Cette thèse s'inscrit dans le contexte de la statistique des valeurs extrêmes. Elle y apporte deux contributions principales. Dans la littérature récente en statistique des valeurs extrêmes, un modèle de queues de distributions a été introduit afin d'englober aussi bien les lois de type Pareto que les lois à queue de type Weibull. Les deux principaux types de décroissance de la fonction de survie sont ainsi modélisés. Un estimateur des quantiles extrêmes a été déduit de ce modèle mais il dépend de deux paramètres inconnus, le rendant inutile dans des situations pratiques. La première contribution de cette thèse est de proposer des estimateurs de ces paramètres. Insérer nos estimateurs dans l'estimateur des quantiles extrêmes précédent permet alors d'estimer des quantiles extrêmes pour des lois de type Pareto aussi bien que pour des lois à queue de type Weibull d'une façon unifiée. Les lois asymptotiques de nos trois nouveaux estimateurs sont établies et leur efficacité est illustrée sur des données simulées et sur un jeu de données réelles de débits de la rivière Nidd se situant dans le Yorkshire en Angleterre. La seconde contribution de cette thèse consiste à introduire et estimer une nouvelle mesure de risque appelé Conditional Tail Moment. Elle est définie comme le moment d'ordre a>0 de la loi des pertes au-delà du quantile d'ordre p appartenant à ]0,1[ de la fonction de survie. Estimer le Conditional Tail Moment permet d'estimer toutes les mesures de risque basées sur les moments conditionnels telles que la Value-at-Risk, la Conditional Tail Expectation, la Conditional Value-at-Risk, la Conditional Tail Variance ou la Conditional Tail Skewness. Ici, on s'intéresse à l'estimation de ces mesures de risque dans le cas de pertes extrêmes c'est-à-dire lorsque p tend vers 0 lorsque la taille de l'échantillon augmente. On suppose également que la loi des pertes est à queue lourde et qu'elle dépend d'une covariable. Les estimateurs proposés combinent des méthodes d'estimation non-paramétrique à noyau avec des méthodes issues de la statistique des valeurs extrêmes. Le comportement asymptotique de nos estimateurs est établi et illustré aussi bien sur des données simulées que sur des données réelles de pluviométrie provenant de la région Cévennes-Vivarais. / This thesis can be viewed within the context of extreme value statistics. It provides two main contributions to this subject area. In the recent literature on extreme value statistics, a model on tail distributions which encompasses Pareto-type distributions as well as Weibull tail-distributions has been introduced. The two main types of decreasing of the survival function are thus modeled. An estimator of extreme quantiles has been deduced from this model, but it depends on two unknown parameters, making it useless in practical situations. The first contribution of this thesis is to propose estimators of these parameters. Plugging our estimators in the previous extreme quantiles estimator allows us to estimate extreme quantiles from Pareto-type and Weibull tail-distributions in an unified way. The asymptotic distributions of our three new estimators are established and their efficiency is illustrated on a simulation study and on a real data set of exceedances of the Nidd river in the Yorkshire (England). The second contribution of this thesis is the introduction and the estimation of a new risk measure, the so-called Conditional Tail Moment. It is defined as the moment of order a>0 of the loss distribution above the quantile of order p in (0,1) of the survival function. Estimating the Conditional Tail Moment permits to estimate all risk measures based on conditional moments such as the Value-at-Risk, the Conditional Tail Expectation, the Conditional Value-at-Risk, the Conditional Tail Variance or the Conditional Tail Skewness. Here, we focus on the estimation of these risk measures in case of extreme losses i.e. when p converges to 0 when the size of the sample increases. It is moreover assumed that the loss distribution is heavy-tailed and depends on a covariate. The estimation method thus combines nonparametric kernel methods with extreme-value statistics. The asymptotic distribution of the estimators is established and their finite sample behavior is illustrated both on simulated data and on a real data set of daily rainfalls in the Cévennes-Vivarais region (France).

