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...

This paper investigates the research question of whether principle parsimony carries over into interval forecasting, and proposes new semiparametric prediction intervals that apply block bootstrap to first-order autoregression. The AR(1) model is parsimonious in which error term may be serially correlated. Then, utilized resample blocks consecutive observations account for serial correlation. M...

Statistical prediction plays an important role in many decision processes, such as university budgeting (depending on the number of students who will enroll), capital remaining lifetime a fleet systems), needed amount cash reserves for warranty expenses returns), and whether product recall is potentially life-threatening failures). In statistical inference, likelihood ratios have long history u...