Random Covariance Research Articles

Environmentally Adaptive Event-Driven Robust Cubature Kalman Filter for RSS-Based Targets Tracking in Mobile Wireless Sensor Network

This article presents an environmentally adaptive event-driven robust square root cubature Kalman filter (EAERCKF) for received-signal-strength-based (RSS-based) moving targets tracking in resource-constrained mobile wireless sensor network (MWSN) under the motion modeling and RSS quantization uncertainties. First, an environmentally adaptive event-driven mechanism, scheduling dynamically and approximately the desired amount of anchors regardless of the anchors distribution density near the moving target, is designed to reduce energy consumption, communication rates, and computation complexity of the MWSN system. Second, a robust square root cubature Kalman filter is developed to estimate the motion state of the moving target, where the covariances of the process noise and the measurement noise assumed to be time-varying uniform distributions are generated randomly. Third, the bounds of the uniformly distributed covariances describing the motion modeling and RSS quantization uncertainties are given to improve the stability, accuracy, and consistency of the estimator. Finally, the results of numerical simulations demonstrate that our proposed EAERCKF estimation method has superiority in simplifying the determination of the random noise covariances with satisfactory target tracking performance and approximating the desired number of triggered anchors.

Impact of modernization on oxidative stress among indigenous populations in northern Laos.

To explore the impact of modernization on oxidative stress during a momentous health transition process, we investigated differences in oxidative stress among the indigenous populations of villages in northern Laos with different levels of modernization. We conducted a cross-sectional study of 380 adults in three villages with different levels of modernization. Three biomarkers related to oxidative stress were measured: urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) and 8-isoprostane concentrations (both measured by liquid chromatography-tandem mass spectrometry), and blood telomere length (measured with qPCR). We examined associations between village-level modernization and oxidative stress-related biomarkers in a multilevel analysis including a random effect and covariates. The geometric means of urinary 8-OHdG and 8-isoprostane concentrations were 2.92 and 0.700 μg/g creatinine, respectively, in our study population. Higher urinary 8-OHdG concentrations and shorter telomeres were observed in participants from the more modernized villages, whereas urinary 8-isoprostane concentrations did not differ significantly among villages. Our findings imply that modernization-induced changes in lifestyle may increase oxidative DNA damage. Baseline levels of oxidative lipid damage are expected to be high in the indigenous populations of northern Laos. Assessments of oxidative stress may provide valuable insights into the mechanisms of health transition in specific populations.

Output-only structural identification with random decrement technique

Most of existing time domain methods fail to identify the unknown structural parameter in the ambient condition, because the force location is unknown and the number of unknown forces is usually larger than the number of sensors. A novel output-only structural identification method based on the random decrement (RD) technique and covariance matrix adaptation evolutionary strategy (CMA-ES) is presented. The RD functions of a structure under ambient excitation are extracted and treated as free vibration responses for structural parameter identification. The unknown structural parameters are identified by minimizing the discrepancy between the measured and calculated RD functions with the CMA-ES. Unlike traditional output-only methods, which require the estimation of the input excitation in the process, the proposed method does not require the identification of the input forces, thereby significantly reducing the computational cost. Numerical studies on two- and three-dimensional trusses and experimental studies on a steel frame are presented to demonstrate the accuracy and efficiency of the proposed approach. The effects of measurement noise and measurement duration on the identified results are examined. Results show that the proposed method can obtain satisfactory results even with 20% measurement noise and structural damage less than 10% can be detected. Comparison studies with the genetic algorithm (GA) and particle swarm optimization (PSO) are presented to verify the computational efficiency of the proposed method. Results also show that the proposed CMA-ES has a faster convergence speed than the GA and PSO method. The results demonstrate that the proposed technique can be used to detect damage severity and location in structures under ambient conditions.

No evidence for a negative association between bovine leukemia virus status and fertility in Kansas beef herds: a cross-sectional study.

