944 resultados para Hazard-Based Models


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1. Model-based approaches have been used increasingly in conservation biology over recent years. Species presence data used for predictive species distribution modelling are abundant in natural history collections, whereas reliable absence data are sparse, most notably for vagrant species such as butterflies and snakes. As predictive methods such as generalized linear models (GLM) require absence data, various strategies have been proposed to select pseudo-absence data. However, only a few studies exist that compare different approaches to generating these pseudo-absence data. 2. Natural history collection data are usually available for long periods of time (decades or even centuries), thus allowing historical considerations. However, this historical dimension has rarely been assessed in studies of species distribution, although there is great potential for understanding current patterns, i.e. the past is the key to the present. 3. We used GLM to model the distributions of three 'target' butterfly species, Melitaea didyma, Coenonympha tullia and Maculinea teleius, in Switzerland. We developed and compared four strategies for defining pools of pseudo-absence data and applied them to natural history collection data from the last 10, 30 and 100 years. Pools included: (i) sites without target species records; (ii) sites where butterfly species other than the target species were present; (iii) sites without butterfly species but with habitat characteristics similar to those required by the target species; and (iv) a combination of the second and third strategies. Models were evaluated and compared by the total deviance explained, the maximized Kappa and the area under the curve (AUC). 4. Among the four strategies, model performance was best for strategy 3. Contrary to expectations, strategy 2 resulted in even lower model performance compared with models with pseudo-absence data simulated totally at random (strategy 1). 5. Independent of the strategy model, performance was enhanced when sites with historical species presence data were not considered as pseudo-absence data. Therefore, the combination of strategy 3 with species records from the last 100 years achieved the highest model performance. 6. Synthesis and applications. The protection of suitable habitat for species survival or reintroduction in rapidly changing landscapes is a high priority among conservationists. Model-based approaches offer planning authorities the possibility of delimiting priority areas for species detection or habitat protection. The performance of these models can be enhanced by fitting them with pseudo-absence data relying on large archives of natural history collection species presence data rather than using randomly sampled pseudo-absence data.

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We propose an alternative approach to obtaining a permanent equilibrium exchange rate (PEER), based on an unobserved components (UC) model. This approach offers a number of advantages over the conventional cointegration-based PEER. Firstly, we do not rely on the prerequisite that cointegration has to be found between the real exchange rate and macroeconomic fundamentals to obtain non-spurious long-run relationships and the PEER. Secondly, the impact that the permanent and transitory components of the macroeconomic fundamentals have on the real exchange rate can be modelled separately in the UC model. This is important for variables where the long and short-run effects may drive the real exchange rate in opposite directions, such as the relative government expenditure ratio. We also demonstrate that our proposed exchange rate models have good out-of sample forecasting properties. Our approach would be a useful technique for central banks to estimate the equilibrium exchange rate and to forecast the long-run movements of the exchange rate.

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El projecte exposat té com a propòsit definir i implementar un model de simulació basat en la coordinació i assignació dels serveis d’emergència en accidents de trànsit. La definició del model s’ha realitzat amb l’ús de les Xarxes de Petri Acolorides i la implementació amb el software Rockwell Arena 7.0. El modelatge de la primera simulació ens mostra un model teòric basat en cues mentre que el segon, mostra un model més complet i real gràcies a la connexió mitjançant la plataforma Corba a una base de dades amb informació geogràfica de les flotes i de les rutes. Com a resultat de l’estudi i amb l’ajuda de GoogleEarth, podem realitzar simulacions gràfiques per veure els accidents generats, les flotes dels serveis i el moviment dels vehicles des de les bases fins als accidents.

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Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.

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In many moral hazard problems, the principal evaluates the agent's performance based on signals which the agent may suppress and replace with counterfeits. This form of fraud may affect the design of optimal contracts drastically, leading to complete market failure in extreme cases. I show that in optimal contracts, the principal deters all fraud, and does so by two complementary mechanisms. First, the principal punishes signals that are suspicious, i.e. appear counterfeit. Second, the principal is lenient on bad signals that the agent could suppress, but does not.

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Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

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This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.

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Immunology-based interventions have been proposed as a promising curative chance to effectively attack postoperative minimal residual disease and distant metastatic localizations of prostate tumors. We developed a chimeric antigen receptor (CAR) construct targeting the human prostate-specific membrane antigen (hPSMA), based on a novel and high affinity specific mAb. As a transfer method, we employed last-generation lentiviral vectors (LV) carrying a synthetic bidirectional promoter capable of robust and coordinated expression of the CAR molecule, and a bioluminescent reporter gene to allow the tracking of transgenic T cells after in vivo adoptive transfer. Overall, we demonstrated that CAR-expressing LV efficiently transduced short-term activated PBMC, which in turn were readily stimulated to produce cytokines and to exert a relevant cytotoxic activity by engagement with PSMA+ prostate tumor cells. Upon in vivo transfer in tumor-bearing mice, CAR-transduced T cells were capable to completely eradicate a disseminated neoplasia in the majority of treated animals, thus supporting the translation of such approach in the clinical setting.

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Computational modeling has become a widely used tool for unraveling the mechanisms of higher level cooperative cell behavior during vascular morphogenesis. However, experimenting with published simulation models or adding new assumptions to those models can be daunting for novice and even for experienced computational scientists. Here, we present a step-by-step, practical tutorial for building cell-based simulations of vascular morphogenesis using the Tissue Simulation Toolkit (TST). The TST is a freely available, open-source C++ library for developing simulations with the two-dimensional cellular Potts model, a stochastic, agent-based framework to simulate collective cell behavior. We will show the basic use of the TST to simulate and experiment with published simulations of vascular network formation. Then, we will present step-by-step instructions and explanations for building a recent simulation model of tumor angiogenesis. Demonstrated mechanisms include cell-cell adhesion, chemotaxis, cell elongation, haptotaxis, and haptokinesis.