The study of how social organizations work, change and develop is central to sociology and to our understanding of the social world and its transformations. At the same time, the underlying principles of organizational dynamics are extremely difficult to investigate. This is partly due to the difficulties of tracking organizations, individuals and their interactions over relatively long periods of time. But it is also due to limitations in the kinds of quantitative methods used to tackle these questions, which are for the most part based on regression analysis. This thesis seeks to improve our understanding of social organizing by using models to explore and describe the logics of the structures and mechanisms underlying organizational change. Particular emphasis is given to the modeling process, the use of new concepts and analogies, and the application of interdisciplinary methods to get new insights into classical sociological questions. The thesis consists of an introductory part and five studies (I-V). Using Swedish longitudinal data on employment in the Stockholm Region, the studies tackle different dimensions of organizational dynamics, from organizational structures and growth processes to labor mobility and employment trajectories. The introductory chapters contextualize the studies by providing an overview of theories, concepts and quantitative methods that are relevant for the modeling of organizational dynamics.  The five studies look into various aspects of organizational dynamics with the help of complementary data representations and non-traditional quantitative methods. Study I analyzes organizational growth statistics for different sectors and industries. The typically observed heavy-tailed statistical patterns for the size and growth rate distributions are broken down into a superposition of interorganizational movements. Study II models interorganizational movements as a labor flow network. Organizations tend to be more tightly linked if they belong to the same ownership sector. Additionally, public organizations have a more stable connection structure. Study III uses a similarity-based method called homogeneity analysis to map out the social space of large organizations in the Stockholm Region. A social distance is then derived within this space, and we find that the interorganizational movements analyzed in Studies I and II take place more often between organizations that are closer in social space and in the same network community. Study IV presents an approach to organizational dynamics based on sequences of employment states. Evidence for a positive feedback mechanism is found for large and highly sequence-diverse public organizations. Finally, Study V features an agent-based model where we simulate a social influence mechanism for organizational membership dynamics. We introduce a parameter analogous to a physical temperature to model contextual influence, and the familiar growth distributions are recovered as an intermediate case between extreme parameter values. The thesis as a whole provides suggestions for a more process-oriented modeling approach to social organizing that gives a more prominent role to the logics of organizational change. Finally, the series of methodological tools discussed can be useful for the analysis of many other social processes and more broadly for the development of quantitative sociological methods. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.</p><p> </p>

