Conditional Distribution Research Articles

Mediterranean meteotsunamis of May 2021 and June 2022

Meteorological tsunamis (i.e., tsunami-like waves of atmospheric origin) are regularly observed in the Mediterranean Sea. During a single event, destructive flooding usually occurs in one location or limited area. However, in May 2021 and June 2022, strong meteotsunamis hit several Mediterranean locations up to 500 km apart. In the morning hours of the 24th of May 2021, a meteotsunami hit Bonifacio on the island of Corsica (western Mediterranean, France) and in the afternoon hours of the same day, another meteotsunami hit Široka Bay on the island of Ist (Adriatic Sea, Croatia), 500 km away. About 13 months later, on the 26th of June 2022, a meteotsunami hit Ciutadella on the island of Menorca (Spain) and two days later Bonifacio, 400 km away. Sea-level and atmospheric pressure data and satellite imagery, as well as synoptic conditions, associated with both events were analysed in detail. It has been confirmed that in the Mediterranean, meteotsunamis occur when meteotsunamigenic synoptic conditions prevail over the area, with a strong southwesterly jet stream embedded in dynamically or convectively unstable atmospheric layers standing out as the most important condition. The meteotsunamigenic potential of each of the three bays (Ciutadella, Bonifacio, Široka Bay) was investigated by considering: (1) the potential for Proudman resonance on the shelves offshore of the bays, (2) the orientation of the mouth of the bay and (3) the frequency of meteotsunamigenic synoptic conditions over the area. The strongest and most frequent meteotsunamis occur at locations where the shelf characteristics (width, depth, orientation), bay mouth orientation and distribution of general synoptic conditions, have characteristics that support the amplification of long-ocean waves and their propagation toward the bay mouth.

Comparison of different sustained-release opioids and acute respiratory conditions in patients with cancer and chronic kidney disease.

Few data are available on the association between the use of oxycodone in patients with chronic kidney disease (CKD) and acute respiratory conditions. The aim of this study was to investigate whether oxycodone is associated with an increased risk of acute respiratory conditions in patients with cancer and CKD compared with other opioids. The data were obtained from a claims database in Japan. Patients with cancer and CKD who had received sustained-release opioids, including oral oxycodone, oral morphine, or transdermal fentanyl, between April 2014 and May 2021 were selected. The primary outcome was defined as an acute respiratory condition. Data for age and sex, morphine equivalent daily dose, concomitant use of specified medications, comorbidities defined based on the modified Charlson comorbidity index, substance use disorder, and lung cancer or metastatic lung cancer were investigated as covariates. Distribution of acute respiratory conditions was compared among the three sustained-release opioid groups using the log-rank test. Estimates of the incidence of acute respiratory conditions were compared among the groups using a Cox proportional hazards model with time-varying variables. A significant difference in the distribution of acute respiratory conditions was found among the three groups (p < 0.01). Cox regression analysis showed a significantly higher risk of acute respiratory conditions with morphine (hazard ratio [HR]: 3.04, 95% confidence interval [CI]: 1.07-8.65, p = 0.04) compared with oxycodone but no significant difference in risk with oxycodone (HR 0.67, 95% CI: 0.32-1.38, p = 0.27) compared with fentanyl. The findings suggest that the risk of acute respiratory conditions may be lower in patients with CKD who use oxycodone for cancer pain than in those who use morphine. Additionally, no difference in the risk of acute respiratory conditions was found between oxycodone and fentanyl use.

Inference for Type-I and Type-II Hybrid Censored Minimal Repair and Record Data

In this paper, hybrid censoring mechanisms are applied to minimal repair and record data. Based on the derivation of the joint distribution of such data under hybrid censoring, likelihood inference is discussed. For illustration, Type-I and Type-II hybrid censoring schemes are considered for exponential distributions. In particular, the exact (conditional) distribution of the maximum likelihood estimator is obtained for an exponential distribution. This result is used to construct exact (conditional) confidence intervals using the method of pivoting the cumulative distribution function. Finally, the results are illustrated using two data sets taken from the literature on minimal repair models. Although the discussion of the results is in terms of minimal repair models, the results can be applied directly to record value data. By utilizing a connection of minimal repair times to occurrence times of non-homogeneous Poisson processes, a nonparametric estimate for the intensity rate of the process and the underlying lifetime distribution under hybrid censoring is also proposed. The paper is supplemented by simulational results.

