Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements

Lucas, James M.; Saccucci, Michael S.; Baxley, Robert V.; Woodall, William H.; Maragh, Hazem D.; Faltin, Fedrick W.; Hahn, Gerald J.; Tucker, William T.; Hunter, J. Stuart; MacGregor, John F.; Harris, Thomas J.

doi:10.2307/1269835

Cited by 570 publications

(287 citation statements)

References 23 publications

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“…For each flight of the two field studies, an exponential moving average (EMA; Lucas and Saccucci, 1990) with a fixed t value (1 Á 501 s in 2 s steps) is applied to the reference RH time series, RH t [see eq. (1)].…”

Section: Temperature-dependent Response Timementioning

confidence: 99%

Quality assessment of MOZAIC and IAGOS capacitive hygrometers: insights from airborne field studies

Neis

Smit²,

Rohs³

et al. 2015

Tellus B: Chemical and Physical Meteorology

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A B S T R A C TIn 2011, the MOZAIC (Measurement of Ozone by AIRBUS In-Service Aircraft) successor programme IAGOS (In-service Aircraft for a Global Observing System) started to equip their long-haul passenger aircraft with the modified capacitive hygrometer Vaisala HUMICAP † of type H. The assurance of the data quality and the consistency of the data set during the transition from MOZAIC Capacitive Hygrometers to IAGOS Capacitive Hygrometers were evaluated within the CIRRUS-III and AIRTOSS-ICE field studies. During these performance tests, the capacitive hygrometers were operated aboard a Learjet 35A aircraft together with a closedcell Lyman-a fluorescence hygrometer, an open-path tunable diode laser (TDL) system and a closed-cell, direct TDL absorption hygrometer for water vapour measurement. For MOZAIC-typical operation conditions, the comparison of relative humidity (RH) data from the capacitive hygrometers and reference instruments yielded remarkably good agreement with an uncertainty of 5% RH. The temperature dependence of the sensor's response time was derived from the cross-correlation of capacitive hygrometer data and smoothed data from the fast-responding reference instruments. The resulting exponential moving average function could explain the major part of the observed deviations between the capacitive hygrometers and the reference instruments.

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Section: Temperature-dependent Response Timementioning

confidence: 99%

Quality assessment of MOZAIC and IAGOS capacitive hygrometers: insights from airborne field studies

Neis

Smit²,

Rohs³

et al. 2015

Tellus B: Chemical and Physical Meteorology

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“…The values of fraction defective p that are most relevant in equation (4) are p p a , and p p r , and the corresponding ARL values might be termed steady-state in-control ARL and steady-state out-of-control ARL. For a discussion of steady-state ARL concepts, one may refer to Taylor (1968), Crosier (1986), and Lucas and Saccucci (1990). There is a further re®nement of the steady-state ARL concept into conditional steady-state ARL and cyclical steady-state ARL, but in our view the di¨erence is minor.…”

Section: The Binomial Cusummentioning

confidence: 94%

“…There is a further re®nement of the steady-state ARL concept into conditional steady-state ARL and cyclical steady-state ARL, but in our view the di¨erence is minor. Here, following the recommendation in Lucas and Saccucci (1990), we consider cyclical steady-state ARL only, and we refer to it simply as steady-state ARL.…”

Section: The Binomial Cusummentioning

confidence: 99%

“…Crosier (1986) has proposed a convenient method for evaluating steady-state ARL, and presents the details of a method for determination of the steady-state distribution of the monitoring statistic (the CUSUM) when the process is incontrol. Following the Lucas and Saccucci (1990) development of this method, we compute a modi®ed form P Ã of the transition matrix in equation (6) as follows:…”

Section: Calculation Of Arl and Ani Valuesmentioning

confidence: 99%

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Sample size and the Binomial CUSUM Control Chart: the case of 100% inspection

