Literature DB >> 24898520

Evaluating environmental performance using new process capability indices for autocorrelated data.

J N Pan1, C I Li, F Y Chen.   

Abstract

Traditionally, the process capability index is developed by assuming that the process output data are independent and follow normal distribution. However, in most environmental cases, the process data are autocorrelated. The autocorrelated process, if unrecognized as an independent process, can lead to erroneous decision making and unnecessary quality loss. In this paper, three new capability indices with unbiased estimators are proposed to relieve the independence assumption for the-nominal-the-best and the-smaller-the-better cases. Furthermore, we use mean squared error (MSE) and mean absolute percent error (MAPE) to compare the accuracy of our proposed indices to previous autocorrelated indices. The results show that our proposed capability indices outperform the predecessors.

Mesh:

Year:  2014        PMID: 24898520     DOI: 10.1007/s10661-014-3861-z

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  2 in total

1.  Forecasts using Box-Jenkins models for the ambient air quality data of Delhi City.

Authors:  Pragati Sharma; Avinash Chandra; S C Kaushik
Journal:  Environ Monit Assess       Date:  2008-09-26       Impact factor: 2.513

2.  Stochastic approaches for time series forecasting of boron: a case study of Western Turkey.

Authors:  Omer Faruk Durdu
Journal:  Environ Monit Assess       Date:  2009-10-21       Impact factor: 2.513

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.