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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

CUSUM Chart for Correlated Control Variables

Böhm, Walter, Hackl, Peter January 1991 (has links) (PDF)
The cumulative sum (CUSUM) technique is well-established in theory and practice of process control. A comprehensive exposition of the method is given, e.g., by Wetherill and Brown (1991). A question that is seldom treated in the literature is that on the effect of serial correlation of the control variable. Johnson and Bagshaw (1974) investigate the effect of correlation on the run length distribution when the control variable follows a first order autoregressive or moving average process. They also give an approximate expression for the average run length of the CUSUM- technique for correlated control variables. In this paper we derive an exact expression for the average run length of a discretized CUSUM-technique, i.e., a technique that uses a scoring system for the observations of the control variable. The scoring system is that suggested by Munford (1980). Our results are derived for a control variable that is assumed to follow a first order autoregressive process and with normally distributed disturbances. After deriving in Section 2 the expression for the average run length we discuss its dependence on the process parameter and give a numerical illustration. In Section 3 we discuss corrections for the CUSUM-technique in order to keep the nominal risk for an out-of-control decision and compare our results with those given by Johnson and Bagshaw (1974). (author's abstract) / Series: Forschungsberichte / Institut für Statistik
2

p values and alternative boundaries for CUSUM tests

Zeileis, Achim January 2000 (has links) (PDF)
Firstly rather accurate approximations to the p value functions of the common Standard CUSUM test and the OLS-based CUSUM test for structural change are derived. Secondly alternative boundaries for both tests are suggested and their properties are examined by simulation of expected p values. It turns out that the power of the OLS-based CUSUM test for early and late structural changes can be improved, whereas this weakness of the Standard CUSUM test cannot be repaired by the new boundaries. / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
3

Applications of stochastic analysis to sequential CUSUM procedures

23 February 2010 (has links)
Ph.D.
4

Monitoramento de séries de contagem por meio de gráficos de controle / Monitoring time series of counts using control charts..

Esparza Albarracin, Orlando Yesid 10 March 2014 (has links)
Na área da saúde, várias abordagens nos últimos anos têm sido propostas baseadas nos gráficos de controle CUSUM para a detecção de epidemias infecciosas em que a caraterística a ser monitorada é uma série temporal de dados de contagem, como o número de internações. Neste trabalho foram implementados os modelos lineares generalizados (MLG) no monitoramento, por meio dos gráficos CUSUM e Shewhart, da série do número diário de internações por causas respiratórias para pessoas com 65 anos ou mais residentes no município de São Paulo. Por meio de simulações, avaliaram-se a eficiência de cinco estatísticas diferentes para detectar mudanças na média em séries de contagem. Uma das abordagens consistiu na implementação de três transformações normalizadoras simples que dependem unicamente dos parâmetros das distribuições Poisson e binomial negativa: a transformação Rossi para dados com distribuição Poisson, a transformação Jorgensen para dados com distribuição binomial negativa e os sesíduos de Anscombe para modelos lineares generalizados. As duas últimas estatísticas já foram propostas como gráficos CUSUM: o Método Rogerson e Yamada (2004) é apresentado para dados com distribuição Poisson e neste trabalho foi proposto um novo parâmetro kt para dados binomial negativa; já o método proposto por Hohle (2007) é baseado na função de verossimilhança da distribuição binomial negativa. Utilizando limites de controle para obter um valor ARL0 = 500 sob normalidade, monitorou-se via simulação a série de interesse, implementando as transformações normalizadoras. Entretanto, utilizando-se esses limiares observa-se um maior número de alarmes falsos para as três estatísticas. Modificando o parâmetro k do gráfico CUSUM permitindo que variasse ao longo do tempo a série foi monitorada e foram obtidos valores ARL0 próximos a 500. Os gráficos CUSUM baseados no método Rogerson e Yamada e na estatística da razão de verossimilhanças para dados com distribuição binomial negativa mostraram, via simulação, bons resultados para detectar mudanças na média. As suposições de normalidade e independência das estatísticas normalizadoras, em geral omitidas em trabalhos publicados na literatura, foram avaliadas e comprova-se que as transformações não normalizam os dados, porém são independentes e estacionárias. Analisando os dados reais, as estatísticas apresentaram autocorrelação significativa no lag 7. Devido à persistência desta autocorrelação, foi proposta uma abordagem baseada no ajuste do modelo GARMA. / In public health several approaches have been proposed for the detection of outbreaks of infectious diseases where the characteristic being monitored is a time series of count data as the number of hospitalizations, where the population and the expected rate of admissions change over time. In this work we fitted generalized linear models (GLM) and implemented Shewhart and CUSUM control charts for monitoring the daily number of hospital admissions due to respiratory diseases for people aged 65 and older in the city of São Paulo. Through simulations, we evaluated the efficiency of implementing five different statistical for detecting changes in time series of count. One approach consisted of applying three transformations that only depend on the parameters of the negative binomial and Poisson distributions: The transformations of Rossi for data with Poisson distribution, the transformation proposed by Jorgensen for data with negative binomial distribution and residuals proposed by Anscombe for generalized linear models. The other statistics have been proposed as CUSUM charts: the method of Rogerson e Yamada (2004) was presented for data with Poisson distribution, in this work we proposed a new parameter kt for negative binomial distribution, the proposed method for Hohle (2007) uses the likelihood ratio statistic. Implementing limit control assuming normality for a value of ARL0 = 500 be monitored via simulation the serie of interest implementing the normalizing statistics. However, using these limits was observed a greater number of alarms for the three transformations. Modifying the parameter k of the CUSUM chart to this change over time the series was monitored and were obtained values of ARL0 close to 500. The CUSUM control charts for the methods of Rogerson and Yamada and Holhe for data with negative binomial distribution showed, by simulation, good results for detecting changes in the mean. For negative binomial distribution generalizing the method of Rogerson e Yamada (2004) and implemented the CUSUM charts using the likelihood ratio statistic. Both methods provided good results via simulation to detect small changes in average. The evaluation of assumptions of normality for the statistics proposed by Rossi, Jorgensen and Anscombe generally is omitted in published studies. In this work, we evaluated this assumptions indicating that the statistics are not normal using the real dataset but are independent and stationary. By analyzing real data, due to the persistence of correlation for the normalized statistics, an approach based on setting GARMA model was proposed. This method showed good results once the residuals of the fitted model were normal and independent. Due to the persistence of correlation for the normalized statistics, an approach based on setting GARMA model was proposed. This method showed good results once the residuals of the fitted model were normal and independent.
5

