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<p>In this thesis, inferential methods for one-shot device testing data from accelerated life-test are developed. Due to constraints on time and budget, accelerated life-tests are commonly used to induce more failures within a reasonable amount of test-time for obtaining more lifetime information that will be especially useful in reliability analysis. One-shot devices, which can be used only once as they get destroyed immediately after testing, yield observations only on their condition and not on their real lifetimes. So, only binary response data are observed from an one-shot device testing experiment. Since no failure times of units are observed, we use the EM algorithm for determining the maximum likelihood estimates of the model parameters. Also, inference for the reliability at a mission time and the mean lifetime at normal operating conditions are also developed.</p> <p>The thesis proceeds as follows. Chapter 2 considers the exponential distribution with single-stress relationship and develops inferential methods for the model parameters, the reliability and the mean lifetime. The results obtained by the EM algorithm are compared with those obtained from the Bayesian approach. A one-shot device testing data is analyzed by the proposed method and presented as an illustrative example. Next, in Chapter 3, the exponential distribution with multiple-stress relationship is considered and corresponding inferential results are developed. Jackknife technique is described for the bias reduction in the developed estimates. Interval estimation for the reliability and the mean lifetime are also discussed based on observed information matrix, jackknife technique, parametric bootstrap method, and transformation technique. Again, we present an example to illustrate all the inferential methods developed in this chapter. Chapter 4 considers the point and interval estimation for the one-shot device testing data under the Weibull distribution with multiple-stress relationship and illustrates the application of the proposed methods in a study involving the development of tumors in mice with respect to risk factors such as sex, strain of offspring, and dose effects of benzidine dihydrochloride. A Monte Carlo simulation study is also carried out to evaluate the performance of the EM estimates for different levels of reliability and different sample sizes. Chapter 5 describes a general algorithm for the determination of the optimal design of an accelerated life-test plan for one-shot device testing experiment. It is based on the asymptotic variance of the estimated reliability at a specific mission time. A numerical example is presented to illustrate the application of the algorithm. Finally, Chapter 6 presents some concluding remarks and some additional research problems that would be of interest for further study.</p> / Doctor of Philosophy (PhD)

<p>The joint censoring scheme is of practical significance while conducting comparative life-tests of products from different units within the same facility. In this thesis, we derive the exact distributions of the maximum likelihood estimators (MLEs) of the unknown parameters when joint censoring of some form is present among the multiple samples, and then discuss the construction of exact confidence intervals for the parameters.</p> <p>We develop inferential methods based on four different joint censoring schemes. The first one is when a jointly Type-II censored sample arising from $k$ independent exponential populations is available. The second one is when a jointly progressively Type-II censored sample is available, while the last two cases correspond to jointly Type-I hybrid censored and jointly Type-II hybrid censored samples. For each one of these cases, we derive the conditional MLEs of the $k$ exponential mean parameters, and derive their conditional moment generating functions and exact densities, using which we then develop exact confidence intervals for the $k$ population parameters. Furthermore, approximate confidence intervals based on the asymptotic normality of the MLEs, parametric bootstrap intervals, and credible confidence regions from a Bayesian viewpoint are all discussed. An empirical evaluation of all these methods of confidence intervals is also made in terms of coverage probabilities and average widths. Finally, we present examples in order to illustrate all the methods of inference developed here for different joint censoring scenarios.</p> / Doctor of Science (PhD)

