This paper deals with evaluating 3D-bootstrap for the mining company New Boliden in an attempt to revise their current method of calculating average gold riches in different areas. The purpose is to find one-sided 3D-bootstrap confidence bound of the average gold riches. There lacks well-defined theory behind using 3D-bootstrap, in this paper the variogram is used as an estimate of dependencies between the observations, and the block length is chosen to be higher than this estimate. In aid of this, a simulated data material is conducted to check the validity of 3D-bootstrap in a controlled area where the theoretical value is known. The results are inconclusive, and further studies are needed.

Spelplanen för energiproducenter kommer inom de kommande årtionden förändras och företagen kommer ställas inför stora utmaningar. Kärnkraften kommer att fasas bort till 2040 enligt ett Regeringsbeslut, något som kommer kräva en utbyggnad av de förnyelsebara energikällorna som vind- och solkraft. Det är energikällor som har betydligt större variation där väder påverkar produktionen samtidigt som det är svårt att planera och prognostisera. Det kan således tillkomma och falla bort elproduktion på nätet under kort tid om det som exempel skulle sluta blåsa under en timme. Ett fungerande elsystem bygger på att det alltid ska finnas en balans mellan elproduktion och elanvändning. Denna balans mäts i frekvens på elnätet och Norden har en nominell frekvens på 50,0Hz, något som påverkas av ett underskott eller överskott av antingen produktion eller konsumtion. En utbyggnad i vind- och solkraft kan således ställa högre krav på vattenkraften som med sin goda planeringsförmåga fungerar utmärkt som frekvensreglerande kraftkälla. Inom vattenkraftsproduktion finns det två produktkategorier som energiproducenter kan sälja sin producerade el som normal drift och frekvensreglering, där den förstnämna är den som majoriteten av intäkterna står för. Eftersom det finns stora kostnader relaterade till slitage som uppstår i en turbin vid drift är det i Skellefteå Krafts intresse att studera de kostnader som är orsakade av frekvensreglering. För detta har olika utjämningsfunktioner utnyttjats för att klassificera data som antingen normal drift eller frekvensreglering, för att sedan beräkna vad frekvensregleringen motsvarar i slitage på ett aggregat. Skellefteå Kraft har i Augusti 2019 implementerat ett system som lagrar driftdata. Då en förväntad livslängd av en kaplanturbin är mellan 40 och 50 år krävdes det då mer data för att kunna dra rimliga slutsatser. Eftersom en begränsning finns gällande mängden av data, används omsamplinstekniken Block bootstrap för simulering av driftdata, vilket är nödvändigt för att kunna utföra en livslängdsanalys. Slutsatsen var att trots att frekvensreglering stod för drygt 95% av slitaget stod det endast för 8-9% av intäkterna, men att det ändå hade en vinstmarginal på 2-3 gånger kostnaden. En livslängdssimulering på 40 år medförde ett slitage på drygt 420 mikrometer för det Yttre löpskoveltappslagret (det lager som slits mest), vilket ligger inom rimligt slitage med hänsyn till designvärdet.

In this dissertation, we review the basic properties of the bootstrap and time series application. Then we apply parametric bootstrap on three simulated normal i.i.d. samples and nonparametric bootstrap on four real life financial returns. Among the time series bootstrap methods, we look into the specific method called block bootstrap and investigate the block length consideration to properly select a suitable block size for AR(1) model. We propose a new rule of blocking named as Combinatorially-Augmented Block Bootstrap(CABB). We compare the existing block bootstrap and CABB method using the simulated i.i.d. samples, AR(1) time series, and the real life examples. Both methods perform equally well in estimating AR(1) coefficients. CABB produces a smaller standard deviation based on our simulated and empirical studies. We study two procedures of collecting time series, (i) aggregation of a flow variable and (ii) systematic sampling of a stock variable. In these two procedures, we derive theorems that calculate exact equations for $m$ aggregated and $m^{th}$ systematically sampled series of the original AR(1) model. We evaluate the performance of block bootstrap estimation of the parameters of ARMA(1,1) and AR(1) model using aggregated and systematically sampled series. Simulation and real data analyses show that in some cases, the performance of the estimation based on the block bootstrap method for the MA(1) parameter of the ARMA(1,1) model in aggregated series is better than the one without using bootstrap. In an extreme case of stock price movement, which is close to a random walk, the block bootstrap estimate using systematically sampled series is closer to the true parameter, defined as the parameter calculated by the theorem. Specifically, the block bootstrap estimate of the parameter of AR(1) model using the systematically sampled series is closer to phi(n) than that based on the MLE for the AR(1) model. Future research problems include theoretical investigation of CABB, effectiveness of block bootstrap in other time series analyses such as nonlinear or VAR. / Statistics