Determine the association between bovine leukemia virus (BLV) status and fertility in beef cows. BLV-status was defined using 3 different testing strategies (ELISA-, quantitative polymerase chain reaction- [qPCR], and high proviral load [PVL]-status). Fertility was defined as the overall probability of pregnancy as well as the probability of becoming pregnant in the first 21 days of the breeding season. Convenience sample of 2,820 cows from 43 beef herds. The association of BLV-status with the probability of becoming pregnant was evaluated with a multivariable logistic regression analysis that used pregnancy status as a binary outcome, herd nested within ranch as a random effect, and BLV-status (ELISA-, qPCR-, and PVL-status as separate models) and potential covariates (eg, age, Body Condition Score [BCS] category, and interactions) as fixed effects. Raw data revealed that 55% (1,552/2,820) of cows were classified as BLV-positive by ELISA, and 95.3% (41/43) of herds had a least 1 ELISA-positive cow. Classification as BLV ELISA-positive was positively associated with the probability of being pregnant; however, when qPCR or PVL were used to classify BLV-status, there was no association with the probability of being pregnant. None of the methods of classifying BLV-status were associated with the probability of becoming pregnant in the first 21 days of the breeding season. This study did not find evidence that testing beef cows for BLV-status using ELISA, qPCR, or a cut-off of 0.9 PVL and removing test-positive cows will improve cowherd fertility as described by the probability of becoming pregnant during the breeding season or becoming pregnant during the first 21 days of the breeding season.

Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data

Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R2 of 0.52–0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R2 for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.

A Wiener Process Model With Dynamic Covariate for Degradation Modeling and Remaining Useful Life Prediction

Most industrial products degrade with usage. To capture degradation patterns and schedule condition-based maintenance actions, it is necessary to model the degradation process and predict the system remaining useful life (RUL). In practice, the operating environments and loading conditions are dynamic and stochastic, which introduces extra randomness in the degradation process. To account for the dynamic environmental impacts, this article proposes a nonlinear Wiener process model with a random time-varying covariate. We model the dynamic covariate with an Ornstein–Uhlenbeck process, and link it to the time-varying degradation rate by an exponential form covariate-effect function. Therefore, the proposed Wiener process has a time-varying degradation rate impacted by the dynamic environment. We propose to estimate the model parameters by maximum likelihood estimation based on given observations of the degradation and covariate. We also propose a simulation-based procedure to obtain the RUL for the proposed model. The model is verified by Monte Carlo simulations. We apply the proposed model to a dust-cleaning fan degradation dataset and a lithium-ion battery degradation dataset, and the results show that the proposed model outperforms the existing degradation models in fitting the real data and RUL prediction.

A Bivariate Extension to Exponentiated Inverse Flexible Weibull Distribution: Shock Model, Features, and Inference to Model Asymmetric Data

The primary objective of this article was to introduce a new probabilistic model for the discussion and analysis of random covariates. The introduced model was derived based on the Marshall–Olkin shock model. After proposing the mathematical form of the new bivariate model, some of its distributional properties, including joint probability distribution, joint reliability distribution, joint reversed (hazard) rate distribution, marginal probability density function, conditional probability density function, moments, and distributions for both Y=max{X1,X2} and W=min{X1,X2}, were investigated. This novel model can be applied to discuss and evaluate symmetric and asymmetric data under various kinds of dispersion. Moreover, it can be used as a probability approach to analyze different shapes of hazard rates. The maximum likelihood approach was utilized for estimating the parameters of the bivariate model. A simulation study was carried out to assess the performance of the parameters, and it was noted that the maximum likelihood technique can be used to generate consistent estimators. Finally, two real datasets were analyzed to illustrate the notability of the novel bivariate distribution, and it was found that the suggested distribution provided a better fit than the competitive bivariate models.

Convergence Analysis of Stochastic Kriging-Assisted Simulation with Random Covariates

