arXiv subjects

Qinglong Tian

Publications and source records attributed to Qinglong Tian.

21 records · Page 2Linked to original sources

arXiv2021

Prediction of Future Failures for Heterogeneous Reliability Field Data

This article introduces methods for constructing prediction bounds or intervals for the number of future failures from heterogeneous reliability field data. We focus on within-sample prediction where early data from a failure-time process is used to predict future failures from the same process. Early data from high-reliability products, however, often have limited information due to some combination of small sample sizes, censoring, and truncation. In such cases, we use a Bayesian hierarchical model to model jointly multiple lifetime distributions arising from different subpopulations of similar products. By borrowing information across subpopulations, our method enables stable estimation and the computation of corresponding prediction intervals, even in cases where there are few observed failures. Three applications are provided to illustrate this methodology, and a simulation study is used to validate the coverage performance of the prediction intervals.

stat.ME↗

arXiv2020

Predicting the Number of Future Events

This paper describes prediction methods for the number of future events from a population of units associated with an on-going time-to-event process. Examples include the prediction of warranty returns and the prediction of the number of future product failures that could cause serious threats to property or life. Important decisions such as whether a product recall should be mandated are often based on such predictions. Data, generally right-censored (and sometimes left truncated and right-censored), are used to estimate the parameters of a time-to-event distribution. This distribution can then be used to predict the number of events over future periods of time. Such predictions are sometimes called within-sample predictions and differ from other prediction problems considered in most of the prediction literature. This paper shows that the plug-in (also known as estimative or naive) prediction method is not asymptotically correct (i.e., for large amounts of data, the coverage probability always fails to converge to the nominal confidence level). However, a commonly used prediction calibration method is shown to be asymptotically correct for within-sample predictions, and two alternative predictive-distributionbased methods that perform better than the calibration method are presented and justified.

stat.ME↗

arXiv2017

Preservation of quantum Fisher information and geometric phase of a single qubit system in a dissipative reservoir through the addition of qubits

In this paper, we have investigated the preservation of quantum Fisher information of a single-qubit system coupled to a common zero temperature reservoir through the addition of noninteracting qubits. The results show that, the QFI is completely protected in both Markovian and non-Markovian regimes by increasing the number of additional qubits. Besides, the phenomena of QFI display monotonic decay or non-monotonic with revival oscillations depending on the number of additional qubits in a common dissipative reservoir. Moreover, we extend this model to investigate the effect of additional qubits N-1 and the initial conditions of the system on the geometric phase. It is found that, the robustness of GP against the dissipative reservoir has been demonstrated by increasing gradually the number of additional qubits. Besides, the GP is sensitive to the initial parameter theta, and possesses symmetric in a range regime 0,2 pi.

quant-ph↗