1,721,015 research outputs found
Nonparametric methods for complex spatial domains: density estimation and hypothesis testing
Full text linkThe analysis of not only big, but increasingly complex data represents a thriving branch of statistics. Modern applications ranging from neuroscience, geo-sciences, astronomy and engineering pose stimulating challenges to classical statistics and require the development of novel methodologies. In this thesis we propose nonparametric approaches to density estimation and hypothesis testing over multidimensional domains with complex shapes. The synergy of ideas and techniques from applied mathematics, numerical analysis and statistics allows us to obtain flexible and efficient tools. The thesis is organized in three main threads. The first considers the problem of density estimation over multidimensional domains with complex shapes. Here we combine a nonparametric likelihood approach with a regularization involving partial differential operators. The second thread examines two sample hypothesis testing. Inspired by the first part, we take advantage of permutation procedures to develop high dimensional
multinomial tests for distributions defined over complex domain. The last thread moves toward a parallel direction, that is the study of hypothesis testing procedures for semiparametric spatial regression models. After a careful analysis of their theoretical properties, we propose a nonparametric randomization approach to test the linear
components of such models
Nonparametric test for density modes
No full textA nonparametric resampling procedure is proposed to test the significance of a mode, with the aim of evaluating whether a region of relatively high observed density reflects the actual presence of a mode in the true distribution underlying a set of data. The method leverages on Morse theory and stochastic gradient methods to characterize the local properties of the modes. This allows the definition of an asymptotic test, based on the concept of gradient ascent paths and relying on resampling methods, to approximate the distribution of the test statistic under the null hypothesis
Modal clustering of matrix-variate data
Full text linkThe nonparametric formulation of density-based clustering, known as modal clustering, draws a correspondence between groups and the attraction domains of the modes of the density function underlying the data. Its probabilistic foundation allows for a natural, yet not trivial, generalization of the approach to the matrix-valued setting, increasingly widespread, for example, in longitudinal and multivariate spatio-temporal studies. In this work we introduce nonparametric estimators of matrix-variate distributions based on kernel methods, and analyze their asymptotic properties. Additionally, we propose a generalization of the mean-shift procedure for the identification of the modes of the estimated density. Given the intrinsic high dimensionality of matrix-variate data, we discuss some locally adaptive solutions to handle the problem. We test the procedure via extensive simulations, also with respect to some competitors, and illustrate its performance through two high-dimensional real data applications
A spatial epidemic model with contact and mobility restrictions
No full textSeveral spatiotemporal epidemic models have described how contact and mobility restrictions have a dynamic effect on morbidity and mortality of fast transmitting pathogens in epidemics. Despite this, there have been rather limited contributions looking at policy optimization. This work combines a new spatiotemporal epidemic model of a heterogeneous mixed population located at different places with an optimal control approach to show the effects of contact and mobility restrictions under policy optimization. The objective of optimization not only includes epidemiological but also socio-economic implications of the restrictions. Several scenarios are numerically investigated, and the dependence of the optimal policy on some basic epidemiological parameters is analysed. The results illustrate the strong impact of spatial heterogeneity on optimal policy measures. An analysis of the stability of the disease-free equilibrium of the model is also presented
Demographic characteristics associated with West Nile virus neuroinvasive disease – A retrospective study on the wider European area 2006–2021
Full text linkBackgroundWith a case-fatality-risk ranging from 3.0 to >20.0% and life-long sequelae, West Nile neuroinvasive disease (WNND) is the most dangerous outcome of West Nile virus (WNV) infection in humans. As no specific prophylaxis nor therapy is available for these infections, focus is on preventive strategies. We aimed to find variables associated with WNND diagnosis, hospitalisation or death, to identify high-risk sub-groups of the population, on whom to concentrate these strategies.MethodsWe used data from The European Surveillance System-TESSy, provided by National Public Health Authorities, and released by the European Centre for Disease Prevention and Control (ECDC). In two Firth-penalised logistic regression models, we considered age, sex, clinical criteria, epidemiological link to other cases (epi-link), calendar year, and season as potential associated variables. In one model we considered also the rural/urban classification of the place of infection (RUC), while in the other the specific reporting country.FindingsAmong confirmed West Nile Virus cases, 2,916 WNND cases were registered, of which 2,081 (71.4%), and 383 (13.1%) resulted in the hospitalisation and death of the patient, respectively. Calendar year, RUC/country, age, sex, clinical criteria, and epi-link were associated with WNND diagnosis. Hospitalisation was associated with calendar year and RUC/country; whereas death was associated with age, sex and country.InterpretationOur results support previous findings on WNND associated variables (most notably age and sex); while by observing the whole population of WNND cases in the considered area and period, they also allow for stronger generalizations, conversely to the majority of previous studies, which used sample populations
An adaptive functional regression framework for locally heterogeneous signals in spectroscopy
