On the problematic of reserving and stochastic loss models

Abstract Traditionally, the common reserving methods used by the non-life actuaries are based on the assumption that future claims are going to behave in the same way as they did in the past. There are two main sources of variability in the processus of development of the claims: the variability of the speed with which the claims are settled and the variability between the severity of the claims from different accident years. High changes in these processes will generate distortions in the estimation of the claims reserves. The main objective of this thesis is to provide an indicator which firstly identifies and quantifies these two influences and secondly to determine which model is adequate for a specific situation. Two stochastic models were analysed and the predictive distributions of the future claims were obtained. The main advantage of the stochastic models is that they provide measures of variability of the reserves estimates. The first model (PDM) combines one conjugate family Dirichlet - Multinomial with the Poisson distribution. The second model (NBDM) improves the first one by combining two conjugate families Poisson -Gamma (for distribution of the ultimate amounts) and Dirichlet Multinomial (for distribution of the incremental claims payments). It was found that the second model allows to find the speed variability in the reporting process and development of the claims severity as function of two above mentioned distributions' parameters. These are the shape parameter of the Gamma distribution and the Dirichlet parameter. Depending on the relation between them we can decide on the adequacy of the claims reserve estimation method. The parameters have been estimated by the Methods of Moments and Maximum Likelihood. The results were tested using chosen simulation data and then using real data originating from the three lines of business: Property/Casualty, General Liability, and Accident Insurance. These data include different developments and specificities. The outcome of the thesis shows that when the Dirichlet parameter is greater than the shape parameter of the Gamma, resulting in a model with positive correlation between the past and future claims payments, suggests the Chain-Ladder method as appropriate for the claims reserve estimation. In terms of claims reserves, if the cumulated payments are high the positive correlation will imply high expectations for the future payments resulting in high claims reserves estimates. The negative correlation appears when the Dirichlet parameter is lower than the shape parameter of the Gamma, meaning low expected future payments for the same high observed cumulated payments. This corresponds to the situation when claims are reported rapidly and fewer claims remain expected subsequently. The extreme case appears in the situation when all claims are reported at the same time leading to expectations for the future payments of zero or equal to the aggregated amount of the ultimate paid claims. For this latter case, the Chain-Ladder is not recommended.

Essays on the treatment of cash flows under stochastic interest rates

Abstract This paper shows how to calculate recursively the moments of the accumulated and discounted value of cash flows when the instantaneous rates of return follow a conditional ARMA process with normally distributed innovations. We investigate various moment based approaches to approximate the distribution of the accumulated value of cash flows and we assess their performance through stochastic Monte-Carlo simulations. We discuss the potential use in insurance and especially in the context of Asset-Liability Management of pension funds.

To what extent does adding tobacco to cannabis expose young users to nicotine?

INTRODUCTION: To determine if mulling, the process of adding tobacco to cannabis for its consumption, exposes young cannabis users to significant levels of nicotine. METHODS: This observational study performed in 2009-2010 among Swiss youths aged 16-25 years involved the completion of a self-administrated questionnaire and the collection of a urine sample on the same day. Measures of urinary cotinine were blindly performed using liquid chromatography coupled-mass spectrometry. A total of 197 eligible participants were divided in 3 groups based on their consumption profile in the past 5 days: 70 abstainers (ABS) not having used cigarettes or cannabis, 57 cannabis users adding tobacco to the cannabis they smoke (MUL) but not having smoked cigarettes, and 70 cigarette smokers (CIG) not having smoked cannabis. RESULTS: Exposure to nicotine was at its lowest among ABS with a mean (SE) cotinine level of 3.2 (1.4) ng/ml compared, respectively, with 214.6 (43.8) and 397.9 (57.4) for MUL and CIG (p < .001). While consumption profile appeared as the only significant factor of influence when examining nicotine exposure from the ABS and MUL participants on multivariate analysis, it did not result in substantial differences among MUL and CIG groups. CONCLUSIONS: Urinary cotinine levels found among MUL are high enough to indicate a significant exposure to nicotine originating from the mulling process. In line with our results, health professionals should pay attention to mulling as it is likely to influence cannabis and cigarette use as well as the efficacy of cessation interventions.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

Adding injury to insult: pathogen detection and responses.

Genomic approaches to the study of the expression of plant genes induced in response to disease and attack are now showing that there is an intimate association between pathogen perception and general stress detection.

The value of adding mirror therapy for upper limb motor recovery of subacute stroke patients: a randomized controlled trial.

