Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

Quantifying Model Errors Caused by Nonlinear Undermodeling in Linear System Identification

The problem of quantifying errors due to nonlinear undermodeling is addressed. It is assumed that the system consists of a linear dynamic block in cascade with a static nonlinearity and that the objective is to identify the linear part using a purely linear model. The stochastic embedding approach is applied to capture the on-average properties of the undermodeling. As compared to previous methods, the priors on the covariance matrix of the embedding parameters are reduced. As a result an expression for the amplitude error bounds of the estimated transfer function, that does not require knowledge of the true system parameters, is obtained. The quality of this measure depends on the degree of accuracy with which the unknown nonlinearity can be represented using a set of known basis functions. The proposed method simultaneously delivers error bounds on the estimated transfer function and an indirect estimate of the size of the nonlinearity. The importance of obtaining error bounds on transfer functions and estimates of static nonlinearities for controller design is well established in the literature

On maximum likelihood identification of errors-in-variables models

\u3cp\u3eIn this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.\u3c/p\u3