An evaluation of change-point estimators for a sequence of normal observations with unknown parameters

2017 ◽  
Vol 46 (6) ◽  
pp. 4297-4317
Author(s):  
Jorge Garza-Venegas ◽  
Victor Tercero-Gomez ◽  
Alvaro Cordero Franco ◽  
María Temblador-Pérez ◽  
Mario Beruvides
Keyword(s):  
Change Point ◽  
Unknown Parameters ◽  
Point Estimators

ESTIMATION OF THE CHANGE POINT OF THE PROCESS FRACTION NONCONFORMING IN SPC APPLICATIONS

2005 ◽  
Vol 12 (02) ◽  
pp. 95-110 ◽  
Author(s):  
MARCUS B. PERRY ◽  
JOSEPH J. PIGNATIELLO
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Control Chart ◽  
Change Point ◽  
Control Charts ◽  
Process Change ◽  
Likelihood Estimator ◽  
Ewma Control Chart ◽  
Point Estimator ◽  
Point Estimators

Knowing when a process has changed would simplify the search for and identification of the special cause. In this paper, we compare the maximum likelihood estimator (MLE) of the process change point (that is, when the process changed) to built-in change point estimators from binomial CUSUM and EWMA control charts. We conclude that it is better to use the maximum likelihood change point estimator when a CUSUM or EWMA control chart signals a change in the process fraction nonconforming. The results show that the MLE provides process engineers with an accurate and useful estimate of the last subgroup from the unchanged process.

scholarly journals Confidence Sets for Statistical Classification

Stats ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 332-346 ◽  
Author(s):  
Wei Liu ◽  
Frank Bretz ◽  
Natchalee Srimaneekarn ◽  
Jianan Peng ◽  
Anthony J. Hayter
Keyword(s):  
Confidence Set ◽  
Unknown Parameters ◽  
Confidence Sets ◽  
Single Class ◽  
Wide Range ◽  
Desirable Feature ◽  
Point Estimator ◽  
Point Estimators ◽  
Future Object

Classification has applications in a wide range of fields including medicine, engineering, computer science and social sciences among others. In statistical terms, classification is inference about the unknown parameters, i.e., the true classes of future objects. Hence, various standard statistical approaches can be used, such as point estimators, confidence sets and decision theoretic approaches. For example, a classifier that classifies a future object as belonging to only one of several known classes is a point estimator. The purpose of this paper is to propose a confidence-set-based classifier that classifies a future object into a single class only when there is enough evidence to warrant this, and into several classes otherwise. By allowing classification of an object into possibly more than one class, this classifier guarantees a pre-specified proportion of correct classification among all future objects. An example is provided to illustrate the method, and a simulation study is included to highlight the desirable feature of the method.

Sign in / Sign up

Export Citation Format

Share Document