BACKGROUND: Upper limb paresis remains a relevant challenge in stroke rehabilitation. AIM: To evaluate if adding mirror therapy (MT) to conventional therapy (CT) can improve motor recovery of the upper limb in subacute stroke patients. DESIGN: Prospective, single-center, single-blind, randomised, controlled trial. SETTING: Subacute stroke patients referred to a Physical and Rehabilitation Medicine Unit between October 2009 and August 2011. POPULATION: Twenty-six subacute stroke patients (time from stroke <4 weeks) with upper limb paresis (Motricity Index â0/00¤ 77). METHODS: Patients were randomly allocated to the MT (N.=13) or to the CT group (N.=13). Both followed a comprehensive rehabilitative treatment. In addition, MT Group had 30 minutes of MT while the CT group had 30 minutes of sham therapy. Action Research Arm Test (ARAT) was the primary outcome measures. Motricity Index (MI) and the Functional Independence Measure (FIM) were the secondary outcome measures. RESULTS: After one month of treatment patients of both groups showed statistically significant improvements in all the variables measured (P<0.05). Moreover patients of the MT group had greater improvements in the ARAT, MI and FIM values compared to CT group (P<0.01, Glass's Î" Effect Size: 1.18). No relevant adverse event was recorded during the study. CONCLUSION: MT is a promising and easy method to improve motor recovery of the upper limb in subacute stroke patients. CLINICAL REHABILITATION IMPACT: While MT use has been advocated for acute patients with no or negligible motor function, it can be usefully extended to patients who show partial motor recovery. The easiness of implementation, the low cost and the acceptability makes this therapy an useful tool in stroke rehabilitation.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Du capitalisme familial au capitalisme financier? : le cas de l'industrie suisse des machines, de l'électrotechnique et de la métallurgie au 20e siècle

L'objectif principal de cette thèse consiste à mettre en évidence la persistance du capitalisme familial en Suisse au cours du 20e siècle, et sa résistance aux capitalismes managérial et financier qui sont censés lui avoir succédé. Pour ce faire, nous avons retenu vingt-deux grandes entreprises du secteur des machines, de l'électrotechnique et de la métallurgie - principale branche de l'industrie suisse pour la période considérée -, pour lesquelles ont été recensés les membres des conseils d'administration et les principaux dirigeants exécutifs pour cinq dates- repère couvrant le siècle (1910, 1937, 1957, 1980 et 2000). Cette thèse s'inscrit dans une démarche pluridisciplinaire qui relève à la fois de l'histoire d'entreprise et de la sociologie des dirigeants, et fait appel à différentes méthodes telles que l'analyse de réseau et l'analyse prosopographique. Elle s'articule autour de trois axes de recherche principaux : le premier vise à mettre en évidence l'évolution des modes de gouvernance dans notre groupe d'entreprises, le second investit la question de la coordination patronale et le troisième a pour but de dresser un portrait collectif des élites à la tête de nos vingt-deux firmes. Nos résultats montrent que durant la majeure partie du siècle, la plupart de nos entreprises sont contrôlées par des familles et fonctionnent sur un mode de coordination hors marché qui repose notamment sur un réseau dense de liens interfirmes, le profil des dirigeants restant dans l'ensemble stable. Si la fin du siècle est marquée par plusieurs changements qui confirment l'avènement d'un capitalisme dit financier ou actionnarial et la mise en place de pratiques plus concurrentielles parmi les firmes et les élites industrielles, le maintien du contrôle familial dans plusieurs entreprises et la persistance de certains anciens mécanismes de coopération nous incitent cependant à nuancer ce constat. - The main objective of this research is to highlight the persistence of family capitalism in Switzerland during the 20th century and its resistance to managerial and financial capitalisms that succeeded. For this purpose, we focus on twenty- two big companies of the machine, electrotechnical and metallurgy sector - the main branch of the Swiss industry for the considered period - whose boards of directors and executive managers have been identified for five benchmarks across the century (1910, 1937, 1957, 1980 and 2000). This thesis relates to business history and elites sociology, and uses different methods such as network analysis and prosopography. It is articulated around three main parts. The aim of the first one is to identify the evolution of corporate governance in our twenty-two enterprises, the second part concentrates on interfirms coordination and the objective of the last one is to highlight the profile of the corporate elite leading our firms. Our results show that during the main part of the century, most of the companies were controlled by families and were characterized by non-market mechanisms of coordination such as interlocking directorates ; moreover, the profile of the corporate elite remained very stable. Although some major changes that took place by the end of the century confirmed a transition towards financial capitalism and more competitive interaction among firms and the corporate elite, the persistence of family control in several companies and the maintaining of some former mechanisms of coordination allow us to put this evolution into perspective.

An allele-specific, stochastic gene expression process controls the expression of multiple Ly49 family genes and generates a diverse, MHC-specific NK cell receptor repertoire.

Mouse NK cells express MHC class I-specific inhibitory Ly49 receptors. Since these receptors display distinct ligand specificities and are clonally distributed, their expression generates a diverse NK cell receptor repertoire specific for MHC class I molecules. We have previously found that the Dd (or Dk)-specific Ly49A receptor is usually expressed from a single allele. However, a small fraction of short-term NK cell clones expressed both Ly49A alleles, suggesting that the two Ly49A alleles are independently and randomly expressed. Here we show that the genes for two additional Ly49 receptors (Ly49C and Ly49G2) are also expressed in a (predominantly) mono-allelic fashion. Since single NK cells can co-express multiple Ly49 receptors, we also investigated whether mono-allelic expression from within the tightly linked Ly49 gene cluster is coordinate or independent. Our clonal analysis suggests that the expression of alleles of distinct Ly49 genes is not coordinate. Thus Ly49 alleles are apparently independently and randomly chosen for stable expression, a process that directly restricts the number of Ly49 receptors expressed per single NK cell. We propose that the Ly49 receptor repertoire specific for MHC class I is generated by an allele-specific, stochastic gene expression process that acts on the entire Ly49 gene cluster.

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.