[pt] Em um mundo globalizado, em contínua transformação, são cada vez mais freqüentes mudanças no perfil da demanda. Se não detectadas rapidamente, elas podem gerar impactos negativos no progresso de um negócio devido à baixa qualidade nas previsões de venda, que começam a gerar valores sistematicamente acima ou abaixo da demanda real indicando a presença de viés. Para evitar esse cenário, técnicas formais para detecção de viés podem ser incorporadas ao processo de previsão de demanda. Diante desse quadro, a presente dissertação compara os desempenhos, via simulação, das principais técnicas formais de detecção de viés em previsão de demanda presentes na literatura. Nesse sentido, seis técnicas são identificadas e analisadas. Quatro são baseadas em estatísticas Tracking Signal e duas são adaptadas de técnicas de Controle Estatístico de Processos. Os modelos de previsão de demanda monitorados pelas técnicas em questão são os de séries temporais estruturadas, associados ao método de amortecimento exponencial simples e ao método de Holt, respectivamente, para séries com nível médio constante e séries com tendência. Três tipos de alterações no perfil da demanda – que acarretam em viés na previsão – são examinados. O primeiro consiste em mudanças no nível médio em séries temporais de nível médio constante. O segundo tipo também considera séries temporais de nível médio constante, porém com o foco em surgimentos de tendências. O terceiro viés consiste em mudanças na tendência em series temporais com tendência pré-incorporada. Entre os resultados obtidos, destaca-se a conclusão de que, para a maioria das situações estudadas, as técnicas baseadas nas estatísticas Tracking Signal possuem desempenho superior às demais técnicas com relação à eficiência na detecção de viés. / [en] In a globalized world, in continuous transformation, changes in the demand pattern are increasingly frequent. If not rapidly detected, they can have a negative and persistent impact in the wellbeing of a business due to continuously poor quality sales forecasts, which begin to generate values systematically above or below the actual demand indicating the presence of bias. To avoid this happening, statistical techniques can be incorporated in a prediction process with the objective known as bias detection in demand forecasting. Considering this situation, the present dissertation compares, through simulation, the efficiency performance of the main existing formal techniques of monitoring demand forecasting models, with the view of bias detection. Six of such techniques are identified and analyzed in this work. Four are based on Tracking Signal Statistics and two are adapted from the Statistical Process Control approach. The demand forecasting models monitored by the techniques in question can be classified as structured time series, for a constant level or trend pattern, and using both the simple exponential smoothing and the Holt s methods. Three types of changes in the demand pattern - which result in biased prediction - are examined. The first one focus on simulated changes on the average level of various constant times series. The second type also considered various constant times series, but now simulating the appearance of different trends. And the third refers to simulate changes in trends in various times series with pre-established trends. Among the results attained, one stands out: the techniques based on Tracking Signal Statistics - when compares to other methods - showed superior performance insofar as efficient bias detection in demand forecasting.

La ecuación de la difusión neutrónica describe la población de neutrones de un reactor nuclear. Este trabajo trata con este modelo para reactores nucleares con geometría hexagonal. En primer lugar se estudia la ecuación de la difusión neutrónica. Este es un problema diferencial de valores propios, llamado problema de los modos Lambda. Para resolver el problema de los modos Lambda se han comparado diferentes métodos en geometrías unidimensionales, resultando como el mejor el método de elementos espectrales. Usando este método discretizamos los operadores en geometrías bidimensiones y tridimensionales, resolviendo el problema algebraica de valores propios resultante con el método de Arnoldi. La distribución de neutrones estado estacionario se utiliza como condición inicial para la integración de la ecuación de la difusión neutrónica dependiente del tiempo. Se utiliza un método de Euler implícito para integrar en el tiempo. Cuando un nodo está parcialmente insertado aparece un comportamiento no físico de la solución, el efecto ``rod cusping'', que se corrige mediante la ponderación de las secciones eficaces con el flujo del paso de tiempo anterior. Cuando la solución de los sistemas algebraicos que surgen en el método hacia atrás, un método de Krylov se utiliza para resolver los sistemas resultantes, y diferentes estrategias de precondicionamiento se evalúan se. La primera consiste en el uso de la estructura de bloque obtenido por los grupos de energía para resolver el sistema por bloques, y diferentes técnicas de aceleración para el esquema iterativo de bloques y un precondicionador utilizando esta estructura de bloque se proponen. Además se estudia un precondicionador espectral, que hace uso de la información en un subespacio de Krylov para precondicionar el siguiente sistema. También se proponen métodos exponenciales de segundo y cuarto orden integrar la ecuación de difusión neutrónica dependiente del tiempo, donde la exponencial de la matriz del sistema tiene qu / González Pintor, S. (2012). Approximation of the Neutron Diffusion Equation on Hexagonal Geometries [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17829

An analytic framework is devised for the performance modelling and evaluation of a wireless Global System for Mobile Telecommunication (GSM) cell with General Packet Radio Service (GPRS) supporting both multiple class voice and data services, respectively, under an aggregate sharing scheme (ASS). The investigation focuses on the study of a proposed GE/GE/c/N/PR/CBS queueing system with c (¿1) servers, finite capacity, N (¿c), generalised exponential (GE) GSM/GPRS interarrival and service times under pre-emptive resume (PR) priority rule and complete buffer sharing (CBS) scheme. The principle of maximum entropy (ME) is used t ocharacterise new closed form expressions for the state and blocking probabilities, subject to appropriate GE-type queueing theoretic constraints per class. Typical numerical examples are included to validate the ME solution against simulation at 95% confidence intervals and study the effect of external GMS/GPRS bursty traffic upon the performance of the cell.