Does financial inclusion spur CO2 emissions? The marginal effects of financial sustainability

PurposeThis study aims to examine the relationship between financial inclusion, CO2 emissions and financial sustainability across 17 African countries.Design/methodology/approachData were sourced from the World Development Indicators for the period 2004-2021. The study performs the principal component analysis, panel fixed effects model and quantile regression estimations to investigate the relationship between financial inclusion, CO2 emissions and financial sustainability.FindingsThe study finds that an increase in automated teller machine (ATM) penetration rate, savings and credits increases CO2 emissions. Findings also reveal that financial sustainability reduces financial inclusion, with significant negative effects on the conditional mean of CO2 emissions and the conditional distribution of CO2 emissions across quantiles.Originality/valueThis study is beneficial for policymakers, particularly in the age of digitalization and drive for low-carbon emissions, to develop green credits for energy players and investors to take up renewable and green energy projects characterized by high levels of carbon storage and carbon capture. Further, the banking sector’s credits and liquid assets should be used to finance alternative banking energy-related equipment and services, such as solar photovoltaic wireless ATMs, and fewer bank branches.

Improving forecasting accuracy using quantile regression neural network combined with unrestricted mixed data sampling

A traditional regression method involving time series variables is often observed at the same frequencies. In a situation where the frequencies differ, the higher ones are averaged or aggregated to the lower frequency. A Mixed Data Sampling (MIDAS) regression model was introduced to address such problems. In any country, stakeholders are interested in monitoring and forecasting accurately the Gross Domestic Product (GDP) using the dynamics of macroeconomic variables. We applied the hybrid QRNN-U-MIDAS model to forecast quarterly GDP using monthly and weekly data. The Quantile Regression Neural Network (QRNN) is designed to model nonlinear relationships amongst data sampled at the same frequency. Therefore, we take advantage of QRNN skills using the optimization techniques of gradient descent-based algorithms to optimise the estimated loss function Ea (\tau), and introduce them into the U-MIDAS framework, which can handle mixed data frequencies, and construct a QRNN-U-MIDAS model. The suggested hybrid QRNN-U-MIDAS model was implemented in an R-package that we created to perform both simulation and real-time data applications. The findings indicate that the QRNN-U-MIDAS regression model outperforms competing models in terms of its capacity for prediction across the conditional distribution of a response variable with a comprehensive view of the information contained in the variables, which is lacking in other competing models like U-MIDAS, ANN-U-MIDAS etc. More so, this novel model will add to the existing works of literature on robust forecasting models.

Promoting Renewable Energy Consumption in Sub-Saharan Africa: How Capital Flight Crowds Out the Favorable Effect of Foreign Aid

The study assesses the effect of capital flight in the nexus between foreign aid and renewable energy consumption in 20 countries in sub-Saharan Africa using data for the period 1996 to 2018. The empirical technique employed is interactive quantile regressions, and the following findings are established. Foreign aid increases renewable energy consumption, while capital flight dampens the favorable effect of foreign aid on renewable energy consumption. The underlying significance and corresponding mitigating effect are exclusively relevant to the bottom (i.e., 10th) quantile of the conditional distribution of renewable energy consumption. The findings are robust to simultaneity and unobserved heterogeneity. Policy implications are discussed. JEL Classification: H10; Q20; Q30; O11; O55

On classical and Bayesian inference for bivariate Poisson conditionals distributions: theory, methods and applications

Bivariate count data arise in several different disciplines (epidemiology, marketing, sports statistics, etc., to name but a few) and the bivariate Poisson distribution which is a generalization of the Poisson distribution plays an important role in modeling such data. In this article, we consider the inferential aspect of a bivariate Poisson conditionals distribution for which both the conditionals are Poisson but the marginals are typically non-Poisson. It has Poisson marginals only in the case of independence. It appears that a simple iterative procedure under the maximum likelihood method performs quite well as compared with other numerical subroutines, as one would expect in such a case where the MLEs are not available in closed form. In the Bayesian paradigm, both conjugate priors and non-conjugate priors have been utilized and a comparison study has been made via a simulation study. For illustrative purposes, a real-life data set is re-analyzed to exhibit the utility of the proposed two methods of estimation, one under the frequentist approach and the other under the Bayesian paradigm.

