The Improved Estimation of σ in Quality Control, Revisited

1997 ◽  
Vol 11 (1) ◽  
pp. 37-42
Author(s):  
John E. Angus
Keyword(s):  
Quality Control ◽  
Unbiased Estimator ◽  
Mean Squared Error ◽  
Minimum Variance ◽  
Underlying Distribution ◽  
Minimum Variance Unbiased Estimator ◽  
Usual Estimator ◽  
Squared Error ◽  
Standard Quality ◽  
The Mean

Recently, Derman and Ross (1995, An improved estimator of a in quality control, Probability in the Engineering and Informational Sciences 9: 411–415) derived an estimator of the standard deviation in the standard quality control model and showed that it had smaller mean squared error than the usual estimator. The new estimator was also shown to be consistent even when the underlying distribution deviates from normality, unlike the usual estimator. In this note, the mean squared error is further improved via shrinkage of the Derman-Ross estimator, and a consistent minimum variance unbiased estimator is presented. Finally, by making use of additional subgroup statistics, a minimum variance unbiased estimator is derived and further improved via shrinkage.

scholarly journals Comparison of Some of Estimation methods of Stress-Strength Model: R = P(Y < X < Z)

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 1103
Author(s):  
Sairan Hamza Raheem ◽  
Bayda Atiya Kalaf ◽  
Abbas Najim Salman
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Moment Method ◽  
Mean Squared Error ◽  
Rayleigh Distribution ◽  
Minimum Variance ◽  
Estimation Methods ◽  
Strength Model ◽  
Minimum Variance Unbiased Estimator ◽  
Squared Error

In this study, the stress-strength model R = P(Y < X < Z)  is discussed as an important parts of reliability system by assuming that the random variables follow Invers Rayleigh Distribution. Some traditional estimation methods are used    to estimate the parameters  namely; Maximum Likelihood, Moment method, and Uniformly Minimum Variance Unbiased estimator and Shrinkage estimator using three types of shrinkage weight factors. As well as, Monte Carlo simulation are used to compare the estimation methods based on mean squared error criteria.  

Estimating the Parameter of a Selected Population

1995 ◽  
Vol 45 (1-2) ◽  
pp. 93-102 ◽  
Author(s):  
Tapan Kumar Nayak
Keyword(s):  
Unbiased Estimator ◽  
Selection Rule ◽  
Random Quantity ◽  
Mean Squared Error ◽  
Minimum Variance ◽  
Estimation Methods ◽  
Unknown Parameters ◽  
Sufficient Statistic ◽  
Minimum Mean Squared Error ◽  
Squared Error

Suppose independent samples from k populations with unknown parameters are taken and one of the populations is selected based on tho data and a prespecified rule. The problem is to estimate the parameter of the selected population. The estimand, G, is a random quantity which depends on both the data and the unknown parameters. While standard estimation methods are inadequate for estimating G, they can be used to estimate the expected value of G. It is shown that the uniformly minimum variance unbiased estimator of E( G) is also the uniformly minimum mean squared error unbiased estimator of G, if the selection rule depends on the data only through a complete sufficient statistic. An approach based on conditional unbiasedness is also discussed.

The Biases and Mean Squared Errors of Estimators of Multinormal Squared Multiple Correlation

1984 ◽  
Vol 9 (3) ◽  
pp. 183-192 ◽  
Author(s):  
Joseph F. Lucke ◽  
Susan Embretson Whitely
Keyword(s):  
Mean Squared Error ◽  
Minimum Variance ◽  
Multiple Correlation Coefficient ◽  
Multiple Correlation ◽  
Minimum Variance Unbiased Estimator ◽  
Mean Squared Errors ◽  
Quadratic Estimator ◽  
Squared Error ◽  
Absolute Bias ◽  
Made In

The sample squared multiple correlation coefficient, R2, is known to have certain unsatisfactory properties as an estimator of the population squared multiple correlation. Hence, numerous adjusted estimators based on functions of 1 – R2 have been proposed. We examine the biases and mean squared errors of five adjusted estimators as well as R2. General results are given for estimators linear in 1 – R2, and four such estimators are examined in detail. In addition, a quadratic estimator and the minimum variance unbiased estimator are examined. Comparisons among these estimators are made in terms of absolute bias and mean squared error.

scholarly journals Estimation of the Shape Parameter of a Wear-Out Failure Period for a Three-Parameter Weibull Distribution in a Small Sample

2020 ◽  
Vol 9 (6) ◽  
pp. 39
Author(s):  
Toru Ogura ◽  
Takatoshi Sugiyama ◽  
Nariaki Sugiura
Keyword(s):  
Monte Carlo ◽  
Weibull Distribution ◽  
Sample Size ◽  
Unbiased Estimator ◽  
Shape Parameter ◽  
Mean Squared Error ◽  
Minimum Variance ◽  
Small Sample ◽  
Scale Parameters ◽  
Squared Error

We propose a method to estimate a shape parameter for a three-parameter Weibull distribution. The proposed method first derives an unbiased estimator for the shape parameter independent of the location and scale parameters and then estimates the shape parameter using a minimum-variance linear unbiased estimator. Since the proposed method is expressed using a hyperparameter, its optimal hyperparameter is searched using Monte Carlo simulations. The recommended hyperparameter used for estimating the shape parameter depends on the sample size, and this causes no problems since the sample size is known when data is obtained. The proposed method is evaluated using a bias and a root mean squared error, and the results are very promising when the population shape parameter is 2 or more in the Weibull distribution representing the wear-out failure period. A numerical dataset is analyzed to demonstrate the practical use of the proposed method.

