Multivariate quantiles are of increasing importance in applications of hydrology. This calls for reliable methods to evaluate the precision of the estimated quantile sets. Therefore, we focus on two recently developed approaches to estimate confidenc...
In this short paper, we aim at a qualitative framework for modeling multivariate decision problems where each alternative is characterized by a set of properties. To this extent, we consider convex spaces as underlying universes and make use of latti...
In structural reliability analysis, sensitivity analysis (SA) can be used to measure how an input variable influences the failure probability Pf of a structure. Although the reliability is usually expressed via Pf, Eurocode building design standards...
We employ the quantile-coherency approach and causality-in-quantile method to revisit the roles of Bitcoin, U.S. dollar, crude oil and gold for USA, Chinese, UK, and Japanese stock markets. The main results show that the impact of global financial as...
In this paper, we provide a novel Bayesian solution to forecasting extreme quantile thresholds that are dynamic in nature. This is an important problem in many fields of study including climatology, structural engineering, and finance. We utilize res...
It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of ex...
A new method to estimate longevity risk based on the kernel estimation of the extreme quantiles of truncated age-at-death distributions is proposed. Its theoretical properties are presented and a simulation study is reported. The flexible yet accurat...
Ranking variables according to their relevance to predict an outcome is an important task in biomedicine. For instance, such ranking can be used for selecting a smaller number of genes for then applying other sophisticated experiments only on genes i...
The combined use of weather generators (WG) and hydrological models (HM) in what is called synthetic continuous simulation (SCS) has become a common practice for carrying out flood studies. However, flood quantile estimations are far from presenting...
This study examines the role of investor sentiment in asset pricing within a frontier market, focusing on Vietnam. Using a comprehensive dataset covering the period 2015–2025 with 4018 observations, sentiment indices are constructed from both m...
Estimating extreme quantiles and the number of future exceedances is an important task in financial risk management. More important than estimating the quantile itself is to insure zero coverage error, which implies the quantile estimate should, on a...
Approximating quantiles and distributions over streaming data has been studied for roughly two decades now. Recently, Karnin, Lang, and Liberty proposed the first asymptotically optimal algorithm for doing so. This manuscript complements their theore...
For a univariate distribution, its M-quantiles are obtained as solutions to asymmetric minimization problems dealing with the distance of a random variable to a fixed point. The asymmetry refers to the different weights awarded to the values of the r...
Finite mixture regression identifies homogeneous groups within a sample and computes the regression coefficients in each group. Groups and group coefficients are jointly estimated using an iterative approach. This work extends the finite mixture esti...
The simultaneous estimation of multiple quantiles is a crucial statistical task that enables a thorough understanding of data distribution for robust analysis and decision-making. In this study, we adopt a Bayesian approach to tackle this critical ta...
The principle of maximum entropy (POME) has been used for a variety of applications in hydrology, however it has not been used in confidence interval estimation. Therefore, the POME was employed for confidence interval estimation for precipitation qu...
The article introduces quantile deviation l as a new sensitivity measure based on the difference between superquantile and subquantile. New global sensitivity indices based on the square of l are presented. The proposed sensitivity indices are compar...
In this paper, we obtain the convergence rate for the high-dimensional sample quantiles with the
This article develops a boundary-adaptive nonparametric methodology for estimating the geometric conditional quantiles of a multivariate response when the conditioning covariate is supported on the simplex—an important case, as it is the natura...
We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily real...
In this article, we propose and study a class of multivariate regression models that account for ignorable missing data in skewed, potentially heavy-tailed response vectors with positive components. It can be used to estimate the marginal quantiles o...
Statistical monitoring of lifetime processes is commonly based on a single process characteristic, which may fail to detect distributional changes that affect different aspects of process behavior. This study proposes a component-wise adaptive joint...
A Hermite-based framework for reliability assessment within the limit state method is developed in this paper. Closed-form design quantiles under a four-moment Hermite density are derived by inserting the Gaussian design quantile into a calibrated cu...
This study investigated the relationship between R&D investments and financial and environmental performance. The direction, size, and significance of various phases of these variables were generated using the bootstrap Fourier quantiles Granger...