No full textIn recent years, there has been growing attention towards food nutritional properties, traceability, and production systems prioritizing environmental sustainability. Consequently, there is a rising demand for tools evaluating food quality and authenticity, with mid-infrared (MIR) spectroscopy techniques playing a pivotal role to collect vast amounts of data. These data pose some challenges that existing methods struggle to address, thus necessitating the development of new statistical techniques. We introduce an adaptive functional regression framework allowing for the definition of a flexible estimator accommodating different degrees of smoothness. We provide an optimization procedure handling both Gaussian and non-Gaussian responses, and allowing for the inclusion of scalar covariates. Our proposal is applied to MIR spectroscopy data, providing excellent performances when predicting milk composition and cows' dietary regimens. Furthermore, the developed inferential routine enhances the interpretability of the results, providing valuable insights leading to a deeper understanding of the relation between specific wavenumbers and milk characteristics
Host selection and forage ratio in West Nile virus–transmitting Culex mosquitoes: Challenges and knowledge gaps
Full text linkBACKGROUND: To date, no specific therapy or vaccination is available for West Nile virus (WNV) infections in humans; preventive strategies represent the only possibility to control transmission. To focus these strategies, detailed knowledge of the virus dynamics is of paramount importance. However, several aspects of WNV transmission are still unclear, especially regarding the role of potential vertebrate host species. Whereas mosquitoes’ intrinsic characteristics cause them to favour certain hosts (host preference), absolute selection is impossible in natural settings. Conversely, the selection carried out among available hosts and influenced from hosts’ availability and other ecological/environmental factors is defined as host selection. METHODOLOGY/PRINCIPAL FINDINGS: In July 2022, we searched PubMed database for original articles exploring host selection among WNV-transmitting Culex mosquitoes, the main WNV vector. We considered only original field studies estimating and reporting forage ratio. This index results from the ratio between the proportion of blood meals taken by mosquitoes on potential host species and the hosts’ relative abundance. From the originally retrieved 585 articles, 9 matched the inclusion criteria and were included in this review. All but one of the included studies were conducted in the Americas, six in the United States, and one each in Mexico and Colombia. The remaining study was conducted in Italy. American Robin, Northern Cardinal, and House Finch were the most significantly preferred birds in the Americas, Common Blackbird in Italy. CONCLUSIONS/SIGNIFICANCE: Although ornithophilic, all observed WNV-transmitting mosquitoes presented opportunistic feeding behaviour. All the observed species showed potential to act as bridges for zoonotic diseases, feeding also on humans. All the observed mosquitoes presented host selection patterns and did not feed on hosts as expected by chance alone. The articles observe different species of mosquitoes in different environments. In addition, the way the relative host abundance was determined differed. Finally, this review is not systematic. Therefore, the translation of our results to different settings should be conducted cautiously
Efficient Parametric Tests in Semiparametric Regression with Differential Regularization
No full textIn this work we consider the problem of making inference on
the nonparametric component within a semiparametric regression model
with differential regularization. The parametric inference methods so far
introduced in the literature perform poorly, due to the variance mis-
specification induced by the penalization term. Nonparametric inference
procedures may instead be excessively computationally demanding. We
hereby propose two new parametric approaches, that are robust to the
effect of the penalization, while retaining a reduced computational cost.
The first method relies on an appropriate undersmoothing strategy, com-
bined with a bootstrap approach. The second one leverages instead on
the asymptotic properties of the scores of the model. The resulting tests
have better control of Type-I error, with respect to the existing alterna-
tives, and reduced computational cost. We apply the novel approaches
to the study chlorophyll-a concentrations in the Mediterranean sea
Urban-rural disparities in COVID-19 hospitalisations and mortality: A population-based study on national surveillance data from Germany and Italy
Full text link
Purpose
Recent literature has highlighted the overlapping contribution of demographic characteristics and spatial factors to urban-rural disparities in SARS-CoV-2 transmission and outcomes. Yet the interplay between individual characteristics, hospitalisation, and spatial factors for urban-rural disparities in COVID-19 mortality have received limited attention.
Methods
To fill this gap, we use national surveillance data collected by the European Centre for Disease Prevention and Control and we fit a generalized linear model to estimate the association between COVID-19 mortality and the individuals’ age, sex, hospitalisation status, population density, share of the population over the age of 60, and pandemic wave across urban, intermediate and rural territories.
Findings
We find that in what type of territory individuals live (urban-intermediate-rural) accounts for a significant difference in their probability of dying given SARS-COV-2 infection. Hospitalisation has a large and positive effect on the probability of dying given SARS-CoV-2 infection, but with a gradient across urban, intermediate and rural territories. For those living in rural areas, the risk of dying is lower than in urban areas but only if hospitalisation was not needed; while for those who were hospitalised in rural areas the risk of dying was higher than in urban areas.
Conclusions
Together with individuals’ demographic characteristics (notably age), hospitalisation has the largest effect on urban-rural disparities in COVID-19 mortality net of other individual and regional characteristics, including population density and the share of the population over 60.
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