<p>Human interactions with white-tailed deer (<em>Odocoileus virginianus</em>) continue to change across the U.S. The growth of deer populations and urbanization of human populations have shifted values for wildlife away from traditional use toward mutual coexistence while simultaneously providing habitat for deer to thrive.<strong> </strong>Still, a mismatch exists between the reality of human-deer interactions and the management of them. Despite a changing social landscape, the human dimensions of deer management remain focused on hunting interests and the mitigation of crop damage to agricultural producers. Amid a national push to broaden wildlife ‘stakeholders’ to encompass all potential beneficiaries of wildlife, state wildlife agencies need to assess the needs and concerns of the broader public they serve to determine whether and how to engage non-traditional groups in wildlife management planning.</p> <p>Recognizing these needs, the Indiana Department of Natural Resources (IN-DNR) partnered with Purdue University in 2018 to initiate the Integrated Deer Management Project (IDMP). As part of the IDMP, this dissertation comprises the first empirical assessment of social perceptions of white-tailed deer across Indiana. My research aimed to: (i) examine the initial context of human-deer interactions in Indiana and identify key social and cognitive factors that shape them; (ii) investigate how emotions, an understudied construct, interact with beliefs and attitudes to influence resident judgements about deer management; (iii) understand existing levels of satisfaction with deer management, potentials for social conflict over management approaches, and their social-ecological drivers; and (iv) develop indices and tools that can help IN-DNR officials better account for social perceptions and concerns in deer management planning. Due to a lack of prior knowledge about human-deer interactions in the state, I used an exploratory mixed-methods research design to address these objectives. I began by conducting 59 semi-structured interviews with residents around Indiana and two focus groups in the city of Bloomington (n=14) to understand their existing perceptions, beliefs, attitudes, and emotions related to deer and deer management. These interviews informed the development of a quantitative survey which I distributed to 6,000 residents across the state. I received 1806 completed surveys for a response rate of 33%.</p> <p>My data show that social perceptions of deer and deer management remain complex, driven by dynamic feedbacks among emotions, personal experiences, livelihood and behavioral contexts, beliefs about deer management, and beliefs about other social groups. I found that mixed emotions, situational contexts, and perceived power imbalances play key roles in shaping and shifting deer-related cognitions, yet models of cognitive processing, and human-wildlife interactions more broadly, neglect these dynamics. Emotions, specifically, have been marginalized by researchers and practitioners, likely due to the perception that they represent irrational reactions rather than calculated judgements. Under different scenarios of encountering deer, however, I found that respondent emotions exert a mediating effect on their judgments about deer management, and that the type of deer encountered matters. Emotions thus work together with cognitions to process various stimuli in a human-wildlife encounter and reach a normative decision. I posit that understanding when and why emotional responses arise will help practitioners develop more effective and socially accepted approaches to wildlife management.</p> <p>I next developed and analyzed indices of public satisfaction with the IN-DNR and potentials for social conflict over deer management approaches. I found that public satisfaction with deer management is nuanced and multidimensional. Cognitive variables like residents’ perceived acceptability of management methods and their deer-related concerns most strongly predicted agency performance and quality measures of satisfaction, whereas demographic characteristics including self-identity, wildlife value orientation, and allowance of hunting on one’s property exerted the strongest influences on trust components of satisfaction. Future studies should advance a multidimensional conception of satisfaction and associate it with key variables that I suspect underly satisfaction but were not captured in this study: perceived control, psychological distance, and norms of knowledge exchange between wildlife agencies and the public. Next, I found that potentials for social conflict over deer management varied with resident self-identities and management methods but showed more predictable variation with political ideologies. Geographically, hotspots of social conflict clustered around urban areas, indicating that cities and their residents should serve as a focus for public engagement efforts and mixed management strategies. Expanding agency conceptions of public satisfaction and social conflict represents a critical step towards broadening support for wildlife management and practicing good wildlife governance.<strong> </strong>I conclude by discussing barriers to integrating social and ecological data and the practicality of incorporating complex social dimensions into wildlife management planning.</p>

Numerous studies on Queueing systems, such as Internet traffic flows, have shown to be bursty, self-similar and/or long-range dependent, because of the heavy (long) tails for the various distributions of interest, including intermittent intervals and queue lengths. Other studies have addressed vacation in no-customers’ queueing system or when the server fails. These patterns are important for capacity planning, performance prediction, and optimization of networks and have a negative impact on their effective functioning. Heavy-tailed distributions have been commonly used by telecommunication engineers to create workloads for simulation studies, which, regrettably, may show peculiar queueing characteristics. To cost-effectively examine the impacts of different network patterns on heavy- tailed queues, new and reliable analytical approaches need to be developed. It is decided to establish a brand-new analytical framework based on optimizing entropy functionals, such as those of Shannon, Rényi, Tsallis, and others that have been suggested within statistical physics and information theory, subject to suitable linear and non-linear system constraints. In both discrete and continuous time domains, new heavy tail analytic performance distributions will be developed, with a focus on those exhibiting the power law behaviour seen in many Internet scenarios. The exposition of two major revolutionary approaches, namely the unification of information geometry and classical queueing systems and unifying information length theory with transient queueing systems. After conclusions, open problems arising from this thesis and limitations are introduced as future work.

<p dir="ltr">The spread and increasing prevalence of chronic wasting disease (CWD) in white-tailed deer (<i>Odocoileus virginianus</i>) has far reaching implications for natural resource management in Indiana. CWD is a transmissible spongiform encephalopathy that affects white-tailed deer and other cervids. This disease is invariably fatal in white-tailed deer, and there is concern that its continuing spread will cause populations to decline. White-tailed deer are also a culturally and economically important game species. Therefore, effective management of CWD must consider the epidemiologic, social, and economic dimensions of disease management. In Chapter One of my dissertation, I apply an agent-based model (ABM) framework to simulate how preemptive harvest increase and reactive culling affect CWD persistence and geographic spread. I found that preemptive harvest and reactive culling both had a small effect on preventing the establishment of CWD in the deer population. In Chapter Two, I test whether presenting deer hunters and non-deer hunters with results from CWD models and images of sick or healthy deer increases behavioral intention to engage in CWD mitigating behaviors. I found that using the web app did not change the behavioral intention of hunters and non-hunters. In Chapter Three, I conduct a cost-effectiveness analysis to identify the optimal combination of CWD surveillance and culling effort in terms of disease prevention. I found that testing 40% of hunter-harvested deer for CWD and culling 30% of deer within culling zones was most cost-effective in terms of disease prevention. In Chapter Four, I synthesize the results of the preceding chapters and discuss options for CWD management in Indiana.</p>