A nonlinear analysis of the impacts of information and communication technologies on environmental quality: A global perspective

Although many current studies have explored a series of factors influencing environmental quality, few have investigated the nonlinearity between information and communication technology (ICT) and the environment. This article uses the method of moments quantile regression (MM-QR) technique to evaluate the nonlinear and heterogeneous effects of all influencing factors on carbon dioxide emissions under various emission distributions. Using panel data from 90 countries, the results first indicate that ICT exerts a significant positive impact on carbon emissions; specifically, the higher the level of ICT development, the greater the carbon emissions. Second, in the conditional distribution of carbon emissions, the influence of ICT exhibits considerable heterogeneity, and ICT development has a larger positive effect on carbon dioxide emissions in countries with lower levels of pollution from a global perspective. Finally, the adverse impact of ICT on the environment can be mitigated through financial system development, urbanization, industrialization, and trade openness. This study enriches existing related literature and provides targeted insights for governments and policymakers in low- and high-pollution countries to reduce environmental pollution and promote sustainable development.

Application of geospatial information systems (GIS) for assessment of the distribution of periodontal disease in Makassar City, South Sulawesi Province, Indonesia.

Addressing the presence of periodontal disease requires a high level of expertise to detect the disease as well as effective communication to understand patients' problems. Based on basic health data from 2018, the prevalence of this problem in Indonesia is approximately 74%. This study examined the distribution of periodontal conditions in March 2021 in Makassar City, the capital of South Sulawesi Province. To determine the distribution of periodontal disease, a questionnaire was used to find out the severity of this issue. A descriptive observational method, used with a cross-sectional design and a web-based geospatial information system (GIS) application linked to ArcGIS, was conducted. The results showed thatout of the 15 districts in Makassar City, the island district of Sangkarranghad had the highest presence of periodontal disease. Three other districts were classified as also belonging to this low category, while 11 other ones exhibited a medium disease incidence score.

A novel time series probabilistic prediction approach based on the monotone quantile regression neural network

Quantile regression is widely applied in various fields such as economy, energy, meteorological prediction research in recent years since it does not require distribution assumptions, has relatively loose conditions, and can effectively estimate the uncertainty of time series forecasting. In this paper, a monotone quantile regression neural network (MQRNN) framework is constructed for time series quantile forecasting. The proposed approach takes the monotonicity of quantile into consideration and handles the quantile crossing problem by adding the quantile information into the input structure and using the gradient based point-wise loss function. Aiming at the complex characteristics of time series, such as time-varying and asymmetric heavy-tailed features, a new quantile function is utilized to describe the complete conditional distribution information of data. Under this model framework, non-crossing multiple quantiles can be predicted simultaneously. The proposed approach is implemented based on artificial neural networks, and the constructed model is applied to actual data in different fields. The experimental results demonstrate that the proposed method combined with long short-term memory (LSTM) can provide accurate and reliable multi-quantile prediction, and alleviate the problem of quantile crossing.

Bayesian boundary trend filtering

Estimating boundary curves has many applications such as economics, climate science, and medicine. Bayesian trend filtering has been developed as one of locally adaptive smoothing methods to estimate the non-stationary trend of data. This paper develops a Bayesian trend filtering for estimating the boundary trend. To this end, the truncated multivariate normal working likelihood and global-local shrinkage priors based on the scale mixtures of normal distribution are introduced. In particular, well-known horseshoe prior for difference leads to locally adaptive shrinkage estimation for boundary trend. However, the full conditional distributions of the Gibbs sampler involve high-dimensional truncated multivariate normal distribution. To overcome the difficulty of sampling, an approximation of truncated multivariate normal distribution is employed. Using the approximation, the proposed models lead to an efficient Gibbs sampling algorithm via the Pólya-Gamma data augmentation. The proposed method is also extended by considering a nearly isotonic constraint. The performance of the proposed method is illustrated through some numerical experiments and real data examples.

Effects of private health insurance on medical expenditure and health service utilization in South Korea: a quantile regression analysis

BackgroundDespite universal health insurance, South Korea has seen a sharp increase in the number of people enrolled in supplemental private health insurance (PHI) during the last decade. This study examined how private health insurance enrollment affects medical expenditure and health service utilization.MethodsUnbalanced panel data for adults aged 19 and older were constructed using the 2016–2018 Korea Health Panel Survey. Quantile regression for medical cost, and quantile count regression for health service utilization were utilized using propensity score-matched data. We included 17 variables representing demographic, socioeconomic, and health information, as well as medical costs and use of outpatient and inpatient care.ResultsWe discovered that PHI enrollees’ socioeconomic and health status is more likely to be better than PHI non-enrollees’. Results showed that private health insurance had a greater effect on the lower quantiles of the conditional distribution of outpatient costs (coefficient 0.149 at the 10th quantile and 0.121 at the 25th quantile) and higher quantiles of inpaitent care utilization (coefficient 0.321 at the 90th quantile for days of hospitalization and 0.076 at the 90th quantile for number of inpatient visits).ConclusionsPHI enrollment is positively correlated with outpatient costs and inpatient care utilization. Government policies should consider these heterogeneous distributional effects of private health insurance.

