1 
Estimate Value at Risk of Portfolio by ConditionalCopulaGARCH MethodLin, Weifu 02 July 2007 (has links)
Copula functions represent a methodology which can describe the dependence structure of multidimension random variable, and has recently become the most significant new tool to handle risk factors in finance such as Valueat Risk( VaR) which was probably the most widely used risk measure in financial institutions. In this paper, Copula and the forecast function of Garch model are well combined, and a new method ConditionalCopulaGarch is built for measure the dependence of financial data and compute the VaR of portfolios. CopulaGarch models allow for very flexible joint distribution by splitting the marginal behaviors form the dependence relation unlike the traditional approaches for the estimation VaR, such as variancecovariance, and the Monte Carlo approaches whereas demand the joint distribution to be known. This work presents an application of the CopulaGarch model in the estimation of VaR of a portfolio composed by NASDAQ and TAIEX (Taiwan stock exchanged capitalization weighted index) stock indices.

2 
Theory and applications of univariate distributionfree Shewhart, CUSUM and EWMA control chartsGraham, Marien Alet 19 November 2008 (has links)
Statistical quality control charts originated in the late 1920’s by Shewhart (1926, 1931 and 1939). Their applications in various disciplines have been everincreasing. Although most control charts are distributionbased, recent literature witnessed the development of a considerable number of distributionfree or nonparametric control charts. The purpose of this thesis is to present the concepts and introduce the researcher to the essentials of univariate nonparametric control charts. Various properties of nonparametric control charts are comprehensively discussed and concepts are clearly explained. Proofs and detailed calculations have been given to help the reader to study and understand the subject more thoroughly. This text contains a wide variety of illustrative examples to give an overall picture of how nonparametric control charts are used. Both simulated and real data examples have been integrated throughout the text. Since most practical problems are too large to be solved using hand calculations, some type of statistical software package is required to solve these problems. There are several excellent statistical packages available and in this thesis we make use of Microsoft Excel, SAS, Minitab, Mathcad and Mathematica to construct (almost all) the tables in this thesis. We point out that a number of Mathematica programs are provided by Chakraborti and Van de Wiel (2003) by means of the website www.win.tue.nl/~markvdw. The aim throughout is to convey the concepts of univariate nonparametric control charts in a way that readers will find attractive and interesting. Since the majority of nonparametric procedures, to be distributionfree, require a continuous population, only variables control charts are covered. We only consider control charts for monitoring the location of a process, since very few nonparametric charts are available for monitoring the spread. In this thesis we consider the three main classes of control charts: the Shewhart, CUSUM and EWMA control charts and their refinements. The text is divided into several chapters. An introduction to nonparametric control charts is presented in Chapter 1. A discussion of some of the advantages of nonparametric control charts is included while pointing out some of the disadvantages. In Chapter 2 we describe the Shewhart, CUSUMand EWMAtype sign control charts with (and without) warning limits. In Chapter 3 we describe the Shewhart, CUSUM and EWMAtype signedrank control charts with (and without) runstype signalling rules. The Shewharttype signlike control chart with (and without) signalling rules is considered in Chapter 4. In Chapter 5 we consider the Shewharttype signedranklike control chart. Finally, in Chapter 6 we consider the Shewhart and CUSUMtype MannWhitneyWilcoxon control charts. We considered decision problems under both Phase I and Phase II (see Section 1.5 for a distinction between the two phases). In all the sections of this thesis we considered Phase II process monitoring, except in Section 6.2 where a CUSUMtype control chart for the preliminary Phase I analysis of individual observations based on the MannWhitney twosample test is proposed. In the last chapter we have some concluding remarks along with some ideas for future research. / Dissertation (MSc)University of Pretoria, 2011. / Statistics / unrestricted

3 
Dynamická metrika v OSPF sítích / Dynamic Metric in OSPF NetworksMácha, Tomáš January 2016 (has links)
Masivní vývoj Internetu vedl ke zvýšeným požadavkům na spolehlivou síťovou infrastrukturu. Efektivita komunikace v síti závisí na schopnosti směrovačů určit nejlepší cestu pro odesílání a přeposílání paketů ke koncovému zařízení. Jelikož OSPF v současné době představuje jeden z nejpoužívanějších směrovacích protokolů, jakýkoli přínos, který by pomohl udržet krok s rychle se měnícím prostředí Internetu, je velmi vítán. Významným omezením OSPF protokolu je, mimo jiné, absence informovanosti algoritmu pro výpočet metriky o aktuálním vytížení linky. Tato vlastnost představuje tzv. slabé místo, což má negativní vliv na výkonnost sítě. Z tohoto důvodu byla navržena nová metoda založená na dynamické adaptaci měnících se síťových podmínek a alternativní strategii OSPF metrik. Navržená metoda řeší problém neinformovanosti OSPF metriky o síťovém provozu a nevhodně vytížených linek, které snižují výkonnost sítě. Práce rovněž přináší praktickou realizaci, kdy vlastnosti nové metody jsou testovány a ověřeny spuštěním testů algoritmu v reálných zařízeních.

