We studied how lagged linear regression can be used to detect the physiologic effects of drugs from data in the electronic health record (EHR). We systematically examined the effect of methodological variations ((i) time series construction, (ii) temporal parameterization, (iii) intra-subject normalization, (iv) differencing (lagged rates of change achieved by taking differences between consecu...

Content-based prediction of musical emotions and moods has a large number of exciting applications in Music Information Retrieval. However, what should be predicted, and precisely how, remain a challenge in the field. We provide an empirical comparison of two common paradigms of emotion representation in music, opposing a multidimensional space to a set of basic emotions. New groundtruth data c...

Lagged cross-correlation and regression analysis are commonly used to gain insights into interaction mechanisms between climatological processes, in particular to assess time delays and to quantify the strength of a mechanism. Exemplified on temperature anomalies in Europe and the tropical Pacific and Atlantic, the authors study lagged correlation and regressions analytically for a simple model...

Dimension reduction aims to reduce the complexity of a regression without requiring a pre-specified model. In the case of multivariate response regressions, covariance-based estimation methods for the k-th moment based dimension reduction subspaces circumvent slicing and nonparametric estimation so that they are readily applicable to multivariate regression settings. In this article, the covari...

This paper extends Woodford’s (2010) approach to the robustly monetary policy to a general linear quadratic framework. We provide algorithms to solve for a time-invariant linear robustly optimal policy from a timeless perspective and for a time-invariant linear Markov perfect equilibrium under discretion. We apply our methods to a New Keynesian model of monetary policy with persistent cost-push...

the fuzzy linear regression model with fuzzy input-output data andcrisp coefficients is studied in this paper. a linear programmingmodel based on goal programming is proposed to calculate theregression coefficients. in contrast with most of the previous works, theproposed model takes into account the centers of fuzzy data as animportant feature as well as their spreads in the procedure ofconstr...