Monitoramento de séries de contagem por meio de gráficos de controle / Monitoring time series of counts using control charts..

Orlando Yesid Esparza Albarracin 10 March 2014 (has links)
Na área da saúde, várias abordagens nos últimos anos têm sido propostas baseadas nos gráficos de controle CUSUM para a detecção de epidemias infecciosas em que a caraterística a ser monitorada é uma série temporal de dados de contagem, como o número de internações. Neste trabalho foram implementados os modelos lineares generalizados (MLG) no monitoramento, por meio dos gráficos CUSUM e Shewhart, da série do número diário de internações por causas respiratórias para pessoas com 65 anos ou mais residentes no município de São Paulo. Por meio de simulações, avaliaram-se a eficiência de cinco estatísticas diferentes para detectar mudanças na média em séries de contagem. Uma das abordagens consistiu na implementação de três transformações normalizadoras simples que dependem unicamente dos parâmetros das distribuições Poisson e binomial negativa: a transformação Rossi para dados com distribuição Poisson, a transformação Jorgensen para dados com distribuição binomial negativa e os sesíduos de Anscombe para modelos lineares generalizados. As duas últimas estatísticas já foram propostas como gráficos CUSUM: o Método Rogerson e Yamada (2004) é apresentado para dados com distribuição Poisson e neste trabalho foi proposto um novo parâmetro kt para dados binomial negativa; já o método proposto por Hohle (2007) é baseado na função de verossimilhança da distribuição binomial negativa. Utilizando limites de controle para obter um valor ARL0 = 500 sob normalidade, monitorou-se via simulação a série de interesse, implementando as transformações normalizadoras. Entretanto, utilizando-se esses limiares observa-se um maior número de alarmes falsos para as três estatísticas. Modificando o parâmetro k do gráfico CUSUM permitindo que variasse ao longo do tempo a série foi monitorada e foram obtidos valores ARL0 próximos a 500. Os gráficos CUSUM baseados no método Rogerson e Yamada e na estatística da razão de verossimilhanças para dados com distribuição binomial negativa mostraram, via simulação, bons resultados para detectar mudanças na média. As suposições de normalidade e independência das estatísticas normalizadoras, em geral omitidas em trabalhos publicados na literatura, foram avaliadas e comprova-se que as transformações não normalizam os dados, porém são independentes e estacionárias. Analisando os dados reais, as estatísticas apresentaram autocorrelação significativa no lag 7. Devido à persistência desta autocorrelação, foi proposta uma abordagem baseada no ajuste do modelo GARMA. / In public health several approaches have been proposed for the detection of outbreaks of infectious diseases where the characteristic being monitored is a time series of count data as the number of hospitalizations, where the population and the expected rate of admissions change over time. In this work we fitted generalized linear models (GLM) and implemented Shewhart and CUSUM control charts for monitoring the daily number of hospital admissions due to respiratory diseases for people aged 65 and older in the city of São Paulo. Through simulations, we evaluated the efficiency of implementing five different statistical for detecting changes in time series of count. One approach consisted of applying three transformations that only depend on the parameters of the negative binomial and Poisson distributions: The transformations of Rossi for data with Poisson distribution, the transformation proposed by Jorgensen for data with negative binomial distribution and residuals proposed by Anscombe for generalized linear models. The other statistics have been proposed as CUSUM charts: the method of Rogerson e Yamada (2004) was presented for data with Poisson distribution, in this work we proposed a new parameter kt for negative binomial distribution, the proposed method for Hohle (2007) uses the likelihood ratio statistic. Implementing limit control assuming normality for a value of ARL0 = 500 be monitored via simulation the serie of interest implementing the normalizing statistics. However, using these limits was observed a greater number of alarms for the three transformations. Modifying the parameter k of the CUSUM chart to this change over time the series was monitored and were obtained values of ARL0 close to 500. The CUSUM control charts for the methods of Rogerson and Yamada and Holhe for data with negative binomial distribution showed, by simulation, good results for detecting changes in the mean. For negative binomial distribution generalizing the method of Rogerson e Yamada (2004) and implemented the CUSUM charts using the likelihood ratio statistic. Both methods provided good results via simulation to detect small changes in average. The evaluation of assumptions of normality for the statistics proposed by Rossi, Jorgensen and Anscombe generally is omitted in published studies. In this work, we evaluated this assumptions indicating that the statistics are not normal using the real dataset but are independent and stationary. By analyzing real data, due to the persistence of correlation for the normalized statistics, an approach based on setting GARMA model was proposed. This method showed good results once the residuals of the fitted model were normal and independent. Due to the persistence of correlation for the normalized statistics, an approach based on setting GARMA model was proposed. This method showed good results once the residuals of the fitted model were normal and independent.
6