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MONITORAMENTO DO CONTROLE ESTATÍSTICO DO PROCESSO UTILIZANDO FERRAMENTAS ESTATÍSTICAS / MONITORING OF STATISTICAL PROCESS CONTROL USING STATISTICAL TOOLSMartins, Sandro Luís Moresco 14 March 2011 (has links)
Empresa Brasileira de Pesquisa Agropecuária / This study used the data from a tobacco company that monitors its production
process through statistical process control. The objective of this study was to apply
multivariate statistical tools of Statistical Process Control to monitor the efficiency of
the production process of the company under study. This production process consists
of four variables monitored during the manufacture of cigarettes: RDT (Resistence to
Draw), weight, ventilation and circunference. These data were verified using
descriptive statistics, test of normality, univariate R and Xbar charts and correlation
matrix. After observing the instability of the system, the Principal Component Analysis
(PCA) was applied. PCA is an exploratory multivariate analysis technique that aims
at gathering various features of the significant variables in the instability of the
production process. EWMA was then applied on the principal components. An unstable
process presenting too much variability was observed, with several points outside the
bounds of statistical control. This situation presents some problems that may cause
inconvenience and loss to the company. This way, the company should review its
manufacturing process in order to improve its productivity and quality that in turn may
help the company to be more competitive. / O presente estudo utilizouse dos dados de uma empresa fumageira que
monitora o processo produtivo por meio do controle estatístico do processo.
Objetivouse a aplicação de ferramentas estatísticas multivariadas de Controle
Estatístico do Processo, para monitorar a eficiência do processo produtivo da
empresa em estudo. Esse processo produtivo é composto de quatro variáveis,
monitoradas na fase de fabricação do cigarro, sendo: RDT (Resistence to Draw),
peso, ventilação e circunferência. Esses dados foram verificados através da
estatística descritiva, Teste de Normalidade, gráficos univariados Xbarra e R, e
Matriz de correlação. Após constatada a instabilidade do sistema, aplicouse Análise
de Componentes Principais ACP, técnica de análise exploratória multivariada com
o objetivo de aglutinar várias características das variáveis significativas na
instabilidade do processo produtivo. Por último, foi aplicado EWMA (Exponentially
Weighted Moving Average  Médias Móveis Exponencialmente Ponderáveis) sobre
as componentes principais. Demonstrouse um processo instável, apresentando
muita variabilidade, com muitos pontos fora dos limites de controle estatístico. Essa
situação, apresenta alguns problemas, que geram transtornos e prejuízos à
empresa, sendo que a mesma deverá rever seu processo fabril a fim de melhorar a
produtividade e a qualidade, que contribuirá para tornar a empresa mais competitiva.

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考慮兩階段相依製程下量測誤差對指數加權移動平均管制圖之效應研究 / Effects of Measurement Error on EWMA Control Charts for TwoStep Process何漢葳, Ho, HanWei Unknown Date (has links)
無 / In this article, a twostep process is considered to investigate the effects of measurement errors on EWMA
and causeselecting EWMA control charts. At the end of current process, a pair of imprecise measurements of incoming quality and outgoing quality is randomly taken with individual units.
The linear relationship between incoming quality and outgoing quality is assumed and four possible states of the process are defined with respective distributions of incoming and outgoing
qualities derived. The EWMA control chart with measurement error is then constructed to monitor smallscale shift in mean for the previous process while the causeselecting control chart, or EWMA control chart based on residuals, including measurement error, is proposed to diagnose the state of current process.
Based on sensitivity analysis, the presence of imprecise measurement diminishes the power of both the EWMA and the proposed control charts and affects the detectability of process disturbances. Further, applications of proposed control charts are demonstrated through a numerical example to show some possible misuses of control charts. If the process mean shifts in a small scale when a single assignable cause occurs on each step, the proposed causeselecting control chart is more sensitive than other control charts. The Hotelling T^2 control chart is also compared to illustrate the diagnostic advantage outweighed by proposed causeselecting control chart.

