This paper deals with CUSUM tests based on grouped or classified observations. The computation of average run length is reduced to that of solving of a system of simultaneous linear equations. Moreover a corresponding approximation based on the Wald approximations for characteristics of sequential likelihood ratio tests is presented. The effect of grouping is investigated with a CUSUM test for the mean of a normal distribution based on F-optimal grouping schemes. The considered example demonstrates that hight efficient CUSUM tests can be obtained for F-optimal grouping schemes already with a small number of groups.

This paper deals with CUSUM tests for monitoring of intensity parameter of a Poisson process if this process can be observed in a restricted manner only at pregiven equidistant time points. In this case the process can be monitored by means of a CUSUM test for the parameter of a corresponding Poisson distribution. For rational reference parameter values the computation of average run length is reduced to that of solving of a system of simultaneous linear equations. The performance of obtained CUSUM tests is discussed by means of corresponding examples.

This thesis aims to give a comprehensive account of some of the most recent methods of a change point estimation. The literature on the change point estimation shows a variety of approaches to deal with this subject. Among them, tests based on the popular CUSUM process, likelihood ratio tests, wild binary segmentation and some of the most recent techniques on the change point estimation in panel data are all covered by this paper. The case of dependent panels is discussed as well. The practical part of the study is focused on application of the wild binary segmentation method on weekly log-returns of the Dow Jones stock index. Firstly, we fit a GARCH model to the analysed time series. We next use the wild binary segmenatation method to detect structural changes in the mean of the original time series. Next, we apply the same method to the residuals from the GARCH fit. We analyse several penalization criteria proposed by previous studies and evaluate their effects on the estimated number and locations of the change points in the given data set. 1

Many performance measurement algorithms can only evaluate measure active managers' performance after a period of operating time. However, most investors are interested in monitoring the active managers' performances at any time, especially, when the performance is going down. So that the investors can adjust the targets and contents of their portfolios to reduce their risks. Yashchin,Thomas and David (1997) proposed to use a statistical quality control (SQC) procedure to monitor active managers' performances. In particular, they established the IR (Information Ratio) control charts under normality assumption to monitor the dynamic performances of active managers. However, the distributions of IR statistic usually possess fat tail property. Since the underlying distribution of IR is an important hypothesis in building up the control chart, we consider the heavy tail distributions, such as mixture normal and generalized error distribution to fit the IR data. Based on the fitted distribution, the IR control charts are rebuilt. By simulations and empirical studies, the remedial control charts are found to detect the shifts of active managers' performances more sensitively.

Count data are often encountered in manufacturing and service industries due to ease of data collection. These counts can be useful in process monitoring to detect shifts of a process from an in-control state to various out-of-control states. It is usually assumed that the observations are independent and identically distributed. However, in practice, observations may be autocorrelated and this may adversely affect the performance of the control charts developed under the assumption of independence. In this thesis, the cumulative sum (CUSUM) control chart for monitoring autocorrelated processes of counts is investigated. To describe the autocorrelation structure of counts, a Poisson integer-valued autoregressive moving average model of order 1, Poisson INAR(1), is employed. Changes in the process mean in both positive and negative directions are taken into account while designing the CUSUM chart. A trivariate Markov Chain approach is utilized for evaluating the performance of the chart.

