Computer evaluation of statistical procedures, and a new quality-control statistical procedure.

1985 ◽  
Vol 31 (2) ◽  
pp. 206-212 ◽  
Author(s):  
A S Blum
Keyword(s):  
Quality Control ◽  
Systematic Error ◽  
Error Rate ◽  
Error Detection ◽  
Statistical Procedure ◽  
True Error ◽  
Computer Evaluation ◽  
Statistical Procedures ◽  
Control Signals ◽  
False Rejection

Abstract I describe a program for definitive comparison of different quality-control statistical procedures. A microcomputer simulates quality-control results generated by repetitive analytical runs. It applies various statistical rules to each result, tabulating rule breaks to evaluate rules as routinely applied by the analyst. The process repeats with increasing amounts of random and systematic error. Rate of false rejection and true error detection for currently popular statistical procedures were comparatively evaluated together with a new multirule procedure described here. The nature of the analyst's response to out-of-control signals was also evaluated. A single-rule protocol that is as effective as the multirule protocol of Westgard et al. (Clin Chem 27:493, 1981) is reported.

A Tool for the Design and Evaluation of Alternative Quality-Control Procedures

1992 ◽  
Vol 38 (2) ◽  
pp. 204-210 ◽  
Author(s):  
Aristides T Hatjimihail
Keyword(s):  
Quality Control ◽  
Systematic Error ◽  
Power Function ◽  
Error Detection ◽  
Simulation Program ◽  
Analytical Process ◽  
Defect Rate ◽  
Control Procedures ◽  
Definition Of ◽  
Design Comparison

Abstract I have developed an interactive microcomputer simulation program for the design, comparison, and evaluation of alternative quality-control (QC) procedures. The program estimates the probabilities for rejection under different conditions of random and systematic error when these procedures are used and plots their power function graphs. It also estimates the probabilities for detection of critical errors, the defect rate, and the test yield. To allow a flexible definition of the QC procedures, it includes an interpreter. Various characteristics of the analytical process and the QC procedure can be user-defined. The program extends the concepts of the probability for error detection and of the power function to describe the results of the introduction of error between runs and within a run. The usefulness of this approach is illustrated with some examples.

scholarly journals Performance evaluation of internal quality control rules, EWMA, CUSUM, and the novel machine learning model

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hikmet Can Çubukçu
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Random Forest ◽  
Systematic Error ◽  
Error Detection ◽  
Average Run Length ◽  
Learning Model ◽  
Internal Quality ◽  
Machine Learning Model ◽  
Outcome Metrics

Abstract Objectives The present study set out to build a machine learning model to incorporate conventional quality control (QC) rules, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) with random forest (RF) algorithm to achieve better performance and to evaluate the performances the models using computer simulation to aid laboratory professionals in QC procedure planning. Methods Conventional QC rules, EWMA, CUSUM, and RF models were implemented on the simulation data using an in-house algorithm. The models’ performances were evaluated on 170,000 simulated QC results using outcome metrics, including the probability of error detection (Ped), probability of false rejection (Pfr), average run length (ARL), and power graph. Results The highest Pfr (0.0404) belonged to the 1–2s rule. The 1–3s rule could not detect errors with a 0.9 Ped up to 4 SD of systematic error. The random forest model had the highest Ped for systematic errors lower than 1 SD. However, ARLs of the model require the combined utility of the RF model with conventional QC rules having lower ARLs or more than one QC measurement is required. Conclusions The RF model presented in this study showed acceptable Ped for most degrees of systematic error. The outcome metrics established in this study will help laboratory professionals planning internal QC.

Performance characteristics of rules for internal quality control: probabilities for false rejection and error detection.

1977 ◽  
Vol 23 (10) ◽  
pp. 1857-1867 ◽  
Author(s):  
J O Westgard ◽  
T Groth ◽  
T Aronsson ◽  
H Falk ◽  
C H de Verdier
Keyword(s):  
Quality Control ◽  
Error Detection ◽  
Control Charts ◽  
Performance Characteristics ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Sum Rule ◽  
Control Rules ◽  
Systematic Shift ◽  
False Rejection

Abstract When assessing the performance of an internal quality control system, it is useful to determine the probability for false rejections (pfr) and the probability for error detection (ped). These performance characteristics are estimated here by use of a computer stimulation procedure. The control rules studied include those commonly employed with Shewhart-type control charts, a cumulative sum rule, and rules applicable when a series of control measurements are treated as a single control observation. The error situations studied include an increase in random error, a systematic shift, a systematic drift, and mixtures of these. The probability for error detection is very dependent on the number of control observations and the choice of control rules. No one rule is best for detecting all errors, thus combinations of rules are desirable. Some appropriate combinations are suggested and their performance characteristics are presented.

