Bivariate Approach Research Articles

Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping

Mapping shallow landslide inventories represents the initial stage in conducting susceptibility, vulnerability, and risk analyses. The identification of areas with a history of landslides, both past and current events, enables the detection of spatio-temporal patterns and facilitates the evaluation of morphological and geological factors. Shallow landslides are frequent in Brazil, especially along the Brazilian south and southeastern coast. Despite their high frequency, the recognition and mapping of these landslides are inadequately developed due to the absence of established mapping guidelines. This resulted of limited investments by Brazilian authorities in initiatives aimed at preventing and mitigating mass movements. Thus, the aim of this study is to establish criteria for the recognition and mapping of shallow landslides in Brazil and to evaluate their applicability in susceptibility analysis. The study area is located in Itaóca and Apiaímunicipalities, São Paulo state, southeastern Brazil. Criteria for shallow landslide recognition were created based on the absence of vegetation, shape and size, drainage network distance, slope position, planar rupture surface, and altimetric variation. An extensive landslide interpretation was carried out using a set of free access Google Earth Pro images. Susceptibility maps were developed through a bivariate statistical approach, specifically the Informative Value method. The outcomes demonstrated that incorporating the deposition area in susceptibility studies for shallow landslides led to modifications in the final mapping, a slight reduction in validation rates, and, notably, an impact on the influence of the thematic variable curvature. Therefore, it is clear that the criteria used for landslide inventory maps have a significant effect on the accuracy of susceptibility maps and should be a critical consideration in studies related to this subject.

Temporal-Like Bivariate Fay-Herriot Model: Leveraging Past Responses and Advanced Preprocessing for Enhanced Small Area Estimation of Growing Stock Volume

Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes. This study introduces an enhanced temporal-like bivariate Fay-Herriot model, improving upon its univariate counterpart. The model incorporates field data and auxiliary data, including canopy height metrics from WorldView stereo-imagery and past census data, sourced from the University Forest of Pertouli in Central Greece. The model aims to estimate the growing stock volume for 2008 and 2018, focusing on enhancing the precision of the 2018 estimates. The 2008 dependent variable is used as auxiliary information by the model for more reliable 2018 small area estimates. A novel preprocessing pipeline is also introduced, which includes outlier identification, cluster analysis, and variance smoothing. Compared to direct estimates and the standard univariate Fay-Herriot model, our bivariate approach shows a percentage variance reduction of 96.58% and 13.52%, respectively. The methodology not only offers more reliable estimates with reduced variance and bias but also contributes to more accurate decision-making for sustainable forest management.

The Effect of Work Ethics and Competence on Employee Performance at the Turatea District Office, Jeneponto Regency

Using a bivariate quantitative research approach, this paper aims to specify the effect of work ethics and competence on employee performance at the Turatea District Office, Jeneponto Regency, both singly and together. The population in this study wereall employees of Turatea District Office, Jeneponto Regency—a total of 33 people consisting of permanent employees and contract employees. A total sampling technique was used, where the entire population is sampled. The analytical technique used to test the hypothesis is multiple linear regression analysis. The results of this study indicate that (1) work ethics has a positive and significant effect on employee performance, (2) competence has a positive and significant effect on employee performance, and (3) work ethics and competence simultaneously affect employee performance, at the Turatea District Office, Jeneponto Regency.
 Keywords: work ethics, competence, performance, employee

The illness trap: The impact of disability benefits on willingness to receive HCV treatment

Health care is assumed to be a primary good, implying that patients should always demand or accept treatments that may enhance their life expectancy and quality of life, especially if the risks associated with the treatment are low. We argue that, especially in countries with a well-developed welfare state, treating an invalidating condition may lead to opportunity costs in terms of reduced disability allowances that may represent a barrier to treatment for low-income individuals. We test this hypothesis by applying a recursive bivariate probit approach to population data from an ad hoc administrative database for Liguria (an Italian administrative region). The dataset includes data for more than 8 thousand people affected by hepatitis C Virus (HCV) infection between 2013 and 2020. After the discovery of new direct-acting antivirals (DAAs) in 2014, HCV eradication may now be possible. However, despite the national and international efforts, several patients diagnosed with HCV choose not to undergo drug therapy despite the adverse consequences for their personal health and relevant costs to the national health system. We show that five years after the implementation of the new drugs, approximately 41 % of the diagnosed population in Liguria remains untreated. This percentage increases to 64 % within the subgroup entitled to disability benefits and characterized by lower income levels. The “illness trap” effect is more substantial for older people but also low-income patients. Moreover, we find that this effect is higher in patients with an intermediate range of comorbidities; indeed, these patients are at a higher risk of losing economic benefits if they recover from HCV. These results suggest the need for healthcare policies addressing this distorting effect when designing benefit programs and granting financial benefits to patients.