Nous développons un modèle basé agents pour reproduire deux anomalies fréquemment observées sur les marchés financiers : distribution leptokurtique des rendements et ampleur de la volatilité irrégulière mais persistante de ces mêmes rendements. Notre but est de montrer de façon probante que ces anomalies pourraient être attribuées à une formation mimétique des anticipations des intervenants sur les marchés. Nous nous éloignons des développements récents dans le domaine des modèles modèles basés agents en finance pour proposer un modèle très simple, estimé à partir des traits statistiques saillants de l’indice français journalier CAC 40. L’hypothèse d’anticipations mimétiques peut ainsi être testée : elle n’est pas rejetée dans notre modélisation. / We develop an agent-based model to replicate two frequently observed anomalies in the financial markets: the fat tails and the clustered volatility of the distribution of the returns. Our goal is to show conclusively that these anomalies could be attributed to a mimetic formation of the expectations of the stakeholders in the markets. We did not follow the rencent developpments in the field of the ACE model in the finance, but we propose a very simple model which is estimated from the stylized facts of the French daily index CAC 40. The hypothesis of mimetic anticipations can thus be tested: it is not rejected in our modeling.

Utilizing multiple biomarkers in medical research is crucial for the diagnostic accuracy of detecting diseases. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). The optimality of the likelihood ratio has been proven but the challenges persist in estimating the likelihood ratio, primarily on the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research. Change point detection for functional time series has attracted considerable attention from researchers. Existing methods either rely on FPCA, which may perform poorly with complex data, or use bootstrap approaches in forms that fall short in effectively detecting diverse change functions. In our study, we propose a novel self-normalized test for functional time series implemented via a non-overlapping block bootstrap to circumvent reliance on FPCA. The SN factor ensures both monotonic power and adaptability for detecting diverse change functions on complex data. We also demonstrate our test's robustness in detecting changes in the autocovariance operator. Simulation studies confirm the superior performance of our test across various settings, and real-world applications further illustrate its practical utility. / Doctor of Philosophy / In medical research, it is crucial to accurately detect diseases and predict patient outcomes using multiple health indicators, also known as biomarkers. Combining these biomarkers effectively can significantly improve our ability to diagnose and treat various health conditions. However, finding the best way to combine these biomarkers has been a long-standing challenge. In this study, we propose a new, easy-to-understand method for combining multiple biomarkers using advanced estimation techniques. Our method takes into account various factors and provides a more accurate way to evaluate the combined information from different biomarkers. Through simulations, we demonstrated that our method performs better than other existing methods under a variety of scenarios. Furthermore, we applied our new method to a real-world study focusing on detecting childhood autism spectrum disorder (ASD), highlighting its practical value and potential for future applications in medical research. Detecting changes in patterns over time, especially shifts in averages, has become an important focus in data analysis. Existing methods often rely on techniques that may not perform well with more complex data or are limited in the types of changes they can detect. In this study, we introduce a new approach that improves the accuracy of detecting changes in complex data patterns. Our method is flexible and can identify changes in both the mean and variation of the data over time. Through simulations, we demonstrate that this approach is more accurate than current methods. Furthermore, we applied our method to real-world climate research data, illustrating its practical value.