Power Study on Testing Epidemic Alternatives

Li, Zihao 29 March 2013 (has links)
Detecting change points in epidemic models has been studied by many scholars. Yao (1993) summarized five existing test statistics in the literature. Out of those test statistics, it was observed that the likelihood ratio statistic showed its standout power. However, all of the existing test statistics are based on an assumption that population variance is known, which is an unrealistic assumption in practice. To avoid assuming known population variance, a new test statistic for detecting epidemic models is studied in this thesis. The new test statistic is a parameter-free test statistic which is more powerful compared to the existing test statistics. Different sample sizes and lengths of epidemic durations are used for the power comparison purpose. Monte Carlo simulation is used to find the critical values of the new test statistic and to perform the power comparison. Based on the Monte Carlo simulation result, it can be concluded that the sample size and the length of the duration have some effect on the power of the tests. It can also be observed that the new test statistic studied in this thesis has higher power than the existing test statistics do in all of cases.
7

Detektion av vätgasläckor med CUSUM-algoritmen

Lundell, David January 2009 (has links)
<p>Detektion av gasläckor används i en mängd olika applikationer som till exempel kvalitetskontroll av kylskåp, lokalisering av skador på kablar och lokalisering av bränsleläckor i bränsletankar.</p><p>Denna rapport undersöker förbättring av detektionsalgoritmen i en existerande vätgasdetektor.</p><p>CUSUM algoritmen är en enkel men kraftfull metod för att snabbt detektera små ändringar i nivån av en signal. Denna metod är anpassad till det dynamiska beteendet i sensorn som används för att spåra vätgasläckorna och även utvärderad på omfattande mätningar utförda på läckor med kända storlekar. Resultaten visar att användning av den föreslagna detektionsalgoritmen innebär en betydande ökning av detektorns prestanda. Särskilt bra blir resultatet på små läckor.</p>
8

Detektion av vätgasläckor med CUSUM-algoritmen

Lundell, David January 2009 (has links)
Detektion av gasläckor används i en mängd olika applikationer som till exempel kvalitetskontroll av kylskåp, lokalisering av skador på kablar och lokalisering av bränsleläckor i bränsletankar. Denna rapport undersöker förbättring av detektionsalgoritmen i en existerande vätgasdetektor. CUSUM algoritmen är en enkel men kraftfull metod för att snabbt detektera små ändringar i nivån av en signal. Denna metod är anpassad till det dynamiska beteendet i sensorn som används för att spåra vätgasläckorna och även utvärderad på omfattande mätningar utförda på läckor med kända storlekar. Resultaten visar att användning av den föreslagna detektionsalgoritmen innebär en betydande ökning av detektorns prestanda. Särskilt bra blir resultatet på små läckor.
9

Generalized cumulative sum control charts

McCulloh, Ian. Pignatiello, Joseph J., January 2004 (has links)
Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Joseph J. Pignatiello, Jr., Florida State University, College of Engineering, Department of Industrial Engineering. Title and description from dissertation home page (viewed June 17, 2004). Includes bibliographical references.
10

Analysis and design of one- and two-sided CUSUM charts with known and estimated parameters

Dunbar, Martin Xavier. January 2007 (has links) (PDF)
Thesis (M.S.)--Georgia Southern University, 2007. / "A thesis submitted to the Graduate Faculty of Georgia Southern University in partial fulfillment of the requirements for the degree Master of Science." Under the direction of Charles W. Champ. ETD. Electronic version approved: May 2007. Includes appendices.

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