Landslide susceptibility assessment in Sabah, Malaysia: A bivariate frequency ratio approach

Landslide susceptibility assessment in Sabah, Malaysia: A bivariate frequency ratio approach

A new bivariate approach for modeling the interaction between stock volatility and interest rate: An application to S&P500 returns and options

The GARCH models developed so far do not take into account the interaction between the volatility of asset returns and the dynamics of the interest rate. In this paper, we propose a bivariate GARCH model in which interest rate movements and asset price volatility are fully coupled. This approach yields explicit and simple to implement recursion formulas for the moment generating function, which can be exploited to compute option prices by applying the fast Fourier transform or other convolution techniques. We perform a thorough and comprehensive empirical analysis based on real S&P500 return and option data showing the usefulness and robustness of the suggested methodology. Both in-sample and out-of-sample results reveal the superiority of our approach over the GARCH model with constant interest rates.

Does exchange rate volatility affect the impact of appreciation and depreciation on the trade balance? A nonlinear bivariate approach

Does exchange rate volatility alter the effect of appreciation and depreciation on the trade balance? The literature posits that the trade balance responds more to depreciation shocks than appreciation shocks. Using monthly data from 1960M02–2020M12, we examine the asymmetric effects of exchange rates on the trade balance while accounting for exchange rate volatility. The study applies a nonlinear bivariate model that allows asymmetric effects and volatility to be examined concurrently. We find that exchange rate volatility reduces the positive effects of an appreciation shock on the trade balance in developed countries in the short and long run. In developing nations, exchange rate volatility promotes the positive effects of a depreciation shock on the trade balance, both in the short and long run. The transmission mechanism implies that a depreciation shock is ineffective in developed countries because the volatility that accompanies the shock diminishes the trade balance.

Mathematical investigation of a combustible visco-elastic material in a cylindrical stockpile with oxygen (O2) consumption and induced electric field

Oxygen concentration plays crucial roles in the combustion of polymeric materials, such as reducing the emission of poisonous gases (nitrogen oxide and carbon monoxide) and enhancing heat transfer. However, in the combustion process, a continuous increase in reaction rate without considering heat loss to the environment may result to the accumulation of heat in the stockpile and possible self-ignition. As such, this work investigates the thermal decomposition of an electrically combustible visco-elastic material in a cylindrical stockpile, considering O2 consumption. The nonlinear partial differential equations governing the problem were solved numerically via the bivariate spectral collocation approach (BSCA). The impact of kinetic parameters (for example, heat generation, oxygen consumption rate, and activation energy.) on the flow, heat and mass transfer, and thermal stability of the system were studied and qualitatively discussed.

Multifactor Risk Attribution Applied to Systemic, Climate and Geopolitical Tail Risks for the Eurozone Banking Sector

The aim of this work is to introduce an innovative methodology for performing risk attribution within a multifactor risk framework. We applied this analysis to the assessment of systemic, climate, and geopolitical risks relative to a representative sample of Eurozone banks between 2011 and 2022. Comparing the results to the output of a bivariate approach, we found that contemporaneous tail crises generate combined equity losses exceeding partial analysis estimates. We then attributed the combined risk to each factor and to the effect of their interaction by employing our proposed frequency-based approach. For our computations, we used multivariate GARCH, Monte Carlo simulations, and a suite of Eurozone-specific factors. Our results show that total combined risk is on average 18% higher than traditional systemic risk estimates, that climate risk more than doubled in our period of analysis, and that geopolitical risk surged to over 5% of total combined risk. Our climate risk estimate is in line with the results of the 2022 European Central Bank climate stress test, and our geopolitical risk measure shows a positive correlation with the GPRD and Threats index.