Bourke

2001

Metrika

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The Binomial CUSUM is used to monitor the fraction defective p of a repetitive process, particularly for detecting small to moderate shifts. The number of defectives from each sample is used to update the monitoring CUSUM. When 100% inspection is in progress, the question arises as to how many sequential observations should be grouped together in forming successive samples. The tabular form of the CUSUM has three parameters: the sample size n, the reference value k, and the decision interval h, and these parameters are usually chosen using statistical or economic-statistical criteria, which are based on Average Run Length (ARL). Unlike earlier studies, this investigation uses steady-state ARL rather than zero-state ARL, and the occurrence of the shift can be anywhere within a sample. The principal ®nding is that there is a signi®cant gain in the performance of the CUSUM when the sample size n is set at one, and this CUSUM might be termed the Bernoulli CUSUM. The advantage of using n 1 is greater for larger shifts and for smaller values of in-control ARL.

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“…According to Lucas and Saccucci (1990), the EWMA control chart was a good alternative to the Shewhart control chart when the interest is in detecting small shifts. The EWMA is generally used with individual observations; therefore, this control chart will be discussed when n = 1.…”

Section: The Control Charts To Detect Small Shiftsmentioning

confidence: 99%

Vol. 16, No. 2 (Full Issue)

2017

J. Mod. App. Stat. Meth.

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EDITORIAL ASSISTANCEJMASM (ISSN 1538−9472, http://digitalcommons.wayne.edu/jmasm) is an independent, open access electronic journal, published biannually in May and November by JMASM Inc. (PO Box 48023, Oak Park, MI, 48237) in collaboration with the Wayne State University Library System. JMASM seeks to publish (1) new statistical tests or procedures, or the comparison of existing statistical tests or procedures, using computer-intensive Monte Carlo, bootstrap, jackknife, or resampling methods, (2) the study of nonparametric, robust, permutation, exact, and approximate randomization methods, and (3) applications of computer programming, preferably in Fortran (all other programming environments are welcome), related to statistical algorithms, pseudo-random number generators, simulation techniques, and self-contained executable code to carry out new or interesting statistical methods.Journal correspondence (other than manuscript submissions) and requests for advertising may be forwarded to ea@jmasm.com. See back matter for instructions for authors. IntroductionComputer simulation studies represent an important tool for investigating statistical procedures difficult or impossible to study using mathematical theory or real data. Descriptors of these studies vary (e.g., statistical experiment, Monte Carlo simulation, computer experiment), but the examples of Hoaglin and Andrews (1975) and Hauck and Anderson (1984) are followed here with use of the term simulation studies. Extensive descriptions of simulation studies can be found in Lewis and Orav (1989) and Santner, Williams, and Notz (2003). In the behavioral sciences simulation studies have been used to study a wide array of statistical methods (e.g., Cribbie, Fiksenbaum, & Wilcox, 2012; Depaoli, 2012; Enders, Baraldi, & Cham, 2014; Hu & Bentler, 1999; Tomarken & Serlin, 1986). The general goal of these studies is to provide evidence of the behavior of statistical methods under a variety of data conditions that improves statistical practice and informs future statistical research. The goal here is to encourage methodological researchers to treat these studies as statistical sampling EXPERIMENTAL DESIGN AND DATA ANALYSIS IN SIMULATION 4 experiments subject to established principles of experimental design and data analysis.An underappreciated facet of simulation studies in statistics is their role in enhancing the reproducibility of scientific findings. The importance of reproducibility has gained momentum in numerous scientific arenas because of growing evidence that many findings cannot be replicated (Stodden, 2015). Concerns over reproducibility and the role of statistics were captured in Statistics and science: A report of the London workshop on the future of the statistical sciences (2014) which noted: "The reproducibility problem goes far beyond statistics, of course, because it involves the entire reward structure of the scientific enterprise. Nevertheless, statistics is a very important ingredient in both the problem and the remedy." (p. 27) Simulati...

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Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements

Cited by 570 publications

References 23 publications

Quality assessment of MOZAIC and IAGOS capacitive hygrometers: insights from airborne field studies

Quality assessment of MOZAIC and IAGOS capacitive hygrometers: insights from airborne field studies

Sample size and the Binomial CUSUM Control Chart: the case of 100% inspection

Vol. 16, No. 2 (Full Issue)

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