Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (SE) on different scales...

Abstract. We consider the problem of estimating a vector x in the linear model Ax ≈ y, where A is a block circulant (BC) matrix with N blocks and x is assumed to have a weighted norm bound. In the case where both A and y are subjected to noise, we propose a minimax mean-squared error (MSE) approach in which we seek the linear estimator that minimizes the worst-case MSE over a BC structured unce...

To quantify and characterize seasonal variation in monosymptomatic optic neuritis (MON) onsets, multiple sclerosis (MS) onsets and MS exacerbations (MSE), a meta-analysis was performed, using established methods and pooling weighted information obtained from nine reports on MON, six reports on MS onsets and nine reports on MSE, which fulfilled specific criteria for report quality and data homog...

The Signal-to-Interference-plus-Noise Ratio (SINR) is an important metric of wireless communication link quality. SINR estimates have several important applications. These include optimizing the transmit power level for a target quality of service, assisting with handoff decisions and dynamically adapting the data rate for wireless Internet applications. Accurate SINR estimation provides for bo...

Ridge regression is often favored in the analysis of ill-conditioned systems. A canonical form identifies regions in the parameter space where Ordinary Least Squares (OLS) is problematic. The objectives are two-fold: To reexamine the view that ill-conditioning necessarily degrades essentials of OLS; and to reassess ranges of the ridge parameter k where ridge is efficient in mean squared error (...

Local models and methods construct function estimates or predictions from observations in a local neighborhood of the point of interest. The bandwidth, i.e., how large the local neighborhood should be, is often determined based on asymptotic analysis. In this report, an alternative, non-asymptotic approach that minimizes a uniform upper bound on the mean square error for a linear or affine esti...

Every program must continuously evolve, or it will become obsolete. This paper explores a methodology for software evolution within the setting of object-orientated programming. The methodology is based on the top–down propagation of change, and it is remotely related to stepwise refinement. To present the methodology, this paper uses one small example (Gregorian calendar) and one medium-sized ...

This paper develops a new efficient estimator for the average treatment effect, if selection for treatment is on observables. The new estimator is linear in the first-stage nonparametric estimator. This simplifies the derivation of the means squared error (MSE) of the estimator as a function of the number of basis functions that is used in the first stage nonparametric regression. We propose an...

This paper presents the adaptation of a single layer complex valued neural network (NN) to use entropy in the cost function instead of the usual mean squared error (MSE). This network has the good property of having only one layer so that there is no need to search for the number of hidden layer neurons: the topology is completely determined by the problem. We extend the existing stochastic MSE...

The sun spot time series is a random series generated with a complicated nonlinear system. Time delay neural networks (TDNN) are typical nonlinear systems that could be used to perform time series prediction. In this project, we applied the TDNN trained with both MSE and MEE criteria to predict the time series. Both criteria showed the effectiveness of the nonlinear systems. Furthermore, the TD...