Investigating injury severity interaction between urban and urban-rural fringe areas: a grouped random parameters seemingly unrelated bivariate probit approach

Urban-rural integration is the critical process of urbanization, while during this process there are various corresponding problems generated, safety is the most important one, especially at urban-rural fringe areas due to mixed traffic flow and complicated land use. This study intended to investigate the injury severity interaction between urban area and urban-rural fringe area, and explore the correlation within injury severity levels and heterogeneity attributed to unobserved factors. To address the correlation and heterogeneity issues, a grouped random parameters seemingly unrelated bivariate (SUB) probit model was proposed, in which the SUB probit model addressed the correlation of residuals, while the random parameters model accommodated the heterogeneity due to unobserved factors. By comparing the pooled with random parameters models, the results showed that random parameters SUB probit model performed better than the pooled one, and the dataset collected from 2013 to 2017 in Chengdu, China was adopted to illustrate the proposed model. It is found that crash location, speed limit and age of person injured are significant for injury severity in urban and urban-rural fringe areas, but crash form plays a significant role in urban area while number of persons involved should be paid more attention due to injury severity in urban-rural fringe area. Some empirical suggestions are presented to improve the safety in urban and urban-rural fringe areas.

Characterization of cardiovascular and cerebrovascular controls via spectral causality analysis in patients undergoing surgical aortic valve replacement during a three-month follow-up

Objective. Aortic valve stenosis (AVS) induces left ventricular function adaptations and surgical aortic valve replacement (SAVR) restores blood flow profile across aortic valve. Modifications of cardiac hemodynamics induced by AVS and SAVR might alter cardiovascular (CV) and cerebrovascular (CBV) controls. The study aims at characterizing CV and CBV regulations one day before SAVR (PRE), within one week after SAVR (POST), and after a three-month follow-up (POST3) in 73 AVS patients (age: 63.9 ± 12.9 yrs; 48 males, 25 females) from spontaneous fluctuations of heart period (HP), systolic arterial pressure, mean arterial pressure and mean cerebral blood velocity. Approach. CV and CBV regulations were typified via a bivariate autoregressive approach computing traditional frequency domain markers and causal squared coherence (CK 2) from CV and CBV variabilities. Univariate time and frequency domain indexes were calculated as well. Analyses were carried out in frequency bands typical of CV and CBV controls at supine rest and during active standing. A surrogate method was exploited to check uncoupling condition. Main results. We found that: (i) CV regulation is impaired in AVS patients; (ii) CV regulation worsens in POST; (iii) CV regulation recovers in POST3 and CV response to active standing is even better than in PRE; (iv) CBV regulation is preserved in AVS patients; (v) SAVR does not affect CBV control; (vi) parameters of the CBV control in POST3 and PRE are similar. Significance. CK 2 is particularly useful to characterize CV and CBV controls in AVS patients and to monitor of patient’s evolution after SAVR.

Detection and treatment of outliers for multivariate robust loss reserving

AbstractTraditional techniques for calculating outstanding claim liabilities such as the chain-ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunately, the literature in robust methods of reserving is scant, with notable exceptions such as Verdonck & Debruyne (2011, Insurance: Mathematics and Economics, 48, 85–98) and Verdonck & Van Wouwe (2011, Insurance: Mathematics and Economics,49, 188–193). In this paper, we put forward two alternative robust bivariate chain-ladder techniques to extend the approach of Verdonck & Van Wouwe (2011, Insurance: Mathematics and Economics,49, 188–193). The first technique is based on Adjusted Outlyingness (Hubert & Van der Veeken, 2008. Journal of Chemometrics,22, 235–246) and explicitly incorporates skewness into the analysis while providing a unique measure of outlyingness for each observation. The second technique is based on bagdistance (Hubert et al., 2016. Statistics: Methodology, 1–23) which is derived from the bagplot; however; it is able to provide a unique measure of outlyingness and a means to adjust outlying observations based on this measure.Furthermore, we extend our robust bivariate chain-ladder approach to an N-dimensional framework. The implementation of the methods, especially beyond bivariate, is not trivial. This is illustrated on a trivariate data set from Australian general insurers and results under the different outlier detection and treatment mechanisms are compared.