A study was conducted to determine what impact high white-tailed deer (Odocoileus virginianus) densities were having on the native grasslands of a tallgrass: aspen forest tract embedded within an agro-urban setting. Due to excessive spring moisture, row-crops were unavailable the first year. Using microhistological fecal analysis to determine dietary composition, deer were assessed to be placing the site’s favoured native plant species at risk of extirpation. Measuring woody stem abundance and height along and near the prairie: forest ecotone, deer were found to restructure woody growth but not directly influence encroachment rates. Indirectly, however, deer facilitated forest encroachment and prairie degradation through seed dispersal, nitrogen deposition, gap-dynamics, and trampling. Comparing dietary composition to nutritional data, deer grazed to maximize fitness, selecting foods high in IVDMD, minimizing energy expenditure, and optimizing CP. High crop CP was offset by intensively grazing particular native plants. ADF was an effective nutritional marker, not AIA.

A study was conducted to determine what impact high white-tailed deer (Odocoileus virginianus) densities were having on the native grasslands of a tallgrass: aspen forest tract embedded within an agro-urban setting. Due to excessive spring moisture, row-crops were unavailable the first year. Using microhistological fecal analysis to determine dietary composition, deer were assessed to be placing the site’s favoured native plant species at risk of extirpation. Measuring woody stem abundance and height along and near the prairie: forest ecotone, deer were found to restructure woody growth but not directly influence encroachment rates. Indirectly, however, deer facilitated forest encroachment and prairie degradation through seed dispersal, nitrogen deposition, gap-dynamics, and trampling. Comparing dietary composition to nutritional data, deer grazed to maximize fitness, selecting foods high in IVDMD, minimizing energy expenditure, and optimizing CP. High crop CP was offset by intensively grazing particular native plants. ADF was an effective nutritional marker, not AIA.

本篇論文將厚尾分配（Heavy-Tailed Distribution）應用在財務及保險精算上。本研究主要有三個部分：第一部份是用厚尾分配來重新建構Lee-Carter模型（1992），發現改良後的Lee-Carter模型其配適與預測效果都較準確。第二部分是將厚尾分配建構於具有世代因子（Cohort Factor）的Renshaw and Haberman模型（2006）中，其配適及預測效果皆有顯著改善，此外，針對英格蘭及威爾斯（England and Wales）訂價長壽交換（Longevity Swaps），結果顯示此模型可以支付較少的長壽交換之保費以及避免低估損失準備金。第三部分是財務上的應用，利用Schmidt等人（2006）提出的多元仿射廣義雙曲線分配（Multivariate Affine Generalized Hyperbolic Distributions; MAGH）於Boyle等人（2003）提出的低偏差網狀法（Low Discrepancy Mesh; LDM）來定價多維度的百慕達選擇權。理論上，LDM法的數值會高於Longstaff and Schwartz（2001）提出的最小平方法（Least Square Method; LSM）的數值，而數值分析結果皆一致顯示此性質，藉由此特性，我們可知道多維度之百慕達選擇權的真值落於此範圍之間。 / The thesis focus on the application of heavy-tailed distributions in finance and actuarial science. We provide three applications in this thesis. The first application is that we refine the Lee-Carter model (1992) with heavy-tailed distributions. The results show that the Lee-Carter model with heavy-tailed distributions provide better fitting and prediction. The second application is that we also model the error term of Renshaw and Haberman model (2006) using heavy-tailed distributions and provide an iterative fitting algorithm to generate maximum likelihood estimates under the Cox regression model. Using the RH model with non-Gaussian innovations can pay lower premiums of longevity swaps and avoid the underestimation of loss reserves for England and Wales. The third application is that we use multivariate affine generalized hyperbolic (MAGH) distributions introduced by Schmidt et al. (2006) and low discrepancy mesh (LDM) method introduced by Boyle et al. (2003), to show how to price multidimensional Bermudan derivatives. In addition, the LDM estimates are higher than the corresponding estimates from the Least Square Method (LSM) of Longstaff and Schwartz (2001). This is consistent with the property that the LDM estimate is high bias while the LSM estimate is low bias. This property also ensures that the true option value will lie between these two bounds.

