BACKGROUND A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a cer...

We consider Bayesian shrinkage predictions for the Normal regression problem under the frequentist Kullback-Leibler risk function. Firstly, we consider the multivariate Normal model with an unknown mean and a known covariance. While the unknown mean is fixed, the covariance of future samples can be different from training samples. We show that the Bayesian predictive distribution based on the u...

The role of regularization is to control fitted model complexity and variance by penalizing (or constraining) models to be in an area of model space that is deemed reasonable. This is typically achieved by penalizing a parametric or non-parametric representation of the model. In this paper we advocate the use of prior knowledge or expectations about the predictions of models for regularization....

In this article, we introduce and study local constant and our preferred local linear nonparametric regression estimators when it is appropriate to assess performance in terms of mean squared relative error of prediction. We give asymptotic results for both boundary and non-boundary cases. These are special cases of more general asymptotic results that we provide concerning the estimation of th...

Robotic systems are widely applied in process industry to reduce manufacturing labor costs and increase production productivity. Due to the uncertainties existed in the manufacturing environment, the performance improvement of the assembly process is important yet challenging. This paper proposes a regression-based method to predict the performance of the robotic assembly process. Statistical h...

This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and we...

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Dimension reduction for regression is a prominent issue today because technological advances now allow scientists to routinely formulate regressions in which the number of predictors is considerably larger than in the past. While several methods have been proposed to deal with such regressions, principal components (PCs) still seem to be the most widely used across the applied sciences. We give...

This paper introduces two methods for estimating reliable prediction intervals for local linear least-squares regressions, named Bounded Oscillation Prediction Intervals (BOPI). It also proposes a new measure for comparing interval prediction models named Equivalent Gaussian Standard Deviation (EGSD). The experimental results compare BOPI to other methods using coverage probability, Mean Interv...

Expressions are derived for generalized ridge and ordinary ridge predictors that are optimal in terms of mean squared error of prediction (MSEP) for predicting the response at a single or at multiple future observation(s). Using the MSEP criterion, operational predictors are compared to the ordinary least squares (OLS) predictor and to several biased predictors derived from some popular biased ...