linear system with threshold, whose output represents theamount of attentional resources allocated to the incoming sensory stimuli. The model postulates that theavailable attention capacity is limited by activity of the default mode network (DMN), a correlated set ofbrain regions related to task irrelevant processing which is known to exhibit reduced activation followingmental training such as mindfulness meditation. The model provides a parsimonious account relating keyfindings from the AB, DMN and meditation research literature, and suggests some new testable predictions.

On the convergence rate for the estimation of impulse response function in the space Lp(T)

The problem of estimation of a stochastic linear system has been a matter of active research for the last years. One of the simplest models considers a ‘black box’ with some input and a certain output. The input may be single or multiple and there is the same choice for the output. This generates a great amount of models that can be considered. The sphere of applications of these models is very extensive, ranging from signal processing and automatic control to econometrics (errors-in-variables models). In this paper a time-invariant continuous linear system is considered with a real-valued impulse response function. We assume that impulse function is square-integrable. Input signal is supposed to be Gaussian stationary stochastic process with known spectral density. A sample input–output cross-correlogram is taken as an estimator of the response function. An upper bound for the tail of the distribution of the estimation error is found that gives a convergence rate of estimator to impulse response function in the space Lp(T).

Convergence rate for the estimation of impulse response function in the space of continuous functions

The problem of estimation of a stochastic linear system has been a matter of active research for the last years. One of the simplest models considers a ‘black box’ with some input and a certain output. The input may be single or multiple and there is the same choice for the output. This generates a great amount of models that can be considered. The sphere of applications of these models is very extensive, ranging from signal processing and automatic control to econometrics (errors-in-variables models). In this paper a time-invariant continuous linear system is considered with a real-valued impulse response function. We assume that impulse function is square-integrable. Input signal is supposed to be Gaussian stationary stochastic process with known spectral density. A sample input–output cross-correlogram is taken as an estimator of the response function. An upper bound for the tail of the distribution of the supremum of the estimation error is found that gives a convergence rate of estimator to impulse response function.