There are many methods for constructing prediction intervals for differences in observations. This document will explain several of these methods in the context of different models using data from a kiwi yield experiment as an example. 1. KIWI DATA In an experiment (discussed in more detail in class), kiwi yields were measured in a designed experiment. There were three blocks (north, east, and ...

This paper presents simple large sample prediction intervals for a future response Yf given a vector xf of predictors when the regression model has the form Yi = m(xi) + ei where m is a function of xi and the errors ei are iid. Intervals with correct asymptotic coverage and shortest asymptotic length can be made by applying the shorth estimator to the residuals. Since residuals underestimate th...

In this work we consider the task of constructing prediction intervals in an inductive batch setting. We present a discriminative learning framework which optimizes the expected error rate under a budget constraint on the interval sizes. Most current methods for constructing prediction intervals offer guarantees for a single new test point. Applying these methods to multiple test points results...

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...

We propose a new method to compute prediction intervals. Especially for small data sets the width of a prediction interval does not only depend on the variance of the target distribution, but also on the accuracy of our estimator of the mean of the target, i.e., on the width of the confidence interval. The confidence interval follows from the variation in an ensemble of neural networks, each of...

in order to perform preventive maintenance (pm), two approaches have evolved in the literature. the traditional approach is based on the use of statistical and reliability analysis of equipment failure. under statistical-reliability (s-r)-based pm, the objective of achieving the minimum total cost is pursued by establishing fixed pm intervals, which are statistically optimal, at which to replac...

Dynamic Bayesian network is the extension of Bayesian network in solving time series problems .It can be well dealt with the time-varying multivariable problem. A state model is given based on Dynamic Bayesian network. The model can more accurately describe the relationship between the system state and the influencing factors. Single-step and multi-step prediction algorithms are given to predic...