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3D vessel extraction has great significance in the diagnosis of vascular diseases. However, accurate extraction of vessels from computed tomography angiography (CTA) data is challenging. For one thing, vessels in different body parts have a wide range of scales and large curvatures; for another, the intensity distributions of vessels in different CTA data vary considerably. Besides, surrounding interfering tissue, like bones or veins with similar intensity, also seriously affects vessel extraction. Considering all the above imaging and structural features of vessels, we propose a new scale-adaptive hybrid parametric tracker (SAHPT) to extract arbitrary vessels of different body parts. First, a geometry-intensity parametric model is constructed to calculate the geometry-intensity response. While geometry parameters are calculated to adapt to the variation in scale, intensity parameters can also be estimated to meet non-uniform intensity distributions. Then, a gradient parametric model is proposed to calculate the gradient response based on a multiscale symmetric normalized gradient filter which can effectively separate the target vessel from surrounding interfering tissue. Last, a hybrid parametric model that combines the geometry-intensity and gradient parametric models is constructed to evaluate how well it fits a local image patch. In the extraction process, a multipath spherical sampling strategy is used to solve the problem of anatomical complexity. We have conducted many quantitative experiments using the synthetic and clinical CTA data, asserting its superior performance compared to traditional or deep learning-based baselines.

It is crucial to accurately predict the probability distribution of long-term wind speed patterns to evaluate the potential for wind energy. This could involve testing various probability density models to ensure they correctly match the wind speed (WS) characteristics provided. Parametric models have prior distributional assumptions that limit their flexibility and are unsuited for skewed, uni-modal, or multimodal wind regimes. Nonparametric kernel density estimation (KDE) is a data-driven model free from prior distribution assumptions. This study offers a thorough approach to modelling the probability density of hourly WS observations covering 11 locations in Tamil Nadu state in India. The efficacy of nonparametric Gaussian KDE with six bandwidth selectors was examined in modelling WS probability distribution. Additionally, both 1-component and 2-component mixture models are fitted to WS via the maximum likelihood estimation (MLE), the L-moment method (LMOM), and the expectation–maximization approach. The performance of some standard kernel functions, BOX, Epanecknokov, Triweight and Biweight, fitted with the direct-plug-in (DPI) method, are also compared. The model performance of all candidate models is examined thoroughly by employing different goodness-of-fit test measures. Investigation reveals that nonparametric KDE with an unbiased cross-validation approach outperformed all other nonparametric and parametric, uni- and bi-modal distributions for all the stations, except at Kanchipuram. The Gaussian KDE with Silverman rule-of-thumb best fits station Kanchipuram. Also, the best-fitted parametric model, among the 1-component model, outperformed the 2-component mixture models for all selected stations. When comparing the performance of some other kernel densities with DPI bandwidth selectors, it performed better than all parametric models. The hourly WS observation in this case study does not favour any fitted mixture models compared to nonparametric KDE density and 1-component density. Each station's selected model is employed further in estimating non-exceedance probabilities and return periods (RPs). Finally, the design WS quantiles are estimated at different univariate RPs (1, 2, 3, 5, 10, 15, 20, 30, 40, 50, 70, 80, 100 years) for all selected stations.

In historical biogeography, phylogenetic trees have long been used as tools for addressing a wide range of inference problems, from explaining common distribution patterns of species to reconstructing ancestral geographic ranges on branches of the tree of life. However, the potential utility of phylogenies for this purpose has yet to be fully realized, due in part to a lack of explicit conceptual links between processes underlying the evolution of geographic ranges and processes of phylogenetic tree growth. We suggest that statistical approaches that use parametric models to forge such links will stimulate integration and propel hypothesis-driven biogeographical inquiry in new directions. We highlight here two such approaches and describe how they represent early steps towards a more general framework for model-based historical biogeography that is based on likelihood as an optimality criterion, rather than having the traditional reliance on parsimony. The development of this framework will not be without significant challenges, particularly in balancing model complexity with statistical power, and these will be most apparent in studies of regions with many component areas and complex geological histories, such as the Mediterranean Basin.

Geographical indications (GIs) convey information about the place of production as a proxy for the attributes of agricultural products. We define the informational content of the GI proxy as its capacity to describe the tangible characteristics of production sites, instead of random noise or intangible factors from political bargaining about designation (i.e., lobbying effects). We estimate econometrically the informational content of wine‐related GIs for the Côte d'Or region of Burgundy, France. We show that GIs signal vineyard attributes with high precision, while we find some persistent bias from lobbying effects. We also study alternative classifications, from history and from simulations, which reveal a significant increase in the informational content of GIs over the last hundred years or so, and provide guidelines for better designated GIs in the future.

