Monitoring multivariate coefficient of variation with upward Shewhart and EWMA charts in the presence of measurement errors using the linear covariate error model

2020 ◽  
Author(s):  
Heba N. Ayyoub ◽  
Michael B. C. Khoo ◽  
Ming Ha Lee ◽  
Abdul Haq
Keyword(s):  
Coefficient Of Variation ◽  
Measurement Errors ◽  
Error Model ◽  
Multivariate Coefficient Of Variation

Multivariate coefficient of variation charts with measurement errors

2020 ◽  
Vol 147 ◽  
pp. 106633 ◽  
Author(s):  
Heba N. Ayyoub ◽  
Michael B.C. Khoo ◽  
Sajal Saha ◽  
Philippe Castagliola
Keyword(s):  
Coefficient Of Variation ◽  
Measurement Errors ◽  
Multivariate Coefficient Of Variation

scholarly journals ON THE UNIFORM CONVERGENCE OF DECONVOLUTION ESTIMATORS FROM REPEATED MEASUREMENTS

2021 ◽  
pp. 1-22
Author(s):  
Daisuke Kurisu ◽  
Taisuke Otsu
Keyword(s):  
Multivariate Analysis ◽  
Measurement Error ◽  
Uniform Convergence ◽  
Measurement Errors ◽  
Convergence Rates ◽  
Free Variable ◽  
Error Model ◽  
Repeated Measurements ◽  
Empirical Characteristic Function ◽  
Measurement Error Model

This paper studies the uniform convergence rates of Li and Vuong’s (1998, Journal of Multivariate Analysis 65, 139–165; hereafter LV) nonparametric deconvolution estimator and its regularized version by Comte and Kappus (2015, Journal of Multivariate Analysis 140, 31–46) for the classical measurement error model, where repeated noisy measurements on the error-free variable of interest are available. In contrast to LV, our assumptions allow unbounded supports for the error-free variable and measurement errors. Compared to Bonhomme and Robin (2010, Review of Economic Studies 77, 491–533) specialized to the measurement error model, our assumptions do not require existence of the moment generating functions of the square and product of repeated measurements. Furthermore, by utilizing a maximal inequality for the multivariate normalized empirical characteristic function process, we derive uniform convergence rates that are faster than the ones derived in these papers under such weaker conditions.

A Control Chart for the Multivariate Coefficient of Variation

2015 ◽  
Vol 32 (3) ◽  
pp. 1213-1225 ◽  
Author(s):  
Wai Chung Yeong ◽  
Michael Boon Chong Khoo ◽  
Wei Lin Teoh ◽  
Philippe Castagliola
Keyword(s):  
Control Chart ◽  
Coefficient Of Variation ◽  
Multivariate Coefficient Of Variation

Improving the Error Model for POS Applicationto Airborne InSAR

2012 ◽  
Vol 239-240 ◽  
pp. 521-529
Author(s):  
Lin Zhou Ting Chen ◽  
Jian Cheng Fang
Keyword(s):  
Measurement Errors ◽  
Constant Error ◽  
Inertial Sensor ◽  
Error Model ◽  
Flight Simulation ◽  
First Order ◽  
Process Error ◽  
Simulation Based ◽  
Orientation System ◽  
Flight Route

Position and Orientation System (POS) is a technology widely used for motional error compensation of airborne InSAR. The measurement errors of inertial sensor (accelerometers and gyros) are the chief influencing factors to the precision of POS. In order to enhance the accuracy of POS, an improved SINS error model should be used in POS. In this paper, a more precise error model for SINS is developed by augmenting random walks error, first-order Markov process error, scale factor error and installation error. To validate the accuracy of the improved error model, semi physical flight simulation based on the imitation of imaging-flight route of InSAR is made to compare with the traditional SINS error model which only considering the random constant error. The simulation results show that the accuracy of the improved SINS error model is one order higher than the traditional SINS error model.

The Method on Acceleration Extraction from Strapdown Airborne Scalar Gravimeter Based on Flight Dynamics

2012 ◽  
Vol 591-593 ◽  
pp. 2152-2156 ◽  
Author(s):  
Shao Kun Cai ◽  
Kai Dong Zhang ◽  
Mei Ping Wu ◽  
Yang Ming Huang
Keyword(s):  
Measurement Errors ◽  
Cutoff Frequency ◽  
Flight Dynamics ◽  
Error Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Combining with the error model of strapdown airborne scalar gravimetry, the paper analyses the natural motions of the aircraft, and then discusses how those natural motions of the aircraft influence the airborne scalar gravimetry. The spectra characteristic of measurement errors of the strapdown airborne scalar gravimetry can be obtained, and its relation with natural motions of the aircraft is demonstrated. As a result, we can determine the cutoff frequency of low-pass filter through the characteristic of the natural motions of the aircraft, the cutoff frequency is very important for acceleration extraction from the strapdown airborne scalar gravimeter.

scholarly journals Importance of modelling heteroscedasticity of survey index data in fishery stock assessments

2014 ◽  
Vol 72 (1) ◽  
pp. 130-136 ◽  
Author(s):  
Saang-Yoon Hyun ◽  
Mark N. Maunder ◽  
Brian J. Rothschild
Keyword(s):  
Coefficient Of Variation ◽  
Likelihood Function ◽  
Error Model ◽  
Fish Stock ◽  
Additional Parameter ◽  
Traditional Practice ◽  
Multiplicative Error ◽  
Additive Error ◽  
Stochastic Error ◽  
Stock Assessments

Abstract Many fish stock assessments use a survey index and assume a stochastic error in the index on which a likelihood function of associated parameters is built and optimized for the parameter estimation. The purpose of this paper is to evaluate the assumption that the standard deviation for the difference in the log-transformed index is approximately equal to the coefficient of variation of the index, and also to examine the homo- and heteroscedasticity of the errors. The traditional practice is to assume a common variance of the index errors over time for estimation convenience. However, if additional information is available about year-to-year variability in the errors, such as year-to-year coefficient of variation, then we suggest that the heteroscedasticity assumption should be considered. We examined five methods with the assumption of a multiplicative error in the survey index and two methods with that of an additive error in the index: M1, homoscedasticity in the multiplicative error model; M2, heteroscedasticity in the multiplicative error model; M3, M2 with approximate weighting and an additional parameter for scaling variance; M4–M5, pragmatic practices; M6, homoscedasticity in the additive error model; M7, heteroscedasticity in the additive error model. M1–M2 and M6–M7 are strictly based on statistical theories, whereas M3–M5 are not. Heteroscedasticity methods M2, M3, and M7 consistently outperformed the other methods. However, we select M2 as the best method. M3 requires one more parameter than M2. M7 has problems arising from the use of the raw scale as opposed to the logarithm transformation. Furthermore, the fitted survey index in M7 can be negative although its domain is positive.

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