Suite à un stage avec la compagnie Hatch, nous possédons des jeux de données composés de séries chronologiques de vitesses de vent mesurées à divers sites dans le monde, sur plusieurs années. Les ingénieurs éoliens de la compagnie Hatch utilisent ces jeux de données conjointement aux banques de données d’Environnement Canada pour évaluer le potentiel éolien afin de savoir s’il vaut la peine d’installer des éoliennes à ces endroits. Depuis quelques années, des compagnies offrent des simulations méso-échelle de vitesses de vent, basées sur divers indices environnementaux de l’endroit à évaluer. Les ingénieurs éoliens veulent savoir s’il vaut la peine de payer pour ces données simulées, donc si celles-ci peuvent être utiles lors de l’estimation de la production d’énergie éolienne et si elles pourraient être utilisées lors de la prévision de la vitesse du vent long terme. De plus, comme l’on possède des données mesurées de vitesses de vent, l’on en profitera pour tester à partir de diverses méthodes statistiques différentes étapes de l’estimation de la production d’énergie. L’on verra les méthodes d’extrapolation de la vitesse du vent à la hauteur d’une turbine éolienne et l’on évaluera ces méthodes à l’aide de l’erreur quadratique moyenne. Aussi, on étudiera la modélisation de la vitesse du vent par la distributionWeibull et la variation de la distribution de la vitesse dans le temps. Finalement, l’on verra à partir de la validation croisée et du bootstrap si l’utilisation de données méso-échelle est préférable à celle de données des stations de référence, en plus de tester un modèle où les deux types de données sont utilisées pour prédire la vitesse du vent. Nous testerons la méthodologie globale présentement utilisée par les ingénieurs éoliens pour l’estimation de la production d’énergie d’un point de vue statistique, puis tenterons de proposer des changements à cette méthodologie, qui pourraient améliorer l’estimation de la production d’énergie annuelle. / Following an internship with the company Hatch, we have access to datasets that are composed of wind speed time series measured at different sites accross the world and over several years. The wind speed engineers from Hatch are using these datasets jointly with Environment Canada databases in order to ascertain the wind energy potential of these sites and to know whether it is worth installing wind turbines there. For a few years, some companies are also offering mesoscale simulations of wind speed based on different environmental characteristics from the site we want to evaluate. We would like to know if it is worth paying for those mesoscale datasets and if they can be used to provide better estimations of the wind energy potential. Among other things, these data could be used to provide a better estimation of the long term mean wind speed. Since we already possess measured datasets, we will also use them to test, with statistical methods, the methodology currently used and the different steps leading to an estimation of the wind energy production. First of all, we will see what are the different methods that could be used to extrapolate wind speed to a wind turbine’s height and we will evaluate those methods with the mean squared extrapolation error. Also, we will study wind distribution modelling by the Weibull distribution and consider its variability over time. Finally, cross-validation and block bootstrap will be used to see whether we should use mesoscale data instead of wind data from Environment Canada or whether it would even be beneficial to use both kind of data to predict wind speed. In summary, the whole methodology used by wind speed engineers to estimate the energy production will be tested from a statistical point of view and we will attempt to propose changes in this methodology that could improve the estimation of the wind speed annual energy production.

近年來剖面資料的監控在統計製程控制中有很大範圍的應用。在這篇論文裡，我們針對監控無母數剖面資料提出一個實務上的操作方法。這個操作方法有下列這些重要的特色：(1)使用一個靈活且有計算效率的無母數模型B-spline來描述反應變數與解釋變數的關係；(2)一般迴歸模型中之殘差結構假設是不需要的；(3)允許剖面資料內之觀測值間具有相關性之結構。最後，我們利用一個無線偵測器的實際資料來評估所提出方法的效率。 / Profile monitoring has received increasingly attention in a wide range of applications in statistical process control (SPC). In this work, we propose a practical proposed guide which has the following important features: (i) a flexible and computationally efficient smoothing technique, called the B-spline, is employed to describe the relationship between the response variable and the explanatory variable(s); (ii) the usual structural assumptions on the residuals are not require; and (iii) the dependence structure for the within-profile observations is appropriately accommodated. Finally, a real data set from a wireless sensor is used to evaluate the efficiency of our proposed method.