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Pressure Monitoring and Fault Detection of an Antig Protection System / Tryckövervakning och feldetektion av ett antigskyddssystemAndersson, Kim January 2010 (has links)
<p>When flying a fighter aircraft such as the JAS 39 Gripen, the pilot is exposed to high gloads. In order to prevent the draining of blood from the brain during this stress an antig protection system is used. The system consists of a pair of trousers, called the antig trousers, with inflatable bladders. The bladders are filled with air, pressing tightly on to the legs in order to prevent the blood from leaving the upper part of the body.</p><p>The purpose of this thesis is to detect if the pressure of the antig trousers is deviating from the desired value. This is done by developing a detection algorithm which gives two kinds of alarm. One is given during minor deviations using a CUSUM test, and one is given at grave deviations, based on different conditions including residual, derivative and time. The thresholds, in which between the pressure should lie in a faultless system, are calculated from the gload value. The thresholds are based upon given static guidelines for the pressure tolerance area and are modified in order to adapt to the estimated dynamics of the system.</p><p>The values of the input signals, pressure and gload, were taken from real flight sessions. The validation has been performed using both faultless and faulty flight sequences, with low false alarm rate and no missed detections. All together the detection system is considered to work well.</p>

7 
Statistical Control Charts of I(d) processesWang, ChiLing 10 July 2002 (has links)
Long range dependent processes occur in many fields, it is important to monitor these processes to early detect their shifts. This paper considers the problem of detecting changes in an I(d) process or an ARFIMA(p,d,q) process by statistical control charts. The control limits of EWMA and EWRMS control charts of I(d) processes are established and analytic forms are derived. The average run lengths of these control charts are estimated by Monte Carlo simulations. In addition, we illustrate the performance of the control charts by empirical examples of I(d) processes and ARFIMA(1,d,1) processes.

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Statistische Prozessregelung von Fertigungs und Messprozess zur Erreichung einer variabilitätsarmen Produktion mikromechanischer Bauteile /Schlipf, Matthias. January 2009 (has links)
Zugl.: Karlsruhe, Universiẗat, Diss., 2009.

9 
Control Charts with Missing ObservationsWilson, Sara R. 05 May 2009 (has links)
Traditional control charts for process monitoring are based on taking samples from the process at regular time intervals. However, it is often possible in practice for observations, and even entire samples, to be missing. This dissertation investigates missing observations in Exponentially Weighted Moving Average (EWMA) and Multivariate EWMA (MEWMA) control charts. The standardized sample mean is used since this adjusts the sample mean for the fact that part of the sample may be missing. It also allows for constant control limits even though the sample size varies randomly. When complete samples are missing, the weights between samples should also be adjusted.
In the univariate case, three approaches for adjusting the weights of the EWMA control statistic are investigated: (1) ignoring missing samples; (2) adding the weights from previous consecutive missing samples to the current sample; and (3) increasing the weights of nonmissing samples in proportion, so that the weights sum to one. Integral equation and Markov chain methods are developed to find and compare the statistical properties of these charts. The EI chart, which adjusts the weights by ignoring the missing samples, has the best overall performance.
The multivariate case in which information on some of the variables is missing is also examined using MEWMA charts. Two methods for adjusting the weights of the MEWMA control statistic are investigated and compared using simulation: (1) ignoring all the data at a sampling point if the data for at least one variable is missing; and (2) using the previous EWMA value for any variable for which all the data are missing. Both of these methods are examined when the incontrol covariance matrix is adjusted at each sampling point to account for missing observations, and when it is not adjusted. The MS control chart, which uses the previous value of the EWMA statistic for a variable if all of the data for that variable is missing at a sampling point, provides the best overall performance. The incontrol covariance matrix needs to be adjusted at each sampling point, unless the variables are independent or only weakly correlated. / Ph. D.

10 
Variable Sampling Rate Control Charts for Monitoring Process VarianceHughes, Christopher Scott 20 May 1999 (has links)
Industrial processes are subject to changes that can adversely affect product quality. A change in the process that increases the variability of the output of the process causes the output to be less uniform and increases the probability that individual items will not meet specifications.
Statistical control charts for monitoring process variance can be used to detect an increase in the variability of the output of a process so that the situation can be repaired and product uniformity restored. Control charts that increase the sampling rate when there is evidence the variance has changed gather information more quickly and detect changes in the variance more quickly (on average) than fixed sampling rate procedures. Several variable sampling rate procedures for detecting increases in the process variance will be developed and compared with fixed sampling rate methods.
A control chart for the variance is usually used with a separate control chart for the mean so that changes in the average level of the process and the variability of the process can both be detected. A simple method for applying variable sampling rate techniques to dual monitoring of mean and variance will be developed. This control chart procedure increases the sampling rate when there is evidence the mean or variance has changed so that changes in either parameter that will negatively impact product quality will be detected quickly. / Ph. D.

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