Coverage probability

About: Coverage probability is a research topic. Over the lifetime, 2479 publications have been published within this topic receiving 53259 citations.

Comparison of confidence intervals on the among-group variance component for the unbalanced one-way random model

Abstract: The purpose of this paper is to compare four methods for constructing approximate confidence intervals on the among-group variance component for the unbalanced random one-way model. Monte-Carlo simulation is used to compare the estimated coverage probabilities of these intervals. The comparison is based on a method for generating designs having specified degrees of imbalance. Two of these methods, namely, the one by Thomas and Hultquist [5] and the modified harmonic mean method, appear to be the best in most cases considered.

The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction

Abstract: Machine learning algorithms have been effectively applied into various real world tasks. However, it is difficult to provide high-quality machine learning solutions to accommodate an unknown distribution of input datasets; this difficulty is called the uncertainty prediction problems. In this paper, a margin-based Pareto deep ensemble pruning (MBPEP) model is proposed. It achieves the high-quality uncertainty estimation with a small value of the prediction interval width (MPIW) and a high confidence of prediction interval coverage probability (PICP) by using deep ensemble networks. In addition to these networks, unique loss functions are proposed, and these functions make the sub-learners available for standard gradient descent learning. Furthermore, the margin criterion fine-tuning-based Pareto pruning method is introduced to optimize the ensembles. Several experiments including predicting uncertainties of classification and regression are conducted to analyze the performance of MBPEP. The experimental results show that MBPEP achieves a small interval width and a low learning error with an optimal number of ensembles. For the real-world problems, MBPEP performs well on input datasets with unknown distributions datasets incomings and improves learning performance on a multi task problem when compared to that of each single model.

Empirical likelihood confidence intervals for the endpoint of a distribution function

Abstract: Estimating the endpoint of a distribution function is of interest in product analysis and predicting the maximum lifetime of an item. In this paper, we propose an empirical likelihood method to construct a confidence interval for the endpoint. A simulation study shows the proposed confidence interval has better coverage accuracy than the normal approximation method, and bootstrap calibration improves the accuracy.

Calibrated nonparametric confidence sets

Abstract: The construction of confidence sets when multivariate normality holds and in the general case where the usual spherical or elliptical structures may not occur is investigated. Calibration is used to correct the coverage probability of the nonparametric sets, and an example involving parameters from a chemical kinetics model in a biological system is used to demonstrate the techniques. Monte Carlo simulations validate the approach.