Background: This study explores the determinants impacting the mortality risk of COVID-19 patients following hospitalisation within South Africa’s Limpopo province. Methods: Utilising a dataset comprising 388 patients, the investigation employs a frailty regression model to evaluate the influence of diverse characteristics on mortality outcomes, contrasting its performance against other parametric models based on loglikelihood measures. Results: The findings underscore diabetes and hypertension as notable contributors to heightened mortality rates, underscoring the urgency of effectively managing these comorbidities to optimise patient well-being. Additionally, regional discrepancies come to the fore, with the Capricorn district demonstrating elevated mortality risks, thereby accentuating the necessity for precisely targeted interventions. Medical interventions, particularly ventilation, emerge as pivotal factors in mitigating mortality risk. Gender-based distinctions in mortality patterns also underscore the need for bespoke patient care strategies. Conclusions: Collectively, these outcomes supply practical insights with implications for healthcare interventions, policy formulation, and clinical strategies aimed at ameliorating COVID-19 mortality risk among individuals discharged from hospitals within South Africa’s Limpopo province.

In the analysis of semi-competing risks data interest lies in estimation and inference with respect to a so-called non-terminal event, the observation of which is subject to a terminal event. Multi-state models are commonly used to analyse such data, with covariate effects on the transition/intensity functions typically specified via the Cox model and dependence between the non-terminal and terminal events specified, in part, by a unit-specific shared frailty term. To ensure identifiability, the frailties are typically assumed to arise from a parametric distribution, specifically a Gamma distribution with mean 1.0 and variance, say, σ2. When the frailty distribution is misspecified, however, the resulting estimator is not guaranteed to be consistent, with the extent of asymptotic bias depending on the discrepancy between the assumed and true frailty distributions. In this paper, we propose a novel class of transformation models for semi-competing risks analysis that permit the non-parametric specification of the frailty distribution. To ensure identifiability, the class restricts to parametric specifications of the transformation and the error distribution; the latter are flexible, however, and cover a broad range of possible specifications. We also derive the semi-parametric efficient score under the complete data setting and propose a non-parametric score imputation method to handle right censoring; consistency and asymptotic normality of the resulting estimators is derived and small-sample operating characteristics evaluated via simulation. Although the proposed semi-parametric transformation model and non-parametric score imputation method are motivated by the analysis of semi-competing risks data, they are broadly applicable to any analysis of multivariate time-to-event outcomes in which a unit-specific shared frailty is used to account for correlation. Finally, the proposed model and estimation procedures are applied to a study of hospital readmission among patients diagnosed with pancreatic cancer.

We derive rates of contraction of posterior distributions on non-parametric models resulting from sieve priors. The aim of the study was to provide general conditions to get posterior rates when the parameter space has a general structure, and rate adaptation when the parameter space is, for example, a Sobolev class. The conditions employed, although standard in the literature, are combined in a different way. The results are applied to density, regression, nonlinear autoregression and Gaussian white noise models. In the latter we have also considered a loss function which is different from the usual l2 norm, namely the pointwise loss. In this case it is possible to prove that the adaptive Bayesian approach for the l2 loss is strongly suboptimal and we provide a lower bound on the rate.

Microseismic (MS) information is often utilised in deep underground engineering projects for the early warning of short-term rockburst hazards. Due to the complex nature of rockburst occurrence, predicting short-term rockburst is always challenging. Recently, machine learning (ML) methods are often employing in different geotechnical engineering applications. Parametric and non-parametric ML methods are two different kinds of approaches, each with distinct characteristics. However, the current applications in short-term rockburst prediction are focused on non-parametric methods. Therefore, this paper proposes and studies the feasibility of a parametric model over the non-parametric model, adopting two fundamental parametric and non-parametric ML models, including logistic regression and support vector machine, to predict short-term rockburst using MS information based on two types of normally and non-normally distributed datasets. After modelling, precision, recall, F1 score, and receiving operating curve are considered to evaluate the model’s strength in predicting tasks. The results indicate that the parametric model, which obtained an average F1 score and AUC score of 0.72 and 0.91 on a normally distributed dataset achieved more remarkable output in evaluating short-term rockburst risk. Limited data availability is always a challenge in short-term rockburst prediction. In such cases, parametric models can accurately classify the rockburst risk levels due to their characteristics of assuming the predefined function, simplifying the learning processes independent of the data size. However, normally distributed data is beneficial for them that allows a perfect fit. The presented work effectively identifies the rockburst risk in deep underground excavation projects regardless of data size.

Partial linear models have been widely used as flexible method for modelling linear components in conjunction with non-parametric ones. Despite the presence of the non-parametric part, the linear, parametric part can under certain conditions be estimated with parametric rate. In this paper, we consider a high-dimensional linear part. We show that it can be estimated with oracle rates, using the least absolute shrinkage and selection operator penalty for the linear part and a smoothness penalty for the nonparametric part.

This paper deals with the problem of predicting the real-valued response variable using explanatory variables containing both multivariate random variable and random curve. The proposed functional partial linear single-index model treats the multivariate random variable as linear part and the random curve as functional single-index part, respectively. To estimate the non-parametric link function, the functional single-index and the parameters in the linear part, a two-stage estimation procedure is proposed. Compared with existing semi-parametric methods, the proposed approach requires no initial estimation and iteration. Asymptotical properties are established for both the parameters in the linear part and the functional single-index. The convergence rate for the non-parametric link function is also given. In addition, asymptotical normality of the error variance is obtained that facilitates the construction of confidence region and hypothesis testing for the unknown parameter. Numerical experiments including simulation studies and a real-data analysis are conducted to evaluate the empirical performance of the proposed method.