Bivariate Analysis of Distribution Functions Under Biased Sampling

This article compares distribution functions among pairs of locations in their domains, in contrast to the typical approach of univariate comparison across individual locations. This bivariate approach is studied in the presence of sampling bias, which has been gaining attention in COVID-19 studies that over-represent more symptomatic people. In cases with either known or unknown sampling bias, we introduce Anderson–Darling-type tests based on both the univariate and bivariate formulation. A simulation study shows the superior performance of the bivariate approach over the univariate one. We illustrate the proposed methods using real data on the distribution of the number of symptoms suggestive of COVID-19.

An approach to modeling residual life of a renewal process for reliability analysis and maintenance planning

This paper extends and relaxes the structure of a mean remaining time to renewal model for a failure prone system subject to minimal repairs. Compared to existing related works, the contributions of our model are threefold. It not only accounts for changes in operating states and age factor, but also can adapt itself to minimal repair process history. It is implemented through the use of a damage process X(t) coupled with a supplementary variable N(t) counting the number of failures. The bivariate approach allows a wide class of models to be considered including age-based models and those ignoring the repair effect. Another feature enhancing the domain of its applicability is to predict failures and quantify some reliability indexes. These impose a challenge when coping with models either characterized by inter-correlated failure times or developed under varying operating conditions. Further, the structure of the devised model called state-dependent mean remaining time to renewal (in short SD-MRTR) enables the implementation of maintenance policy on the basis of renewal–reward arguments. In present setting, a renewal is defined with respect to the supplementary variable. That means, a renewal (perfect repair) is carried out as soon as the number of failures reaches a give value j. Using the renewal–reward theorem argument and the explored SD-MRTR, the practical contribution of the model is enhanced by proposing a j-based preventive replacement policy achieving minimum repair costs. The proposed model and the behavior of the optimal solution as the model parameters change are illustrated through numerical examples.

A hierarchical Bayesian analysis for bivariate Weibull distribution under left-censoring scheme

This paper presents a novel approach for analyzing bivariate positive data, taking into account a covariate vector and left-censored observations, by introducing a hierarchical Bayesian analysis. The proposed method assumes marginal Weibull distributions and employs either a usual Weibull likelihood or Weibull–Tobit likelihood approaches. A latent variable or frailty is included in the model to capture the possible correlation between the bivariate responses for the same sampling unit. The posterior summaries of interest are obtained through Markov Chain Monte Carlo methods. To demonstrate the effectiveness of the proposed methodology, we apply it to a bivariate data set from stellar astronomy that includes left-censored observations and covariates. Our results indicate that the new bivariate model approach, which incorporates the latent factor to capture the potential dependence between the two responses of interest, produces accurate inference results. We also compare the two models using the different likelihood approaches (Weibull or Weibull–Tobit likelihoods) in the application. Overall, our findings suggest that the proposed hierarchical Bayesian analysis is a promising approach for analyzing bivariate positive data with left-censored observations and covariate information.

Unpaid eldercare and its impact on the US labor supply

Globally, the dependency ratio is rising due to increase in aging population. Individuals, especially women are challenged when choosing between participating in the labor market and providing care. Using 2011-2017 American Time Use Survey data for a subsample of individuals aged 25- 61 years, we examine the effect of frequent eldercare provision on labor force participation in the US using bivariate probit instrumental variable approach. Our findings suggest that unpaid eldercare performed frequently reduces labor force participation. Female frequent providers are likely to have lower labor force participation compared to their male counterparts. Robustness and sensitivity checks confirm these findings.

Gaidai-Xing reliability method validation for 10-MW floating wind turbines

In contrast to well-known bivariate statistical approach, which is known to properly forecast extreme response levels for two-dimensional systems, the research validates innovative structural reliability method, which is particularly appropriate for multi-dimensional structural responses. The disadvantage of dealing with large system dimensionality and cross-correlation across multiple dimensions is not a benefit of traditional dependability approaches that deal with time series. Since offshore constructions are built to handle extremely high wind and wave loads, understanding these severe stresses is essential, e.g. wind turbines should be built and operated with the least amount of inconvenience. In the first scenario, the blade root flapwise bending moment is examined, whereas in the second, the tower bottom fore-aft bending moment is examined. The FAST simulation program was utilized to generate the empirical bending moments for this investigation with the load instances activated at under-rated, rated, and above-rated speeds. The novel reliability approach, in contrast to conventional reliability methods, does not call for the study of a multi-dimensional reliability function in the case of numerical simulation. As demonstrated in this work, it is now possible to assess multi-degree-of-freedom nonlinear system failure probability, in the case when only limited system measurements are available.