We consider performing simulation experiments in the presence of covariates. Here, covariates refer to some input information other than system designs to the simulation model that can also affect the system performance. To make decisions, decision makers need to know the covariate values of the problem. Traditionally in simulation-based decision making, simulation samples are collected after the covariate values are known; in contrast, as a new framework, simulation with covariates starts the simulation before the covariate values are revealed and collects samples on covariate values that might appear later. Then, when the covariate values are revealed, the collected simulation samples are directly used to predict the desired results. This framework significantly reduces the decision time compared with the traditional way of simulation. In this paper, we follow this framework and suppose there are a finite number of system designs. We adopt the metamodel of stochastic kriging (SK) and use it to predict the system performance of each design and the best design. The goal is to study how fast the prediction errors diminish with the number of covariate points sampled. This is a fundamental problem in simulation with covariates and helps quantify the relationship between the offline simulation efforts and the online prediction accuracy. Particularly, we adopt measures of the maximal integrated mean squared error (IMSE) and integrated probability of false selection (IPFS) for assessing errors of the system performance and the best design predictions. Then, we establish convergence rates for the two measures under mild conditions. Last, these convergence behaviors are illustrated numerically using test examples. History: Accepted by Bruno Tuffin, area editor for simulation. Funding: This work was supported in part by Singapore Ministry of Education Academic Research Funds [Tier 1 Grants R-155-000-201-114 and A-0004822-00-00], the City University of Hong Kong [Grants 7005269 and 7005568], and the National Natural Science Foundation of China [Grant 72091211]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1263 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0329 ) at ( http://dx.doi.org/10.5281/zenodo.7344997 ).

A comprehensive multi-state conditional occupancy model for evaluating interactions of non-native and native species

A major challenge in ecology is disentangling interactions of non-native, potentially invasive species on native species. Conditional two-species occupancy models examine the effects of dominant species (e.g., non-native) on subordinate species (e.g., native) while considering the possibility that occupancy of one species may affect occupancy and/ or detection of the other. Although conditional two-species models are useful for evaluating the influence of one species on presence of another, it is possible that species interactions are density dependent. Therefore, we developed a novel two-species occupancy model that incorporates multiple abundance states (i.e., absent, present, abundant) of the native species. We showcase the utility of this model with a case study that incorporates random effects and covariates on both occupancy and detection to help disentangle species interactions given varying occupancy and detection in different abundance states. We use snorkel survey data from the Umpqua basin, Oregon, where it is hypothesized that smallmouth bass Micropterus dolomieu, a non-native piscivore, exclude Umpqua chub Oregonichthys kalawatseti, a small endemic minnow. From our two-species multi-state (2SMS) model, we concluded that average occupancy was low for both fishes, and that when non-native bass were present, overall native chub occupancy in the present (0.18 ± 0.05 SD) and abundant (0.19 ± 0.03) states was higher than when non-natives were absent (0.14 ± 0.02/ 0.08 ± 0.02), indicating the non-native was not excluding the native species. By incorporating a species interaction factor, we found a positive association (6.75 ± 5.54 SD) between native chub and non-native bass. The covariates strongly related to occupancy were elevation, algae, and land cover type (urban and shrub). Detection probability for both species (0.21–0.82) was most strongly related to the covariates day of year, water temperature, gravel substrate, and stream order/ magnitude. Incorporation of detection probability and covariates enabled interpretation of interactions between the two species that may have been missed without their inclusion in the modeling process. Our new 2SMS occupancy model can be used by scientists and managers with a broad range of survey and covariate data to disentangle species interactions problems to help them inform management decisions.

Graphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust Model

The Cluster Weighted Robust Model (CWRM) is a recently introduced methodology to robustly estimate mixtures of regressions with random covariates. The CWRM allows users to flexibly perform regression clustering, safeguarding it against data contamination and spurious solutions. Nonetheless, the resulting solution depends on the chosen number of components in the mixture, the percentage of impartial trimming, the degree of heteroscedasticity of the errors around the regression lines and of the clusters in the explanatory variables. Therefore, an appropriate model selection is crucially required. Such a complex modeling task may generate several “legitimate” solutions: each one derived from a distinct hyperparameters specification. The present article introduces a two step-monitoring procedure to help users effectively explore such a vast model space. The first phase uncovers the most appropriate percentages of trimming, whilst the second phase explores the whole set of solutions, conditioning on the outcome derived from the previous step. The final output singles out a set of “top” solutions, whose optimality, stability and validity is assessed. Novel graphical and computational tools—specifically tailored for the CWRM framework—will help the user make an educated choice among the optimal solutions. Three examples on real datasets showcase our proposal in action. Supplementary files for this article are available online.

