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Machine and Statistical learning techniques become more and more important for the analysis of psychological data. Four core concepts of machine learning are the bias variance trade-off, cross-validation, regularization, and basis expansion. We present some early psychometric papers, from almost a century ago, that dealt with cross-validation and regularization. From this review it is safe to conclude that the origins of these lie partly in the field of psychometrics. From our historical review, two new ideas arose which we investigated further: The first is about the relationship between reliability and predictive validity; the second is whether optimal regression weights should be estimated by regularizing their values towards equality or shrinking their values towards zero. In a simulation study we show that the reliability of a test score does not influence the predictive validity as much as is usually written in psychometric textbooks. Using an empirical example we show that regularization towards equal regression coefficients is beneficial in terms of prediction error.
Monitoring several correlated quality characteristics of a process is common in modern manufacturing and service industries. Although a lot of attention has been paid to monitoring the multivariate process mean, not many control charts are available
This paper reviews two main types of prediction interval methods under a parametric framework. First, we describe methods based on an (approximate) pivotal quantity. Examples include the plug-in, pivotal, and calibration methods. Then we describe met
Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of randomization-based
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative power of deep neural networks. Recent works have demonstrated the great po
When making choices in software projects, engineers and other stakeholders engage in decision making that involves uncertain future outcomes. Research in psychology, behavioral economics and neuroscience has questioned many of the classical assumptio