FPSO offloading operational safety study by a multi-dimensional reliability method

In contrast to the well-known bivariate statistical approach, which is known to properly anticipate extreme response levels for two-dimensional systems, the research validates a unique structural reliability method that is particularly appropriate for multi-dimensional structural responses. Traditional dependability techniques that deal with time series don't have an edge when it comes to handling high-dimensional systems and cross-correlation between several dimensions. A Floating Production Storage and Offloading Unit (FPSO) is a tanker that can produce, store and transport hydrocarbon products such as oil. The hawser during the offloading process is an important part used during operations, and a thorough prediction of its extreme tension is essential for operational safety. Failure of the offloading process may lead to great environmental harm and economic loss. This study assessed hydrodynamic forces acting on FPSO vessels in real-world environmental conditions based on the potential flow theory. A brief discussion of the experimental verification of numerical results is also included. The outlined method may be effectively employed during the vessel design phase for choosing the best vessel specifications to reduce potential FPSO hawser tension. The ability to foresee high hawser strains during the FPSO offloading operation is essential for reliability and safety.

Operational reliability study of ice loads acting on oil tanker bow

As a result of climate change, the Arctic glaciers start to melt, and the summer season arrives, making it acceptable for trade ships. There is still shattered ice in the saltwater even though the Arctic glaciers melt in the summer. The stochastic ice loading on the ship's hull is a complex ship-ice interaction. In order to properly build a vessel, it is necessary to reliably estimate the consequent high bow stresses using statistical extrapolation techniques. The bivariate reliability approach is used in this study to compute the excessive bow forces that an oil tanker encounters while sailing in the Arctic Ocean. Two stages are taken in the analysis. First, ANSYS/LS-DYNA is used to compute the oil tanker's bow stress distribution. Second, high bow stresses are projected utilizing a unique dependability methodology to evaluate return levels associated with extended return times. This research focuses on bow loads of an oil tanker travelling in the Artic Ocean using the recorded ice thickness distribution. To take advantage of weaker ice, the vessel's itinerary across the Arctic Ocean was windy (not the shortest straight path). This results in the ship route data used being inaccurate concerning the ice thickness statistics for the area yet skewed concerning the ice thickness data that was particular to a vessel's path. Therefore, this work aims to present a quick and precise approach for estimating the high bow stresses experienced by oil tankers along a given path. Most designs incorporate univariate characteristic values, while this study advocates a bivariate reliability approach for a safer and better design.

The Inventory of Problems-29 (IOP-29): A systematic review and bivariate diagnostic test accuracy meta-analysis.

The Inventory of Problems-29 (IOP-29) is a 29-item self-administered symptom validity test (SVT) that assesses the credibility of clinical presentations related to posttraumatic stress disorder, depression/anxiety, psychosis, cognitive impairment and combination thereof. To date, no publications have summarized the classification accuracy of the IOP-29 using a bivariate meta-analytical approach that preserves the two-dimensional nature of the estimators. Our objective was to conduct a systematic review and bivariate diagnostic test accuracy meta-analysis of the IOP-29 according to the relevant guidelines. Twenty-one independent samples were included, with a total sample size of 4,163 participants. The results indicated that the IOP-29 is able to discriminate adequately between instructed simulators and healthy controls/clinical patients. Using the recommended cutoff (False Disorder Probability Score [FDS], ≥ .50), a sensitivity of 82% was achieved, maintaining specificity at 93% (false positive rate of 7%). The language of the test and the type of comparison group have been identified as possible sources of heterogeneity. Specificity decreases for the non-English version of the IOP-29, for the FDS ≥ .30, and also decreases for studies using clinical controls, for all three cutoff scores. In general, our findings support the usefulness of the IOP-29 as an SVT; however, most of the included studies use a simulation design and have been coauthored by the test authors. Likewise, about half of the studies did not include bona fide patient controls but only nonclinical controls. The results obtained are highly promising, but further research, especially that using the criterion group paradigm, is recommended. (PsycInfo Database Record (c) 2023 APA, all rights reserved).