Yes / Risk Management is one of the most relevant approaches and systematic applications of strategies, procedures and practices management that have been introduced in literatures for identifying and analysing risks which exist through the whole life of a product ,a process or services. Therefore, the aim of this paper is to propose a risk assessment model that will be implemented to the energy sector, particularly to power plants. This model combines the Analytic Hierarchy Process (AHP) technique with a new enhanced Balance Score Card (BSC). AHP is constructed to determine the weights and the priorities for all perspectives and risk indicators that involved in the BSC. The novelty in this paper is not only in using the BSC for risk assessment, but also, in developing a new BSC with six perspectives, which are sustainability perspective; economic; learning and growth; internal and operational business process; supply chain and customer/demand perspective. Another three contributions of this paper are firstly, including the sustainability dimension in BSC, and covering nine risk categories, which comprise 84 risk indicators that have been distributed across the six risk BSC perspectives. Secondly, assessing the non-technical risks in power plants and finally, this research will concentrate on the strategic level instead of the operational level where the majority of researches focus on latter but the former is far less researched. The created model will provide an effective measurement for the risks particularly, in the power plants sector. The results of this study demonstrate that the supply chain risks perspective is the keystone for the decision making process. Furthermore, these risk indicators with the new structure of BSC with six perspectives, help in achieving the organisation mission and vision in addition to affording a robust risk assessment model. The inputs of this model are composed from a previous stage using a modified Failure Mode and Effect Analysis (FMEA) (which has been used the Exponential Weighted Geometric Mean (EWGM)) to understand and analyse all risks, after which, the results of the developed FMEA which are the Risk Priority Numbers (RPN’s), have been used to build the AHP-BSC risk model. These risks are collected with difficulty from various literatures. This study will be validated in the next stage in power plants in the Middle East. / Hashemite University, Jordan

Yes / One of the main features of the third generation (3G) mobile networks is their capability to provide different classes of services; especially multimedia and real-time services in addition to the traditional telephony and data services. These new services, however, will require higher Quality of Service (QoS) constraints on the network mainly regarding delay, delay variation and packet loss. Additionally, the overall traffic profile in both the air interface and inside the network will be rather different than used to be in today's mobile networks. Therefore, providing QoS for the new services will require more than what a call admission control algorithm can achieve at the border of the network, but also continuous buffer control in both the wireless and the fixed part of the network to ensure that higher priority traffic is treated in the proper way. This paper proposes and analytically evaluates a buffer management scheme that is based on multi-level priority and Complete Buffer Sharing (CBS) policy for all buffers at the border and inside the wireless network. The analytical model is based on the G/G/1/N censored queue with single server and R (R¿2) priority classes under the Head of Line (HoL) service rule for the CBS scheme. The traffic is modelled using the Generalised Exponential distribution. The paper presents an analytical solution based on the approximation using the Maximum Entropy (ME) principle. The numerical results show the capability of the buffer management scheme to provide higher QoS for the higher priority service classes.