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BACKGROUND AND STUDY AIMS: Various screening methods for colorectal cancer (CRC) are promoted by professional societies; however, few data are available about the factors that determine patient participation in screening, which is crucial to the success of population-based programs. This study aimed (i) to identify factors that determine acceptance of screening and preference of screening method, and (ii) to evaluate procedure success, detection of colorectal neoplasia, and patient satisfaction with screening colonoscopy. PATIENTS AND METHODS: Following a public awareness campaign, the population aged 50 - 80 years was offered CRC screening in the form of annual fecal occult blood tests, flexible sigmoidoscopy, a combination of both, or colonoscopy. RESULTS: 2731 asymptomatic persons (12.0 % of the target population) registered with and were eligible to take part in the screening program. Access to information and a positive attitude to screening were major determinants of participation. Colonoscopy was the method preferred by 74.8 % of participants. Advanced colorectal neoplasia was present in 8.5 %; its prevalence was higher in males and increased with age. Significant complications occurred in 0.5 % of those undergoing colonoscopy and were associated with polypectomy or sedation. Most patients were satisfied with colonoscopy and over 90 % would choose it again for CRC screening. CONCLUSIONS: In this population-based study, only a small proportion of the target population underwent CRC screening despite an extensive information campaign. Colonoscopy was the preferred method and was safe. The determinants of participation in screening and preference of screening method, together with the distribution of colorectal neoplasia in different demographic categories, provide a rationale for improving screening procedures.

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We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.

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When actuaries face with the problem of pricing an insurance contract that contains different types of coverage, such as a motor insurance or homeowner's insurance policy, they usually assume that types of claim are independent. However, this assumption may not be realistic: several studies have shown that there is a positive correlation between types of claim. Here we introduce different regression models in order to relax the independence assumption, including zero-inflated models to account for excess of zeros and overdispersion. These models have been largely ignored to multivariate Poisson date, mainly because of their computational di±culties. Bayesian inference based on MCMC helps to solve this problem (and also lets us derive, for several quantities of interest, posterior summaries to account for uncertainty). Finally, these models are applied to an automobile insurance claims database with three different types of claims. We analyse the consequences for pure and loaded premiums when the independence assumption is relaxed by using different multivariate Poisson regression models and their zero-inflated versions.

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Objectives: Several population pharmacokinetic (PPK) and pharmacokinetic-pharmacodynamic (PK-PD) analyses have been performed with the anticancer drug imatinib. Inspired by the approach of meta-analysis, we aimed to compare and combine results from published studies in a useful way - in particular for improving the clinical interpretation of imatinib concentration measurements in the scope of therapeutic drug monitoring (TDM). Methods: Original PPK analyses and PK-PD studies (PK surrogate: trough concentration Cmin; PD outcomes: optimal early response and specific adverse events) were searched systematically on MEDLINE. From each identified PPK model, a predicted concentration distribution under standard dosage was derived through 1000 simulations (NONMEM), after standardizing model parameters to common covariates. A "reference range" was calculated from pooled simulated concentrations in a semi-quantitative approach (without specific weighting) over the whole dosing interval. Meta-regression summarized relationships between Cmin and optimal/suboptimal early treatment response. Results: 9 PPK models and 6 relevant PK-PD reports in CML patients were identified. Model-based predicted median Cmin ranged from 555 to 1388 ng/ml (grand median: 870 ng/ml and inter-quartile range: 520-1390 ng/ml). The probability to achieve optimal early response was predicted to increase from 60 to 85% from 520 to 1390 ng/ml across PK-PD studies (odds ratio for doubling Cmin: 2.7). Reporting of specific adverse events was too heterogeneous to perform a regression analysis. The general frequency of anemia, rash and fluid retention increased however consistently with Cmin, but less than response probability. Conclusions: Predicted drug exposure may differ substantially between various PPK analyses. In this review, heterogeneity was mainly attributed to 2 "outlying" models. The established reference range seems to cover the range where both good efficacy and acceptable tolerance are expected for most patients. TDM guided dose adjustment appears therefore justified for imatinib in CML patients. Its usefulness remains now to be prospectively validated in a randomized trial.

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Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.

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BACKGROUND: Among young people, about one in three females and one in five males report experiencing emotional distress but 65-95% of them do not receive help from health professionals. AIM: To assess the differences among young people who seek help and those who do not seek help for their psychological problems, considering the frequency of consultations to their GP and their social resources. DESIGN OF STUDY: School survey. SETTING: Post-mandatory school. METHOD: Among a Swiss national representative sample of 7429 students and apprentices (45.6% females) aged 16-20 years, 1931 young people reported needing help for a problem of depression/sadness (26%) and were included in the study. They were divided into those who sought help (n = 256) and those who did not (n = 1675), and differences between them were assessed. RESULTS: Only 13% of young people needing help for psychological problems consulted for that reason and this rate was positively associated with the frequency of consultations to the GP. However, 80% of young people who did not consult for psychological problems visited their GP at least once during the previous year. Being older or a student, having a higher depression score, or a history of suicide attempt were linked with a higher rate of help seeking. Moreover, confiding in adults positively influenced the rate of help seeking. CONCLUSION: The large majority of young people reporting psychological problems do not seek help, although they regularly consult their GP. While young people have difficulties in tackling issues about mental health, GPs could improve the situation by systematically inquiring about this issue.