In spite of the remarkable development of modern medical treatment and technology, the threat of pandemic diseases such as anthrax, cholera, and SARS has not disappeared. As a part of emerging healthcare decision problems, many researchers have studied how to detect and contain disease outbreaks, and our research is aligned with this trend. This thesis mainly consists of two parts: epidemic simulation modeling for effective intervention strategies and spatiotemporal monitoring for outbreak detection. We developed a stochastic epidemic simulation model of a pandemic influenza virus (H1N1) to test possible interventions within a structured population. The possible interventions — such as vaccination, antiviral treatment, household prophylaxis, school closure and social distancing — are investigated in a large number of scenarios, including delays in vaccine delivery and low and moderate efficacy of the vaccine. Since timely and accurate detection of a disease outbreak is crucial in terms of preparation for emergencies in healthcare and biosurveillance, we suggest two spatiotemporal monitoring charts, namely, the SMCUSUM and RMCUSUM charts, to detect increases in the rate or count of disease incidents. Our research includes convenient methods to approximate the control limits of the charts. An analytical control limit approximation method for the SMCUSUM chart performs well under certain conditions on the data distribution and monitoring range. Another control limit approximation method for the RMCUSUM chart provides robust performance to various monitoring range, spatial correlation structures, and data distributions without intensive modeling of the underlying process.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2018-03-12T14:16:55Z No. of bitstreams: 1 WaleskaCristinaEuflausino_DISSERT.pdf: 2155309 bytes, checksum: 4d1605e644787439d12a4dd235990b92 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2018-03-14T19:58:05Z (GMT) No. of bitstreams: 1 WaleskaCristinaEuflausino_DISSERT.pdf: 2155309 bytes, checksum: 4d1605e644787439d12a4dd235990b92 (MD5) / Made available in DSpace on 2018-03-14T19:58:05Z (GMT). No. of bitstreams: 1 WaleskaCristinaEuflausino_DISSERT.pdf: 2155309 bytes, checksum: 4d1605e644787439d12a4dd235990b92 (MD5) Previous issue date: 2016-12-16 / T?cnicas de controle estat?stico de processos (CEP), comumente usadas em problemas industriais, podem tamb?m ser aplicadas na ?rea da sa?de, tendo em vista que a qualidade em procedimentos m?dicos faz parte integrante do dia a dia nas estruturas hospitalares. Contudo, neste caso, a popula??o em estudo n?o pode ser considerada homog?nea como mercadorias em uma linha de produ??o. A aplica??o do CEP na ?rea de sa?de precisa admitir a exist?ncia de heterogeneidade, levando em considera??o caracter?sticas particulares de cada paciente como, por exemplo, sexo, idade, al?m de medidas que reflitam as condi??es gerais de sa?de. Neste sentido, alguns autores prop?em o monitoramento do tempo de sobreviv?ncia de pacientes, atrav?s de um gr?fico de controle de Somas Acumuladas - CUSUM (Cumulative Sum), ajustado para incorporar o risco de cada indiv?duo por meio de um modelo param?trico de regress?o. Este gr?fico ? chamado pelos autores de Gr?fico de Controle CUSUM Ajustado ao Risco (RAST CUSUM), e os resultados te?ricos foram desenvolvidos para monitorar tempos at? a ocorr?ncia de um evento, mesmo quando estes est?o sujeito a informa??es incompletas (censura ? direita), atrav?s do modelo Weibull, muito comum na modelagem de dados de sobreviv?ncia. Neste trabalho o gr?fico RAST CUSUM Weibull foi empregado com o objetivo geral de monitorar, em um estudo retrospectivo, os tempos de sobreviv?ncia dos pacientes diagnosticados com c?ncer de boca e orofaringe da Liga Norte Riograndense Contra o C?ncer no per?odo de 2001 a 2013. Os dados deste trabalho s?o oriundos da base de dados da Liga sendo registradas al?m das datas do diagn?stico e da ?ltima consulta na institui??o, caracter?sticas s?cio demogr?ficas e cl?nicas dos pacientes, consideradas como potenciais fatores de risco para redu??o no tempo m?dio de sobreviv?ncia desses indiv?duos. Como resultado do monitoramento, que levou em considera??o os fatores localiza??o e tamanho do tumor, tipo do primeiro tratamento adotado e hist?rico familiar, foi detectado um aumento de 20 % no tempo m?dio de sobreviv?ncia dos pacientes a partir do ano 2005. / Statistical process control techniques are commonly used in industrials problems and can also be applied in the health area, since quality in medical procedures are an integral part of day-to-day hospital structures. However, in this case, the population can not be regarded as homogeneous as goods in a production line. The application of the CEP in the health area must admit the existence of heterogeneity, taking into account particular characteristics of each patient with, for example, gender, age, and measures that reflect the general health conditions. In this sense, some authors propose monitoring survival time of patients, through a CUSUM (Cumulative Sum) chart, adjusted to incorporate the risk of each individual by a parametric regression model. This chart is called by the authors of RAST CUSUM, and the theoretical results were developed to monitor times until the occurrence of an event, even when they are subject to incomplete information (right censoring), considering for that the Weibull model, which is very used to model survival data. In this work the RAST CUSUM Weibull graph was used in a retrospective study, to monitoring the survival of patients with oral and oropharyngeal cancer, treated on the Northern Riograndense League of the c?ncer, from 2001 to 2013. The data of this work come from the data base of the hospital being registered beyond the dates of diagnosis and last visit to the institution, socio-demographic characteristics and clinics of patients, considered as potential risk factors for mean survival time of these individuals. As a result of the monitoring that considered the location and size of the tumor, type of the first treatment adopted and family history as risk factors, an increase of 20% in the mean survival time of the patients from 2005 was detected.