Since carbon price volatility is critical to the risk management of the CO2 emissions trading market, research has focused on energy prices and macroeconomic drivers which cause changes in carbon prices and make the carbon market more volatile than o...
In this study, we explore the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR). The UQR provides better interpretative and thus policy-relevant information on the predictive effect of the target varia...
Deglobalization, as opposed to the term globalization, appears in the world order due to local solutions to problems and border controls, ignoring the principles of treaties, trade wars, and the expansion of regionalism. In addition, slowbalization h...
GARCH models play a fundamental role in modeling time-varying volatility in financial return series. In practice, financial returns are also well known to exhibit heavy-tailed distributions, which naturally motivates the use of quasi-maximum exponent...
A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc. Existing outlier detection algorithms, which can be divided into supervised metho...
A spectrum of upper bounds (Qα(X ; p) αε[0,∞] on the (largest) (1-p)-quantile Q(X;p) of an arbitrary random variable X is introduced and shown to be stable and monotonic in α, p, and X , with Q0(X ;p) = Q(X;p). If p is small enough and...
Achieving environmental sustainability has become an urgent priority in the era of rapid digital economic expansion, which presents both opportunities and challenges for environmental sustainable development. This study investigates the impact of dig...
Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potenti...
The high quantile estimation of heavy tailed distributions has many important applications. There are theoretical difficulties in studying heavy tailed distributions since they often have infinite moments. There are also bias issues with the existing...
Supply chain disruptions have become a major source of macro-financial stress, yet their implications for green finance remain underexplored. This paper investigates the state-dependent connectedness between supply-side bottlenecks and the green fina...
We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing “at scale” within operational contexts. The experiment is set up usi...
This paper uses nonparametric causality-in-quantiles tests to examine the asymmetric effects of climate risk perception (CRP) on the thermal and coking coal futures high-frequency returns and volatilities. The results show that CRP significantly impa...
In the present paper, we study quantile risk measures and their domain. Our starting point is that, for a probability measure Q on the open unit interval and a wide class
Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribu...
There has always been a complex relationship between uncertainty and crude oil prices. Three types of uncertainty, i.e., economic policy uncertainty, geopolitical risk uncertainty, and climate policy uncertainty (EPU, GPR, and CPU for short), have ex...
In this article, we focus on the efficient estimators of the derivative of the nonparametric function in the nonparametric quantile regression model. We develop two ways of combining quantile regression information to derive the estimators. One is th...
Climate change and global warming have been shown to increase the frequency and intensity of extreme weather events. Concurrently, substantial efforts are being directed toward fostering the transition to a low-carbon economy. These concurrent trends...
Wind power forecasting techniques have received substantial attention recently due to the increasing penetration of wind energy in national power systems. While the initial focus has been on point forecasts, the need to quantify forecast uncertainty...
We investigate the risk–return trade-off on the US and European stock markets. We investigate the non-linear risk–return trade-off with a special eye to the tails of the stock returns using quantile regressions. We first consider the US stock market...
In this paper, we investigate the relationship between the RavenPack news-based index associated with coronavirus outbreak (Panic, Sentiment, Infodemic, and Media Coverage) and returns of two commodities—Bitcoin and gold. We utilized the novel...
In many society and natural science fields, some stochastic orders have been established in the literature to compare the variability of two random variables. For a stochastic order, if an individual (or a unit) has some property, sometimes we need t...
The paper addresses three objectives: the first is a presentation and overview of some important developments in quantile times series approaches relevant to demographic applications—secondly, development of a general framework to represent qua...
This paper examines the effect of board characteristics, especially board independence, on firm performance from a dynamic perspective through copula-based quantile regression approaches, which allow us to focus on changes at different points in the...
This study investigates the nonlinear and regime-dependent relationship between climate policy uncertainty (CPU) and sovereign credit default swap (CDS) spreads across a panel of developed and emerging economies from February 2010 to March 2025. Util...
The objective of the current examination is to identify the dynamic relationship between the textile industry and energy intensity. The study evaluates the asymmetric impact of textile manufacturing on energy intensity in leading Asian economies base...
Quantile functions provide a natural representation of probability distributions and offer direct advantages for random variate generation, simulation, and quantile-based statistical analysis. In this paper, we develop a quantile-differential constru...
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