In this thesis a number of statistical methods have been developed and applied to habitat suitability modelling for rare and threatened species. Data available on these species are typically limited. Therefore, developing these models from these data can be problematic and may produce prediction biases. To address these problems there are three aims of this thesis. The _rst aim is to develop and implement frequentist and Bayesian statistical modelling approaches for these types of data. The second aim is develop and implement expert elicitation methods. The third aim is to apply these novel approaches to Australian rare and threatened species case studies with the intention of habitat suitability modelling. The _rst aim is ful_lled by investigating two innovative approaches for habitat suitability modelling and sensitivity analysis of the second approach to priors. The _rst approach is a new multilevel framework developed to model the species distribution at multiple scales and identify excess zeros (absences outside the species range). Applying a statistical modelling approach to the identi_cation of excess zeros has not previously been conducted. The second approach is an extension and application of Bayesian classi_cation trees to modelling the habitat suitability of a threatened species. This is the _rst `real' application of this approach in ecology. Lastly, sensitivity analysis of the priors in Bayesian classi_cation trees are examined for a real case study. Previously, sensitivity analysis of this approach to priors has not been examined. To address the second aim, expert elicitation methods are developed, extended and compared in this thesis. In particular, one elicitation approach is extended from previous research, there is a comparison of three elicitation methods, and one new elicitation approach is proposed. These approaches are illustrated for habitat suitability modelling of a rare species and the opinions of one or two experts are elicited. The _rst approach utilises a simple questionnaire, in which expert opinion is elicited on whether increasing values of a covariate either increases, decreases or does not substantively impact on a response. This approach is extended to express this information as a mixture of three normally distributed prior distributions, which are then combined with available presence/absence data in a logistic regression. This is one of the _rst elicitation approaches within the habitat suitability modelling literature that is appropriate for experts with limited statistical knowledge and can be used to elicit information from single or multiple experts. Three relatively new approaches to eliciting expert knowledge in a form suitable for Bayesian logistic regression are compared, one of which is the questionnaire approach. Included in this comparison of three elicitation methods are a summary of the advantages and disadvantages of these three methods, the results from elicitations and comparison of the prior and posterior distributions. An expert elicitation approach is developed for classi_cation trees, in which the size and structure of the tree is elicited. There have been numerous elicitation approaches proposed for logistic regression, however no approaches have been suggested for classi_cation trees. The last aim of this thesis is addressed in all chapters, since the statistical approaches proposed and extended in this thesis have been applied to real case studies. Two case studies have been examined in this thesis. The _rst is the rare native Australian thistle (Stemmacantha australis), in which the dataset contains a large number of absences distributed over the majority of Queensland, and a small number of presence sites that are only within South-East Queensland. This case study motivated the multilevel modelling framework. The second case study is the threatened Australian brush-tailed rock-wallaby (Petrogale penicillata). The application and sensitivity analysis of Bayesian classi_cation trees, and all expert elicitation approaches investigated in this thesis are applied to this case study. This work has several implications for conservation and management of rare and threatened species. Novel statistical approaches addressing the _rst aim provide extensions to currently existing methods, or propose a new approach, for identi _cation of current and potential habitat. We demonstrate that better model predictions can be achieved using each method, compared to standard techniques. Elicitation approaches addressing the second aim ensure expert knowledge in various forms can be harnessed for habitat modelling, a particular bene_t for rare and threatened species which typically have limited data. Throughout, innovations in statistical methodology are both motivated and illustrated via habitat modelling for two rare and threatened species: the native thistle Stemmacantha australis and the brush-tailed rock wallaby Petrogale penicillata.

No description available.