The Mean Squared Error of the Instrumental Variables Estimator When the Disturbance Has an Elliptical Distribution

2006 ◽  
Vol 25 (1) ◽  
pp. 117-138 ◽  
Author(s):  
Fernanda P. M. Peixe ◽  
Alastair R. Hall ◽  
Kostas Kyriakoulis
Keyword(s):  
Instrumental Variables ◽  
Mean Squared Error ◽  
Elliptical Distribution ◽  
Squared Error ◽  
The Mean

Computing trade-offs in robust design: Perspectives of the mean squared error

2011 ◽  
Vol 60 (2) ◽  
pp. 248-255 ◽  
Author(s):  
Sangmun Shin ◽  
Funda Samanlioglu ◽  
Byung Rae Cho ◽  
Margaret M. Wiecek
Keyword(s):  
Robust Design ◽  
Mean Squared Error ◽  
Squared Error ◽  
Trade Offs ◽  
The Mean

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

scholarly journals On double stage minimax-shrinkage estimator for generalized Rayleigh model

2016 ◽  
Vol 5 (1) ◽  
pp. 39 ◽  
Author(s):  
Abbas Najim Salman ◽  
Maymona Ameen
Keyword(s):  
Sample Size ◽  
Shape Parameter ◽  
Mean Squared Error ◽  
Scale Parameter ◽  
Rayleigh Distribution ◽  
Shrinkage Estimator ◽  
Squared Error ◽  
Expected Sample Size ◽  
Generalized Rayleigh Distribution ◽  
The Mean

<p>This paper is concerned with minimax shrinkage estimator using double stage shrinkage technique for lowering the mean squared error, intended for estimate the shape parameter (a) of Generalized Rayleigh distribution in a region (R) around available prior knowledge (a<sub>0</sub>) about the actual value (a) as initial estimate in case when the scale parameter (l) is known .</p><p>In situation where the experimentations are time consuming or very costly, a double stage procedure can be used to reduce the expected sample size needed to obtain the estimator.</p><p>The proposed estimator is shown to have smaller mean squared error for certain choice of the shrinkage weight factor y(<strong>×</strong>) and suitable region R.</p><p>Expressions for Bias, Mean squared error (MSE), Expected sample size [E (n/a, R)], Expected sample size proportion [E(n/a,R)/n], probability for avoiding the second sample and percentage of overall sample saved  for the proposed estimator are derived.</p><p>Numerical results and conclusions for the expressions mentioned above were displayed when the consider estimator are testimator of level of significanceD.</p><p>Comparisons with the minimax estimator and with the most recent studies were made to shown the effectiveness of the proposed estimator.</p>

scholarly journals Performance Analysis of AOA-Based Localization Using the LS Approach: Explicit Expression of Mean-Squared Error

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Byung-Kwon Son ◽  
Do-Jin An ◽  
Joon-Ho Lee
Keyword(s):  
Explicit Expression ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Squared Error ◽  
Distance Weighted ◽  
The Mean ◽  
Passive Localization ◽  
Location Estimate

In this paper, a passive localization of the emitter using noisy angle-of-arrival (AOA) measurements, called Brown DWLS (Distance Weighted Least Squares) algorithm, is considered. The accuracy of AOA-based localization is quantified by the mean-squared error. Various estimates of the AOA-localization algorithm have been derived (Doğançay and Hmam, 2008). Explicit expression of the location estimate of the previous study is used to get an analytic expression of the mean-squared error (MSE) of one of the various estimates. To validate the derived expression, we compare the MSE from the Monte Carlo simulation with the analytically derived MSE.

Estimation of critical torque using intermittent isometric maximal voluntary contractions of the quadriceps in humans

2009 ◽  
Vol 106 (3) ◽  
pp. 975-983 ◽  
Author(s):  
Mark Burnley
Keyword(s):  
Laboratory Tests ◽  
Mean Squared Error ◽  
Voluntary Activation ◽  
Peripheral Fatigue ◽  
Contraction Time ◽  
Twitch Interpolation ◽  
Squared Error ◽  
The Mean ◽  
Duration Relationship ◽  
Voluntary Contractions

To determine whether the asymptote of the torque-duration relationship (critical torque) could be estimated from the torque measured at the end of a series of maximal voluntary contractions (MVCs) of the quadriceps, eight healthy men performed eight laboratory tests. Following familiarization, subjects performed two tests in which they were required to perform 60 isometric MVCs over a period of 5 min (3 s contraction, 2 s rest), and five tests involving intermittent isometric contractions at ∼35–60% MVC, each performed to task failure. Critical torque was determined using linear regression of the torque impulse and contraction time during the submaximal tests, and the end-test torque during the MVCs was calculated from the mean of the last six contractions of the test. During the MVCs voluntary torque declined from 263.9 ± 44.6 to 77.8 ± 17.8 N·m. The end-test torque was not different from the critical torque (77.9 ± 15.9 N·m; 95% paired-sample confidence interval, −6.5 to 6.2 N·m). The root mean squared error of the estimation of critical torque from the end-test torque was 7.1 N·m. Twitch interpolation showed that voluntary activation declined from 90.9 ± 6.5% to 66.9 ± 13.1% ( P < 0.001), and the potentiated doublet response declined from 97.7 ± 23.0 to 46.9 ± 6.7 N·m ( P < 0.001) during the MVCs, indicating the development of both central and peripheral fatigue. These data indicate that fatigue during 5 min of intermittent isometric MVCs of the quadriceps leads to an end-test torque that closely approximates the critical torque.

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