由於許多國家死亡率下降快速、壽命延長幅度超乎預期，加上生育率持續低於替代水準，人口老化現象愈發明顯，近年來個人生涯規劃及政府施政，都格外強調退休後經濟生活及老年相關社會資源分配的比重。以臺灣為例，行政院經濟建設委員會 (簡稱經建會) 從1990年代開始，每兩年公布一次未來的人口推估，但過去十年來經建會屢次修正歷年的推估假設，以因應生育率及死亡率變化快速，適時提醒臺灣日益加速的人口老化。正因為人口推估可能受到人口數、社會變遷、資料品質等因素，影響統計分析的可靠性，常用於國家層級的推估方法，往往無法直接套用至縣市及其以下的層級 (即小區域)，使得小區域人口推估較為棘手，需要更加謹慎面對。 本文延續王信忠等人 (2012) 的研究，以小區域人口推估為目標，著重在生育率推估研究，結合隨機模型與修勻方法，尋找適合臺灣縣市層級的小區域人口推估方法。本文考量的隨機模型計有區塊拔靴法 (Block Bootstrap) 和 Lee-Carter 模型 (Lee and Carter 1992)，以預測未來的生育率和死亡率，並套用年輪組成推計法 (或稱為人口要素合成法；Cohort Component Method) 及修勻 (Graduation) 方法，探討這些方法與人口規模之間的關係，評估用於小區域人口推估之可行性。 本文首先以電腦模擬，探討生育率的推估，討論是否可直接推估總生育率，類似增加樣本數的概念，取代各縣市的年齡別生育率，以取得較為穩定的推估。根據模擬結果，發現人口規模對出生數的推估沒有明顯的關係，只要使用總生育率、再結合區塊拔靴法，就足以提供穩定的推估結果。實證研究方面，以臺灣縣市層級的人口及其年齡結構 (例如：0-14歲、15-64歲、65歲以上) 為驗證對象，發現分析結果也與電腦模擬相似，發現以區塊拔靴法推估臺灣各縣市的總生育率、年齡組死亡率，其推估精確度不因人口規模而打折扣，顯示以區塊拔靴法推估總生育率、年齡組死亡率，可用於推估臺灣小地區的未來人口。 / Due to the rapid mortality reduction, prolonging human longevity is a common phenomenon and longevity risk receives more attention in 21st century. Many developed countries encounter many problems brought up by prolonging life, such as poor community infrastructure and insufficient financial pension funds for the elderly. Population Projection thus becomes essential in government planning in dealing with the population aging. However, rapid changes in mortality and fertility make the projection very tricky. It would be even more difficult to project areas with fewer populations (i.e., small areas) since it takes extra efforts to deal with the larger fluctuations in small population. The objective of the study is to construct a standard operating procedure (SOP) for small population projection. Unlike the previous study, e.g., Wang et al. (2012), we will take both the fertility and mortality into account (but set migration aside for simplicity). First, for the fertility projection, we evaluate if total fertility rates (TFR) are more appropriate than the age-specific fertility rates for small population. Also, we compare two fertility projection methods: Lee-Carter model and block bootstrap, and check which shows better results. Based on the computer simulation, we found that TFR performs better and the block bootstrap method is more sensitive to rapid fertility changes. As for mortality rate projection, we also recommend the standard operating procedure by Wang et al. (2012). However, the smoothing methods have limited impacts on mortality projection and can be ignored. In addition to simulation, we also apply the SOP for projecting the small population to Taiwan counties and it achieves satisfactory results. However, due to the availability of data, our method can only be used for short-term projection (at most 30 years) and these results might not apply to long-term projection. Also, similar to the previous work, the fertility rates have the larger impact on small population projection, although we think that the migration has large impact as well. In this study, only the stochastic projection is considered and we shall consider including expert opinions as the future study.

