Estimating the confidence interval of Weibull modulus is an important problem in fracture strength modeling ceramic and composite materials. It particularly cases where sample size small due to high experimental costs. For this purpose, several classical methods, including popular maximum likelihood method, Bayesian methods have been developed literature. However, studies on inference remained ...

In this paper, we propose new sequential estimation methods based on inclusion principle. The main idea is to reformulate the estimation problems as constructing sequential random intervals and use confidence sequences to control the associated coverage probabilities. In contrast to existing asymptotic sequential methods, our estimation procedures rigorously guarantee the pre-specified levels o...

This paper describes experiments in improving word confidence estimation using documentand task-level features of the hypothesized word sequence from a recognizer. The improved confidence estimates are shown to improve information extraction performance, specifically named entity (NE) recognition. The detected names can then be used to further improve confidence estimation in a multi-pass NE re...

In this paper we report a study for confidence measure estimation in a large vocabulary speaker-independent continuous speech recognition system. A hybrid confidence measure estimation algorithm was developed. The final confidence measure consists of a number of confidence parameters which are generated from the different processing levels of the recognition system. A Parameter Reliability Anal...

We implement a confidence estimation stage for a speech recognition system by using two maximum entropy models. These models takes information from various sources including a set of scores which have proved to be useful in confidence estimation tasks, and efficiently combine those scores. Two different approaches of the maximum entropy model are developed. First a basic model which takes advan...

Error estimation accuracy is the salient issue regarding the validity of a classifier model. When samples are small, training-data-based error estimates tend to suffer from inaccuracy and quantification of error estimation accuracy is difficult. Numerous methods have been proposed for estimating confidence intervals for the true error based on the estimated error. This paper surveys proposed me...