Prediction intervals are needed to quantify prediction uncertainty in, for example, warranty prediction. Naïve prediction intervals (also known as intervals from the “plug-in method”) ignore the uncertainty in parameter estimates. Simulation-based calibration methods can be used to improve prediction interval coverage probabilities. This article evaluates the coverage probabilities for naive an...

presented as: A Multimarker Strategy for Risk Prediction in Heart Failure: Application of Novel Methods in a Community Cohort. American Heart Association 2008 Scientific Sessions, Elizabeth-Barrett Connor Award Finalist, Oral Presentation November 9, 2008. New Orleans, LA. Word Count: Total 5992 Tables: 4 Figures: 1 Journal Subject Codes: [8] Epidemiology, [110] Congestive Heart Failure by gest...

BACKGROUND Dynamic hip screw (DHS) is a common device for treating intertrochanteric fracture (ITF). Various risk factors have been reported to be associated with the operative treatment outcome. However, an integrated risk scoring prediction model is lacking. In this study, we aimed to develop a prediction model for treatment outcome of intertrochanteric fracture. METHODS We analyzed 442 AO/...

Using a conditional method, explicit formulae for computing quantiles pertinent to prediction intervals for future Weibull order statistics are developed for two cases: when only previous independent failure data are available, and when both previous independent failure data and early-failure data in current experiment are available. The second case includes the case when only current early-fai...

A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research work, using the artificial neural network (ANN), a model was built to predict the ultimat...

To develop an effective preventive or proactive repair and replacement action plan, water utilities often rely on water main failure prediction models. However, in the prediction modeling water mains failure, uncertainty is inherent regardless of quality and quantity of data used in model-data fusion. To improve the understanding of water main failure processes, a new and effective Bayesian fra...

Abstract. In this paper we design an econometric test for monotone comparative statics (MCS) often found in models with multiple equilibria. Our test exploits the observable implications of the MCS prediction: that the extreme (high and low) conditional quantiles of the dependent variable increase monotonically with the explanatory variable. The main contribution of the paper is to derive a lik...

BACKGROUND The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. METHODS AND RESULTS Using data fro...