A Probabilistic View on Predictive Constructions for Bayesian Learning

Given a sequence X=(X1,X2,…) of random observations, a Bayesian forecaster aims to predict Xn+1 based on (X1,…,Xn) for each n≥0. To this end, in principle, she only needs to select a collection σ=(σ0,σ1,…), called “strategy” in what follows, where σ0(·)=P(X1∈·) is the marginal distribution of X1 and σn(·)=P(Xn+1∈·|X1,…,Xn) the nth predictive distribution. Because of the Ionescu–Tulcea theorem, σ can be assigned directly, without passing through the usual prior/posterior scheme. One main advantage is that no prior probability is to be selected. In a nutshell, this is the predictive approach to Bayesian learning. A concise review of the latter is provided in this paper. We try to put such an approach in the right framework, to make clear a few misunderstandings, and to provide a unifying view. Some recent results are discussed as well. In addition, some new strategies are introduced and the corresponding distribution of the data sequence X is determined. The strategies concern generalized Pólya urns, random change points, covariates and stationary sequences.

Multiple‐trait and structural equation modelling approaches to infer genetic relationships between tail length and weight traits in Merinoland sheep

Tail docking is routinely conducted in long-tailed sheep breeds to prevent flystrike infections, but it is not in agreement with legal guidelines and animal welfare issues. Selection on short tails is a sustainable alternative in this regard, but side effects on other breeding goal traits are unclear. In consequence, the present study aimed to estimate genetic parameters for tail length (TL) at birth, birth weight (BW), weaning weight (WW) and postweaning weight (PWW) at the slaughtering date considering single-trait (STM), multiple-trait (MTM) and structural equation models (SEM) with different random effects, and accordingly, different covariance structures. The SEM considered time-lagged recursive relationships among response variables in three different pathways. The first path pertained to the effect of TL on WW and of WW on PWW. The second path reflected the effect of BW on WW and of WW on PWW. The third path was the recursive effect of TL on PWW. The phenotypic data consisted of 2803 records for TL, 13,042 records for BW, 1556 records for WW and 3986 records for PWW from Merinoland lambs. Lambs were born in the period from 1995 to 2021 and kept at the university Gießen research station, Germany, with their naturally long tails. Genetic statistical model evaluation based on Bayesian and Akaike's information criteria suggested models simultaneously considering direct genetic, maternal genetic and maternal permanent environmental effects and respective covariances. For statistical models including the same random effects and covariance structures, SEM were superior over MTM. The direct heritability for TL from the best-fitting STM was 0.60 ± 0.08, indicating the potential for genetic reduction of tail length within a few generations. For growth traits, the direct heritabilities ranged from 0.16 ± 0.03 for BW to 0.31 ± 0.09 for PWW. The maternal heritabilities were 0.03 ± 0.03 for TL, 0.12 ± 0.02 for BW, 0.04 ± 0.03 for WW and 0.07 ± 0.03 for PWW, reflecting small, but the non-significant influence of uterine characteristics on the tail development. The direct genetic correlations between TL and all weight traits were positive and very similar to MTM and SEM but reflected antagonistic genetic relationships from a breeding perspective. Oppositely, the structural equation coefficients reflecting trait associations phenotypically were negative (favourable) for the time-lagged effects of TL on WW and on PWW. As an explanation, lambs with long and woolly tails have an increased risk for contamination with dirt and dust causing infections, which in turn impairs the body weight development. In conclusion, breeding on short tails should consider trait-associated environmental risk factors, for example, disease susceptibility, which can be mimicked via SEM approaches.