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Introduction: As the number of minimally invasive technologies increases in the field of thoracic surgery, so have the number of learning curve analyses performed for these innovations. Variation in learning curve methodology makes between-study comparisons and evidence syntheses difficult. Furthermore, poorly described and reported learning curve analyses make the results difficult to apply to different clinical settings. The objective of this systematic review is to characterize the variability in the methods used to construct and describe learning curves, with the goal of identifying shortcomings and potential areas for improvement in this line of research. Methods: A search of Ovid Medline, Ovid Embase, EBSCO CINAHL, and Web of Science was performed. Studies of learning curves of anatomical lung resection operations in adult patients published in the English language were eligible for inclusion. Two reviewers independently assessed studies for eligibility, and extracted relevant data. Results: The search yielded 56 articles eligible for inclusion in the present review. A variety of methods were used to construct the learning curve, with chronological grouping of cases being the most commonly used technique in 22 (39.29%) studies, followed by the cumulative sum method, employed in 21 (37.50%) studies. A total of 15 unique metrics were used for learning curve analyses; operative time was the most common metric, used in 39 (69.64%) studies. A large proportion of studies failed to provide details on learning curve parameters such as competency thresholds, surgeon’s prior experience, case complexity, and learning curve definition. Considerable heterogeneity was found in the methods and reporting standards of learning curve evaluations in minimally invasive thoracic surgery. Conflicts of Interest: None. Funding Source: Boris Family Centre for Robotic Surgery. / Thesis / Master of Science (MSc)

Generalized likelihood ratio (GLR) control charts are investigated for two types of statistical process monitoring (SPC) problems. The first part of this dissertation considers the problem of monitoring a normally distributed process variable when a special cause may produce a time varying linear drift in the mean. The design and application of a GLR control chart for drift detection is investigated. The GLR drift chart does not require specification of any tuning parameters by the practitioner, and has the advantage that, at the time of the signal, estimates of both the change point and the drift rate are immediately available. An equation is provided to accurately approximate the control limit. The performance of the GLR drift chart is compared to other control charts such as a standard CUSUM chart and a CUSCORE chart designed for drift detection. We also compare the GLR chart designed for drift detection to the GLR chart designed for sustained shift detection since both of them require only a control limit to be specified. In terms of the expected time for detection and in terms of the bias and mean squared error of the change-point estimators, the GLR drift chart has better performance for a wide range of drift rates relative to the GLR shift chart when the out-of-control process is truly a linear drift. The second part of the dissertation considers the problem of monitoring a linear functional relationship between a response variable and one or more explanatory variables (a linear profile). The design and application of GLR control charts for this problem are investigated. The likelihood ratio test of the GLR chart is generalized over the regression coefficients, the variance of the error term, and the possible change-point. The performance of the GLR chart is compared to various existing control charts. We show that the overall performance of the GLR chart is much better than other options in detecting a wide range of shift sizes. The existing control charts designed for certain shifts that may be of particular interest have several chart parameters that need to be specified by the user, which makes the design of such control charts more difficult. The GLR chart is very simple to design, as it is invariant to the choice of design matrix and the values of in-control parameters. Therefore there is only one design parameter (the control limit) that needs to be specified. Especially, the GLR chart can be constructed based on the sample size of n=1 at each sampling point, whereas other charts cannot be applied. Another advantage of the GLR chart is its built-in diagnostic aids that provide estimates of both the change-point and the values of linear profile parameters. / Ph. D.