國家政策之制定須配合未來人口總數及其結構等特性，藉以達到提高國民福 祉的願景，因此各國均定期公佈人口推估(Population Projection)的結果，目前臺 灣官方人口推估為每兩年公布一次。人口推估主要考量三個要素：出生、死亡、 遷移，以國家層級而言，通常遷徙對未來人口的影響遠小於出生與死亡，所以過 去行政院經濟建設委員會的官方全國人口推估一般專注於出生與死亡。然而，各 國研究發現遷徙是小區域人口推估為最重要的因素，人口數愈少、影響程度有愈 大的傾向，但文獻中較缺乏臺灣內部遷移的研究。如能掌握臺灣小區域人口遷徙 的變遷，將能使政策因地制宜，有助地方政府提高推行政策的有效性，也是本研 究之目標。 由於缺乏完整的縣市、鄉鎮市區層級的詳細遷移資料，本研究以人口平衡公 式反推淨遷移人數，找出各地區的遷移特性後，代入人口變動要素合成法(Cohort Component Method)，搭配屬於機率推估的區塊拔靴法(Block Bootstrap)，推估小 區域的未來人口。關於出生及死亡的推估，過去研究發現使用區塊拔靴法用於小 區域的生育率（曹育欣，2012）及死亡率（金碩，2011），皆有不錯的推估結果。 本研究以臺北市為範例，討論區塊拔靴法在小區域遷徙人口數、年齡別遷徙人口 的推估效果，及是否適合運用在其他不同縣市。 / The population projection is used to provide information for the policy planning of governments. In Taiwan, the Council for Economic Planning and Development is in charge of the official population projection and it release projection results every two years. Basically, three factors are considered in population projection: birth, death, and migration. Since the migration has little impacts in country-level projection, many countries (including Taiwan) assume the future migration is zero or close to zero, and the focus of projection is usually on the birth and death. However, for the projection of small area (such as county- or township-level), past studies found that the effect of migration cannot be ignored. But, partly due to the limitation of migration data, there are not many studies explore the migration patterns of counties or townships in Taiwan. In this study, we use the population records (births and deaths) and the population equation to derive the county-level records of internal migration in Taiwan. We use these data to explore the migration patterns of all counties in Taiwan, and then applying block bootstrap method to modify the county-level population projection. Note that, the block bootstrap is shown to be reliable in forecasting fertility (Tsao, 2012) and mortality (Jin, 2011) for small areas. In this study, we also use the Taipei City to demonstrate the population projection which includes the internal migration, and the result is promising.

Characterizing the uncertainty in rainfall using stochastic models has been a challenging area of research in the field of operational hydrology for about half a century. Simulated sequences drawn from such models find use in a variety of hydrological applications. Traditionally, parametric models are used for simulating rainfall. But the parametric models are not parsimonious and have uncertainties associated with identification of model form, normalizing transformation, and parameter estimation. None of the models in vogue have gained universal acceptability among practising engineers. This may either be due to lack of confidence in the existing models, or the inability to adopt models proposed in literature because of their complexity or both. In the present study, a new nonparametric Matched Block Bootstrap (MABB) model is proposed for stochastic simulation of rainfall at daily time scale. It is based on conditional matching of blocks formed from the historical rainfall data using a set of predictors (conditioning variables) proposed for matching the blocks. The efficiency of the developed model is demonstrated through application to rainfall data from India, Australia, and USA. The performance of MABB is compared with two non-parametric rainfall simulation models, k-NN and ROG-RAG, for a site in Melbourne, Australia. The results showed that MABB model is a feasible alternative to ROG-RAG and k-NN models for simulating daily rainfall sequences for hydrologic applications. Further it is found that MABB and ROG-RAG models outperform k-NN model. The proposed MABB model preserved the summary statistics of rainfall and fraction of wet days at daily, monthly, seasonal and annual scales. It could also provide reasonable performance in simulating spell statistics. The MABB is parsimonious and requires less computational effort than ROG-RAG model. It reproduces probability density function (marginal distribution) fairly well due to its data driven nature. Results obtained for sites in India and U.S.A. show that the model is robust and promising.