Der verhaltensökonomischen Literatur entsprechend behandeln die drei Kapitel dieser Dissertation unterschiedliche Aspekte des menschlichen Verhaltens, welches als "nicht-rational" zu bezeichnen ist. Jedes dieser Kapitel leistet einen Beitrag zum aktuellen Stand der Forschung auf dem Gebiet der Verhaltensökonomik mit Hilfe von entweder experimentellen, empirischen oder methodischen Ansätzen. Das erste Kapitel schlägt ein einfaches verhaltensökonomisches Modell vor und unterzieht dieses einer Reihe von experimentellen Tests. Das Modell erweitert die Literatur zur Fehlwahrnehmung von multiplikativen Wachstumsprozessen und hilft somit typische Fehlinvestitionen in der langen Frist zu erklären. Im Rahmen des zweiten Kapitels werden Daten einer Online-Kreditbörse genutzt, um empirisch zu untersuchen, ob sich private Investoren entsprechend den Vorhersagen der standardmäßigen ökonomischen Fachliteratur verhalten und einzig die erwartete Rendite berücksichtigen oder ob sie von anderen nicht-finanztechnischen Attributen eines Schuldners beeinflusst werden. Der Schwerpunkt der Analyse liegt dabei auf Geschlechterdiskriminierung im Rahmen dessen unterschiedliche Diskriminierungskonzepte getestet werden. Das dritte Kapitel wählt einen methodischen Ansatz und schlägt ein innovatives Experiment-Design vor, welches den empirisch gut dokumentierten Schwierigkeiten bzgl. der Angabe von subjektiven Wahrscheinlichkeiten von Teilnehmern an Umfragen und Laborexperimenten Rechnung trägt. Ein Binary-Choice-Ansatz eingebettet in ein adaptives Experiment-Design minimiert den Aufwand für die Befragten und ermöglich somit eine praktikable und effiziente Elizitierung der subjektiven Meinungen. / In the line with the literature on behavioral economics, the three chapters of this dissertation shed light on different aspects of human behavior that are at odds with rationality. Each chapter contributes to the existing behavioral economic research using either experimental, empirical, or methodological tools. First, by proposing and experimentally testing a simple behavioral model that extends the literature on the misperception of multiplicative growth processes, Chapter 1 aims to explain common money mistakes that people often make with long-term investments such as retirement savings plans. Second, in Chapter 2, real-life investment data of an online-lending platform are used to empirically investigate if private investors behave as the standard economic literature would predict and solely consider an investment’s expected return or if they also care about other non-financial attributes of a debtor. The focus of the analysis is on gender discrimination, thereby defining and econometrically testing different concepts of how investors discriminate between male and female borrowers. Third, Chapter 3 takes a methodological path and proposes a novel experimental design that accounts for the empirically well-documented difficulties that survey respondents typically have when asked to state subjective probabilities. A binary choice approach embedded in an adaptive experimental design helps to minimize effort of the respondents, thus allowing for a more practical belief elicitation in both the lab and the field.

In this work, we focus on enhancing the efficacy of predictive modeling in operational management decision making in two different settings: Essay 1 focuses on demand forecasting for the companies and the second study utilizes longitudinal data to analyze the illicit drug seizure and overdose deaths in the United States. In Essay 1, we utilize an operational system (newsvendor model) to evaluate the forecast method outcome and provide guidelines for forecast method (the exponential smoothing model) performance assessment and judgmental adjustments. To assess the forecast outcome, we consider not only the common forecast error minimization approach but also the profit maximization at the end of the forecast horizon. Including profit in our assessment enables us to determine if error minimization always results in maximum profit. We also look at the different levels of profit margin to analyze their impact on the forecasting method performance. Our study also investigates how different demand patterns influence maximizing the forecasting method performance. Our study shows that the exponential smoothing model family has a better performance in high-profit products, and the rate of decrease in performance versus demand uncertainty is higher in a stationary demand environment.In the second essay, we focus on illicit drug overdose death rate. Illicit drug overdose deaths are the leading cause of injury death in the United States. In 2017, overdose death reached the highest ever recorded level (70,237), and statistics show that it is a growing problem. The age adjusted rate of drug overdose deaths in 2017 (21.7 per 100,000) is 9.6% higher than the rate in 2016 (19.8 per 100,000) (U. S. Drug Enforcement Administration, 2018, p. V). Also, Marijuana consumption among youth has increased since 2009. The magnitude of the illegal drug trade and its resulting problems have led the government to produce large and comprehensive datasets on a variety of phenomena relating to illicit drugs. In this study, we utilize these datasets to examine how marijuana usage among youth influence excessive drug usage. We measure excessive drug usage in terms of drug overdose death rate per state. Our study shows that illegal marijuana consumption increases excessive drug use. Also, we analyze the pattern of most frequently seized illicit drugs and compare it with drugs that are most frequently involved in a drug overdose death. We further our analysis to study seizure patterns across layers of heroin and cocaine supply chain across states. This analysis reveals that most active layers of the heroin supply chain in the American market are retailers and wholesalers, while multi-kilo traffickers are the most active players in the cocaine supply chain. In summary, the studies in this dissertation explore the use of analytical, descriptive, and predictive models to detect patterns to improve efficacy and initiate better operational management decision making.