A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA), independent component analysis (ICA), anechoic demixing, and the time-varying synergy model (d'Avella and Tresch, 2002). However, choosing the parameters of these models, or indeed...

Abstract ARIMA model forecasting algorithm is a commonly used time series algorithm, this paper first obtains stable sequence through differential operation, and then from the AR model, as MA even model. Select appropriate for prediction use it adaptive mode design. In field of machine learning, complexity likely to increase, while accuracy improves, models with complex structure usually cause ...

In the problem of selecting variables in a multivariate linear regression model, we derive new Bayesian information criteria based on prior mixing smooth distribution and delta distribution. Each them can be interpreted as fusion Akaike criterion (AIC) (BIC). Inheriting their asymptotic properties, our are consistent variable selection both large-sample high-dimensional frameworks. numerical si...

We discuss empirical comparison of analytical methods for model selection. Currently, there is no consensus on the best method for finite-sample estimation problems, even for the simple case of linear estimators. This article presents empirical comparisons between classical statistical methods - Akaike information criterion (AIC) and Bayesian information criterion (BIC) - and the structural ris...

Consider the problem of modeling hysteresis for finite-state random walks using higher-order Markov chains. This Letter introduces a Bayesian framework to determine, from data, the number of prior states of recent history upon which a trajectory is statistically dependent. The general recommendation is to use leave-one-out cross validation, using an easily-computable formula that is provided in...

In evolutionary studies, a number of methods have been proposed so far for reconstruction of phylogenetic tree. But, it has been pointed out, that even maximum likelihood method which is considered as the most rigorous one still has some problems for estimating multifurcate tree. Two kinds of approaches are thought to be appropriate to resolve this problem. One is the approach by information cr...

We propose a simple algorithm for improving the MDL (minimum description length) estimator of the number of sources of signals impinging on multiple sensors. The algorithm is based on the norms of vectors whose elements are the normalized and nonlinearly scaled eigenvalues of the received signal covariance matrix and the corresponding normalized indexes. Such norms are used to discriminate the ...

In model selection literature two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike’s information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric s...