Genotype by environment interaction due to heat stress in Brown Swiss cattle

Due to its geographical position and a highly variable orography, Italy is characterized by several climatic areas and thus, by many different dairy cow farming systems. Brown Swiss cattle, in this context, are a very appreciated genetic resource for their adaptability and low metabolic requirement. The significant heterogeneity in farming systems may consist of genotype by environment (G × E) interactions with neglected changes in animals' rank position. The objective of this study was to investigate G × E for heat tolerance in Brown Swiss cattle for several production traits (milk, fat, and protein yield in kilograms; fat, protein, and cheese yield in percentage) and 2 derivate traits (fat-corrected milk and energy-corrected milk). We used the daily maximum temperature-humidity index (THI) range, calculated according to weather stations' data from 2008 to 2018 in Italy, and 202,776 test-day records from 23,396 Brown Swiss cows from 639 herds. Two different methodologies were applied to estimate the effect of the environmental variable (THI) on genetic parameters: (1) the reaction norm model, which uses a continuous random covariate to estimate the animal additive effect, and (2) the multitrait model, which splits each production pattern as a distinct and correlated trait according to the first (a thermal comfort condition), third (a moderate heat stress condition), and fifth (a severe heat stress condition) mean THI value quintile. The results from the reaction norm model showed a descending trend of the additive genetic effect until THI reached the value of 80. Then we recorded an increase with high extreme THI values (THI 90). Permanent environmental variance at increasing THI values revealed an opposite trend: The plot of heritability and the ratio of animal permanent environmental variance to phenotypic variance showed that when the environmental condition worsens, the additive genetic and permanent environmental component for production traits play a growing role. The negative additive genetic correlation between slope and linear random coefficient indicates no linear relationship between the production traits or under heat stress conditions, except for milk yield and protein yield. In tridimensional wireframe plots, the extreme margin decreases until a minimum of ∼0.90 of genetic correlation in the ECM trait, showing that the magnitude of G × E interaction is greater than the other traits. Genetic correlation values in Brown Swiss suggest the possibility of moderate changes in animals' estimated breeding value in heat stress conditions. Results indicated a moderate G × E interaction but significant variability in sire response related to their production level.

Model averaging for generalized linear models with missing at random covariates

We consider a frequentist model averaging approach for prediction issue of generalized linear model with covariates involving missing at random. To avoid the uncertainty problem from imputation on the missing covariates data, this work adjusts model averaging criterion by selecting a batch of candidate models with the available covariate patterns rather than all possible combinations of covariates. The weights over the batch of candidate models are estimated based on the leave-one-out cross-validation criterion of the fully observed data. The asymptotic optimality of the proposed method is established under some regular conditions. Simulation studies are carried out to demonstrate the finite sample performance of our proposed approach in comparison to other methods. A real data set from NHANES 2017–2018 is also applied to check the effectiveness of the proposed approach.

The Role of Green Entrepreneurship and Green Variables in Sustainable Development in the Culinary Sector in Indonesia: Early Days of the COVID-19 Pandemic

The UN Sustainable Development Goals are among the crucial missions of countries worldwide, including Indonesia. Nevertheless, application of a production system does not guarantee sustainability, and the Indonesian government’s support for sustainability in the entrepreneurial sector is nonoptimal. This study investigates green entrepreneurship in Indonesia’s culinary sector in relation to sustainable development and green variables. Using a culinary sector survey based on the UNWTO and the Ministry of Tourism, this study references primary questionnaire data, applying a random sampling technique and structural equation modeling covariance with a sample of 270 respondents. The results indicate that the provision of green products affects sustainable development, regardless of respondents’ green entrepreneurship. While green products may affect sustainable development, directly or through green entrepreneurship, green design does not appear to affect sustainable development, and green entrepreneurship does not appear to support sustainable conditions. Environmentally friendly product design and production processes have generated multiple recyclable products; however, the culinary sector must be made aware of the entrepreneurial ecosystem surrounding culinary business governance and the concerns and practices of sustainable development. This study was conducted at the beginning of the COVID-19 pandemic and represents the conditions of the culinary sector during the pandemic.

Remaining Useful Life Estimation of Fan Slewing Bearings in Nonlinear Wiener Process with Random Covariate Effect

Since the degradation process of fan slewing bearings is easily influenced by the external environment, it is difficult to estimate its remaining useful life (RUL) accurately. A nonlinear Wiener degradation model considering the influence of random covariate is established for the prediction of the RUL of fan slewing bearings in this study. Firstly, considering the nonlinear and nonmonotonic properties of the operation process of fan slewing bearings, the degradation model of the nonlinear Wiener process of fan slewing bearings is established. Secondly, the combination of random covariate models and the nonlinear Wiener degradation process is researched. The stress effect which is used as a random covariate is introduced into the nonlinear Wiener degradation model in the form of the additive hazard model. Moreover, the closed expression for the RUL probability density function(PDF) is derived for the random variation of drift coefficients, the individual differences and the random variation of covariates. Thirdly, the maximum likelihood estimation algorithm is used to estimate the RUL parameters depending on the historical degradation data. Finally, the vibration data of fan slewing bearings monitored by sensors are used to verify the effectiveness of the proposed method. The results show that the proposed method can be used to improve the fitting degree of the model and the accuracy of RUL estimation.

