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Robust estimators of process capabil...

Hsu, Bi-Min.

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Robust estimators of process capability indices using smooth adaptive estimator.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Robust estimators of process capability indices using smooth adaptive estimator./
Author:	Hsu, Bi-Min.
Description:	134 p.
Notes:	Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 2011.
Contained By:	Dissertation Abstracts International63-04B
Subject:	Engineering, Industrial -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3048684
ISBN:	0493629610

Robust estimators of process capability indices using smooth adaptive estimator.
Hsu, Bi-Min.

Robust estimators of process capability indices using smooth adaptive estimator. - 134 p.

Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 2011.

Thesis (Ph.D.)--The University of Texas at Arlington, 2002.

Process capability indices <italic>Cp, Cpk, C pm,</italic> and <italic>Cpmk</italic> have been used in manufacturing industries to assess a quantitative measure of process potential and performance. Many studies have pointed out that the normally-based process capability indices (PCIs) are very sensitive to non-normal process. Also these capability indices are calculated using the process mean μ and standard deviation σ, which are almost always unknown and conventionally replaced with the sample mean <italic>X¯</italic> and standard deviation S. Since it is well known that the distribution of <italic>X¯</italic> is not stable to non-normality when sample size n is small, say n < 30, and S is not reliable for non-normality, none of the estimates of process capability indices are robust to non-normality.

ISBN: 0493629610Subjects--Topical Terms:

1260336
Engineering, Industrial

Robust estimators of process capability indices using smooth adaptive estimator.
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020 $a 0493629610
035 $a (UnM)AAI3048684
035 $a AAI3048684
040 $a UnM $c UnM
100 1 $a Hsu, Bi-Min. $3 1260400
245 1 0 $a Robust estimators of process capability indices using smooth adaptive estimator.
300 $a 134 p.
500 $a Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 2011.
500 $a Supervisors: Chien-Pai Han; Herbert W. Corley.
502 $a Thesis (Ph.D.)--The University of Texas at Arlington, 2002.
520 $a Process capability indices <italic>Cp, Cpk, C pm,</italic> and <italic>Cpmk</italic> have been used in manufacturing industries to assess a quantitative measure of process potential and performance. Many studies have pointed out that the normally-based process capability indices (PCIs) are very sensitive to non-normal process. Also these capability indices are calculated using the process mean μ and standard deviation σ, which are almost always unknown and conventionally replaced with the sample mean <italic>X¯</italic> and standard deviation S. Since it is well known that the distribution of <italic>X¯</italic> is not stable to non-normality when sample size n is small, say n < 30, and S is not reliable for non-normality, none of the estimates of process capability indices are robust to non-normality.
520 $a For the above reasons, this research uses the smooth adaptive estimator, SA, proposed by Han and Hawkins (1994) to construct new and robust process capability indices, <italic>&Ccirc;p, &Ccirc;pk, &Ccirc; pm,</italic> and <italic>&Ccirc;pmk</italic>. These indices are called smooth adaptive PCIs to improve the measure performance when the processes are non-normally distributed and sample sizes are small. The main idea of this estimator is to set the weight sequence, <italic>wi</italic>, with probability one, constant “in the middle” observations and decreasing “toward and extremes” for observations at either end. The smooth adaptive PCIs will be compared with the normally-based (classical), median, and Clements PCIs in term of bias and mean square error
520 $a To investigate the effect of sample size and non-normal processes for the estimated PCIs, we use the bootstrap method and four bootstrap confidence intervals to make the comparisons in a Monte Carlo study. The sample sizes considered are n = 10, 20, 30, and 50. The distributions used in the simulation are the beta distribution, and some bell-shaped (e.g., student-t) and right-skewed (e.g., Gamma) distributions
590 $a School code: 2502
650 $a Engineering, Industrial $3 1260336
690 $a 054
710 2 $a The University of Texas at Arlington $3 1260401
773 0 $t Dissertation Abstracts International $g 63-04B
790 $a 250
790 1 $a Corley, Herbert W., $e adviso
790 1 $a Han, Chien-Pai, $e adviso
791 $a Ph.D
792 $a 200
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3048684