Sig‐Wasserstein GANs for conditional time series generation

AbstractGenerative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high‐dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time‐series data. Furthermore, long time‐series data streams hugely increase the dimension of the target space, which may render generative modeling infeasible. To overcome these challenges, motivated by the autoregressive models in econometric, we are interested in the conditional distribution of future time series given the past information. We propose the generic conditional Sig‐WGAN framework by integrating Wasserstein‐GANs (WGANs) with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterizes the law of the time‐series model. In particular, we develop the conditional Sig‐ metric that captures the conditional joint law of time series models and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators, which alleviates the need for expensive training. We validate our method on both synthetic and empirical dataset and observe that our method consistently and significantly outperforms state‐of‐the‐art benchmarks with respect to measures of similarity and predictive ability.

Deep Learning-Enabled Heterogeneous Transfer Learning for Improved Network Attack Detection in Internal Networks

Cybersecurity faces constant challenges from increasingly sophisticated network attacks. Recent research shows machine learning can improve attack detection by training models on large labeled datasets. However, obtaining sufficient labeled data is difficult for internal networks. We propose a deep transfer learning model to learn common knowledge from domains with different features and distributions. The model has two feature projection networks to transform heterogeneous features into a common space, and a classification network then predicts transformed features into labels. To align probability distributions for two domains, maximum mean discrepancy (MMD) is used to compute distribution distance alongside classification loss. Though the target domain only has a few labeled samples, unlabeled samples are adequate for computing MMD to align unconditional distributions. In addition, we apply a soft classification scheme on unlabeled data to compute MMD over classes to further align conditional distributions. Experiments between NSL-KDD, UNSW-NB15, and CICIDS2017 validate that the method substantially improves cross-domain network attack detection accuracy.

A multi-constrained domain adaptation network for remaining useful life prediction of bearings

The prediction of the remaining useful life (RUL) of bearings could reduce maintenance costs and prevent serious consequences. Due to the variable working conditions of bearings, domain adaptation methods for cross-domain RUL prediction have received extensive attention. However, existing research often focuses solely on aligning marginal distributions while overlooking the alignment of conditional distributions and fails to retain domain-specific information during the alignment process. To address these issues, we propose a multi-constrained domain adaptation (MCDA) method for cross-domain RUL prediction. MCDA method is based on an adversarial domain adaptation architecture, where the improved domain discriminator aligns the data features of different degradation stages separately to achieve the alignment of marginal distribution and simultaneously aligns the conditional distribution. The state discrimination module is introduced to align the marginal and conditional distributions while retaining the target domain-specific information. To confirm the effectiveness of the proposed method, we conducted RUL prediction tasks for bearings in various cross-domain scenarios using both publicly and self-built datasets. The experimental results showed that the MCDA method could better handle cross-domain RUL prediction tasks compared to existing methods.

Iteratively Reweighted Least Squares Method for Estimating Polyserial and Polychoric Correlation Coefficients

An iteratively reweighted least squares (IRLS) method is proposed for the estimation of polyserial and polychoric correlation coefficients in this article. It calculates the slopes in a series of weighted linear regression models fitting on conditional expected values. For polyserial correlation, conditional expectations of the latent predictor is derived from the observed ordinal categorical variable, and the regression coefficient is obtained with the weighted least squares method. In estimating polychoric correlation coefficient, conditional expectations of the response variable and the predictor are updated in turns. Standard errors of the estimators are obtained using the delta method based on data summaries instead of the whole data. Conditional univariate normal distribution is exploited and a single integral is numerically evaluated in the proposed algorithm, comparing to the double integral computed numerically based on the bivariate normal distribution in the traditional maximum likelihood (ML) approaches. This renders the new algorithm very fast in the estimation of both polyserial and polychoric correlation coefficients. Thorough simulation studies are conducted to compare the performances of the proposed method with the classical ML methods. Real data analyses illustrate the advantage of the new method in computing speed. Supplementary materials for this article are available online.