Estimating the performance characteristics of quality-control procedures when error persists until detection

1991 ◽  
Vol 37 (10) ◽  
pp. 1720-1724 ◽  
Author(s):  
C A Parvin
Keyword(s):  
Quality Control ◽  
Error Detection ◽  
Rejection Rate ◽  
Performance Characteristics ◽  
Simulation Methods ◽  
Control Rule ◽  
Control Rules ◽  
Control Procedures ◽  
Error Detection Rate ◽  
False Rejection

Abstract The concepts of the power function for a quality-control rule, the error detection rate, and the false rejection rate were major advances in evaluating the performance characteristics of quality-control procedures. Most early articles published in this area evaluated the performance characteristics of quality-control rules with the assumption that an intermittent error condition occurred only within the current run, as opposed to a persistent error that continued until detection. Difficulties occur when current simulation methods are applied to the persistent error case. Here, I examine these difficulties and propose an alternative method that handles persistent error conditions effectively when evaluating and quantifying the performance characteristics of a quality-control rule.

scholarly journals Controversies in modern evolutionary biology: the imperative for error detection and quality control

BMC Genomics ◽  
2012 ◽  
Vol 13 (1) ◽  
pp. 5 ◽  
Author(s):  
Francisco Prosdocimi ◽  
Benjamin Linard ◽  
Pierre Pontarotti ◽  
Olivier Poch ◽  
Julie D Thompson
Keyword(s):  
Quality Control ◽  
Error Detection ◽  
Evolutionary Biology

An English pronunciation learning system for Japanese students based on diagnosis of critical pronunciation errors

ReCALL ◽  
2004 ◽  
Vol 16 (1) ◽  
pp. 173-188 ◽  
Author(s):  
YASUSHI TSUBOTA ◽  
MASATAKE DANTSUJI ◽  
TATSUYA KAWAHARA
Keyword(s):  
Error Rate ◽  
Error Detection ◽  
Native Speakers ◽  
Error Rates ◽  
Learning System ◽  
Second Phase ◽  
Acoustic Model ◽  
Japanese Students ◽  
Speech Data ◽  
English Pronunciation

We have developed an English pronunciation learning system which estimates the intelligibility of Japanese learners' speech and ranks their errors from the viewpoint of improving their intelligibility to native speakers. Error diagnosis is particularly important in self-study since students tend to spend time on aspects of pronunciation that do not noticeably affect intelligibility. As a preliminary experiment, the speech of seven Japanese students was scored from 1 (hardly intelligible) to 5 (perfectly intelligible) by a linguistic expert. We also computed their error rates for each skill. We found that each intelligibility level is characterized by its distribution of error rates. Thus, we modeled each intelligibility level in accordance with its error rate. Error priority was calculated by comparing students' error rate distributions with that of the corresponding model for each intelligibility level. As non-native speech is acoustically broader than the speech of native speakers, we developed an acoustic model to perform automatic error detection using speech data obtained from Japanese students. As for supra-segmental error detection, we categorized errors frequently made by Japanese students and developed a separate acoustic model for that type of error detection. Pronunciation learning using this system involves two phases. In the first phase, students experience virtual conversation through video clips. They receive an error profile based on pronunciation errors detected during the conversation. Using the profile, students are able to grasp characteristic tendencies in their pronunciation errors which in effect lower their intelligibility. In the second phase, students practise correcting their individual errors using words and short phrases. They then receive information regarding the errors detected during this round of practice and instructions for correcting the errors. We have begun using this system in a CALL class at Kyoto University. We have evaluated system performance through the use of questionnaires and analysis of speech data logged in the server, and will present our findings in this paper.

scholarly journals Fuzzy Based Adaptive Controller for on - Chip Communication

2018 ◽  
Vol 7 (3.27) ◽  
pp. 362
Author(s):  
M Jasmin ◽  
T Vigneswaran
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Error Detection ◽  
Input Data ◽  
Fuzzy Controller ◽  
Proper Time ◽  
System On Chip ◽  
Coding Scheme ◽  
Error Detection And Correction ◽  
On Chip

Occurrence of bit error is more when communication takes place in System on chip environment. By employing proper error detection and correction codes the bit error rate can be considerably reduced in On-chip communication. As System on chip involves heterogeneous system the efficiency of communication is improved when reconfigurable multiple coding schemes are preferred. Depending upon the requirements for various subsystem the correct code has to be selected. Due to the variations in input demands based on various subsystems the proper selection of codes become fuzzy in nature. In this paper Fuzzy Controller is designed to select the correct coding scheme. Inputs are given to the fuzzy controller based on the application demand of the user. The input parameters are minimum bit error rate, computational complexity and correlation level of the input data. Fuzzy Controller employs three membership functions and 27 rules to select the appropriate coding scheme. The selected coding scheme should be communicated at the proper time to the decoder. To enable the decoding process selected coding scheme is communicated effectively by using less overhead frame format. To verify the functionality of fuzzy controller random input data sets are used for testing.  

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