An Offenders-Offenses Shared Component Spatial Model for Identifying Shared and Specific Hotspots of Offenders and Offenses: A Case Study of Juvenile Delinquents and Violent Crimes in the Greater Toronto Area

ObjectivesWe attempted to apply the Bayesian shared component spatial modeling (SCSM) for the identification of hotspots from two (offenders and offenses) instead of one (offenders or offenses) variables and developed three risk surfaces for (1) common or shared by both offenders and offenses; (2) specific to offenders, and (3) specific to offenses.MethodsWe applied SCSM to examine the joint spatial distributions of juvenile delinquents (offenders) and violent crime (offenses) in the York Region of the Greater Toronto Area at the dissemination area level. The spatial autocorrelation, overdispersion, and latent covariates were adjusted by spatially structured and unstructured random effect terms in the model. We mapped the posterior means of the estimated shared and specific risks for identifying the three risk surfaces and types of hotspots.ResultsResults suggest that about 50% and 25% of the relative risks of juvenile delinquents and violent crimes, respectively, could be explained by the shared component of offenders and offenses. The spatially structured terms attributed to 48% and 24% of total variations of the delinquents and violent crimes, respectively. Contrastingly, the unstructured random covariates influenced 3% of total variations of the juvenile delinquents and 51% for violent crimes.ConclusionsThe Bayesian SCSM presented in this study identifies shared and specific hotspots of juvenile delinquents and violent crime. The method can be applied to other kinds of offenders and offenses and provide new insights into the clusters of high risks that are due to both offenders and offenses or due to offenders or offenses only.

Nonparametric estimation of conditional marginal excess moments

Several risk measures have been proposed in the literature, among them the marginal mean excess, defined as MMEp=E[{Y(1)−Q1(1−p)}+|Y(2)>Q2(1−p)], provided E|Y(1)|<∞, where (Y(1),Y(2)) denotes a pair of risk factors, y+≔max(0,y), Qj the quantile function of Y(j),j∈{1,2}, and p∈(0,1). In this paper we consider a generalization of this measure, where the random variables of main interest (Y(1),Y(2)) are observed together with a random covariate X∈Rd, and where the Y(1) excess is also power transformed. This leads to the concept of conditional marginal excess moment for which an estimator is proposed allowing extrapolation outside the data range. The main asymptotic properties of this estimator have been established, using empirical processes arguments combined with the multivariate extreme value theory. The finite sample behavior of the estimator is evaluated by a simulation experiment. We apply also our method on a vehicle insurance customer dataset.

Spectral properties of the generalized diluted Wishart ensemble

The celebrated Marčenko–Pastur law, that considers the asymptotic spectral density of random covariance matrices, has found a great number of applications in physics, biology, economics, engineering, among others. Here, using techniques from statistical mechanics of spin glasses, we derive simple formulas concerning the spectral density of generalized diluted Wishart matrices. These are defined as , where X and Y are diluted N × P rectangular matrices, whose entries correspond to the links of doubly-weighted random bipartite Poissonian graphs following the distribution , with the probability density ϱ(x, y) controlling the correlation between the matrices entries of X and Y . Our results cover several interesting cases by varying the parameters of the matrix ensemble, namely, the dilution of the graph d, the rectangularity of the matrices α = N/P, and the degree of correlation of the matrix entries via the density ϱ(x, y). Finally, we compare our findings to numerical diagonalisation showing excellent agreement.

Lethal violence against women in southern Brazil: Spatial analysis and associated factors

Objectiveestimate the risk for the occurrence of lethal violence against women and to identify the associated factors in the state of Paraná. Methodecological study of deaths of women aged between 15 and 59 years, victims of aggression. The units of analysis were the cities of Paraná. Latent Bayesian Gaussian models with negative binomial probability distribution were used. The modeling considered intercept, spatial random effects and covariates, performed with the deterministic Integrated Nested Laplace Approximations approach. ResultsThere was a positive association between lethal violence against women and the percentage of mothers who were heads of households. Finally, male homicide rates, rates of non-lethal violence against women and the cities with women mayors and councilors were also associated. ConclusionThis type of violence was associated with low education, structural violence and the participation of women in politics.