Holding the economy by the tail: analysis of short- and long-run macroeconomic risks

We put forward a macro-financial empirical modeling framework that can examine the tails of distributions of macroeconomic variables and the implied risks. It does so without quantile regression, also allowing for non-normal distributions. The framework offers a number of relevant insights into higher moments of the US output growth distribution, as well as the effects of monetary policy and financial (risk premia) shocks on downside macroeconomic risk. This is not only from the short-run perspective but also from the long-run perspective, which has remained largely unexamined in the existing Macro-at-Risk literature. In particular, we estimate the short-run (conditional) and long-run US output growth distributions and study their evolution. The short-run analysis finds that monetary policy and financial shocks render the conditional output growth distribution asymmetric. As such, they affect downside risk over and above their impact on the conditional mean that policymakers routinely focus on. The long-run analysis indicates that US output growth left-tail risk showed a general downward trend in the two decades preceding the Global Financial Crisis (GFC), but this trend got reversed post-2008. Our examination strongly points to the adopted unconventional monetary policy framework featuring quantitative easing as a potential source of elevated long-run downside tail risk in the post-GFC period.

Flow-Based Spatio-Temporal Structured Prediction of Motion Dynamics.

Conditional Normalizing Flows (CNFs) are flexible generative models capable of representing complicated distributions with high dimensionality and large interdimensional correlations, making them appealing for structured output learning. Their effectiveness in modelling multivariates spatio-temporal structured data has yet to be completely investigated. We propose MotionFlow as a novel normalizing flows approach that autoregressively conditions the output distributions on the spatio-temporal input features. It combines deterministic and stochastic representations with CNFs to create a probabilistic neural generative approach that can model the variability seen in high-dimensional structured spatio-temporal data. We specifically propose to use conditional priors to factorize the latent space for the time dependent modeling. We also exploit the use of masked convolutions as autoregressive conditionals in CNFs. As a result, our method is able to define arbitrarily expressive output probability distributions under temporal dynamics in multivariate prediction tasks. We apply our method to different tasks, including trajectory prediction, motion prediction, time series forecasting, and binary segmentation, and demonstrate that our model is able to leverage normalizing flows to learn complicated time dependent conditional distributions.

Unlocking information technology infrastructure for promoting climate resilience and environmental quality

We examine the role of Information Technological Infrastructure (ITI) in promoting climate resilience and environmental quality in OECD countries. Our empirical strategy is framed in the advanced econometrics methodology. Our analysis begins with standard specification, which controls for unobserved factors in the panel data. We then explore the variables' long and short-run relationships using the Generalised Method of Moment dynamic family specifications. We also evaluate the heterogeneity drivers of environmental quality using Quantile via Moment. The analysis is also extended using alternative and complementary statistical procedures by Hausman–Taylor and Feasible Generalised Least Squares as robustness checks. Our findings indicate that ITI and renewable energy significantly mitigates carbon emissions and can be helpful in achieving a net-zero target. In contrast, the empirical evidence reveals that economic growth and non-renewable energy usage are harmful to the environment. The finding also suggests a significant degree of heterogeneity exists in the covariates on the conditional distribution of environmental quality and its driven factors. While the findings reaffirm the significance of ITI in ensuring careful planning and monitoring of critical infrastructure, they also show that ITI can be used to balance the entire system by creating resilience. We strongly suggest that policymakers should use ITI to spur innovation and drive better solutions for energy transition and environmental improvement.

A conditional extreme value distribution method for dynamic reliability analysis of stochastic structures

An efficient post-processing simulation method is proposed to estimate small failure probabilities of stochastic dynamic structures involving the inherent randomness of structural physical-geometrical parameters and external excitations. To extract a small failure probability, the proposed method introduces an intermediate event to represent the realizations of extreme structural response in the tail of distribution. With the aid of this intermediate event, structural failure probability is reformulated as a product of the event’s occurrence probability and the conditional exceedance probability of extreme response when the event occurs. The latter corresponds to a distribution of extreme response under the condition of the intermediate event, referred to as the conditional extreme value distribution (CEVD). Accordingly, the proposed method is termed the CEVD method. To reconstruct the CEVD, a truncated shifted generalized lognormal distribution model is employed. Bayesian estimation method is utilized to determine the two shape parameters of this model based on the samples of both original extreme value distribution and CEVD, where the CEVD samples are generated by Markov chain Monte Carlo sampling. The efficiency and accuracy of the proposed method are demonstrated through two numerical examples considering seismic reliability analyses of a 10-story nonlinear frame and a soil-foundation-structure interaction system.