Since its origins and numerous applications in material science, the Birnbaum–Saunders family of distributions has now found widespread uses in some areas of the applied sciences such as agriculture, environment and medicine, as well as in quality co...
Simulation-based optimization design is becoming increasingly important in engineering. However, carrying out multi-point, multi-variable, and multi-objective optimization work is faced with the “Curse of Dimensionality”, which is highly...
Flooding often has a negative impact on society. In particular, widespread flood events can cause a lot of damage. These events are often spatially and temporally heterogeneous and should be duly considered for an appropriate analysis of flooding. Th...
Background: Nocturia is common and associated with multiple disease states. Many potential mechanisms have been proposed for nocturia, which also remains challenging to manage. Purpose: To use multivariate analysis to determine which combinations of...
Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data st...
This study examines the impact of various pretreatments on the composting of white grape skin (Vitis vinifera cv. Graševina) over a 30-day aerobic composting period. The pretreatments included the extraction of bioactive compounds from the gra...
To model multivariate, possibly heavy-tailed data, we compare the multivariate normal model (N) with two versions of the multivariate Student model: the independent multivariate Student (IT) and the uncorrelated multivariate Student (UT). After recal...
Projection to Latent Structures (PLS) regression, a generalization of multiple linear regression, is used to model two datasets (40 observed data points each) of adsorption removal of three pharmaceutical compounds (PhCs), of different therapeutic cl...
Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula a...
Joint models have become the standard framework for simultaneously analyzing longitudinal biomarkers and time-to-event outcomes because they account for their underlying association. However, most existing joint models are restricted to a single long...
In this article, a multivariate extension of the unit-sinh-normal (USHN) distribution is presented. The new distribution, which is obtained from the conditionally specified distributions methodology, is absolutely continuous, and its marginal distrib...
Many real-world multivariable systems need to be modeled to capture the interconnected behavior of their physical variables and to understand how uncertainty in actuators and sensors affects the system dynamics. In system identification, some estimat...
While existing mixed-effects models address multivariate outcomes, nonlinearity, or non-normality separately, their joint integration remains limited in medical research. This study proposes a flexible semi-parametric multivariate mixed-effects model...
This paper considers a multivariate time series model for stock prices in the stock market. A multivariate heterogeneous autoregressive (HAR) model is adopted with exponentially decaying coefficients. This model is not only suitable for multivariate...
With the purpose of introducing dependence between different types of claims, multivariate collective models have recently gained a lot of attention. However, when it comes to the evaluation of the corresponding compound distribution, the problems in...
Multivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields. To effectively characterize such data...
In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network mod...
Several extensions of the basic scalar variable logistic density to the multivariate and matrix-variate cases, in the real and complex domains, are given where the extended forms end up in extended zeta functions. Several cases of multivariate and ma...
In this work, multivariate heterogeneous autoregressive-realized volatility (HAR-RV) models are discussed with their least squares estimations. We consider multivariate HAR models of order p with q multiple assets to explore the relationships between...
Count data appears in many research fields and exhibits certain features that make modeling difficult. Most popular approaches to modeling count data can be classified into observation and parameter-driven models. In this paper, we review two models...
Artificial intelligence (AI) technology plays a crucial role in infectious disease outbreak prediction and control. Many human interventions can influence the spread of epidemics, including government responses, quarantine, and economic support. Howe...
Bimodal distributions have rarely been studied although they appear frequently in datasets. We develop a novel bimodal distribution based on the triangular distribution and then expand it to the multivariate case using a Gaussian copula. To determine...
This paper investigated an ultrahigh-dimensional feature screening approach for additive models with multivariate responses. We proposed a nonparametric screening procedure based on random vector correlations between each predictor and multivariate r...
Maintaining stable prices is one of the goals of monetary policy makers. Since its formation, inflation has been a key issue and priority for every Pakistani government; it is a fundamental macroeconomic variable that plays a significant role in a na...
Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a signi...
With the increasing complexity of the active distribution network (ADN) due to distributed generation (DG) integration, together with the electricity market evolution, the traditional ADN is divided into multiple areas to operate independently. Due t...
Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD)-based mo...
The solvent dehydration tower in Purified Terephthalic Acid (PTA) production is characterized by strong multivariable interactions, slow vapor–liquid dynamics, and high sensitivity to upstream disturbances, often limiting the effectiveness of c...
A comprehensive auto insurance policy usually provides the broadest protection for the most common events for which the policyholder would file a claim. On the other hand, some insurers offer extended third-party car insurance to adapt to the persona...
This paper provides a complete matrix analysis on equivalence problems of estimation and inference results under a true multivariate linear model
The effectiveness of model-based multivariable controllers depends on the quality of the model used. In addition to satisfying standard accuracy requirements for model structure and parameter estimates, a model to be used in a controller must also sa...
Precipitation is influential in determining runoff at different scales of analysis, whether in minutes, hours, or days. This paper proposes the use of a multisite multivariate model of precipitation at a daily scale. Stochastic models allow the gener...
Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task beca...
The rapid progress in artificial intelligence technologies has significantly impacted the global economy, driving transformative changes in manufacturing and giving rise to intelligent manufacturing. In this context, multivariable time-series data ha...
Cleanrooms are a fundamental setup of every semiconductor and life science factory, representing strictly controlled ambient conditions designed to minimize the air contamination that could affect a product within the production process. A cleanroom...
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent N...
In this study, monthly streamflow and satellite-based actual evapotranspiration data (AET) were used to evaluate the Soil and Water Assessment Tool (SWAT) model for the calibration of an experimental sub-basin with mixed land-use characteristics in A...
The prediction of crop growth and nitrogen status is essential for agricultural development and food security under climate change scenarios. Crop models are powerful tools for simulating crop growth and their responses to environmental variables, bu...
Owing to frequent traffic accidents and casualties nowadays, the ability to predict the number of traffic accidents in a period is significant for the transportation department to make decisions scientifically. However, owing to many variables affect...
Waste stabilization ponds (WSPs) as natural wastewater treatment plants are commonly utilized for wastewater treatment due to their simple design, low cost, and low-skilled operator requirements. Large-scale studies assessing the performance of WSPs...
The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment...
The present study proposes developing a multivariate model that predicts water availability in Mexico through 26 variables related to aquifers, renewable water, demographic characteristics, rivers and basins, dams, and irrigation factors. The informa...
We consider a constructive definition of the multivariate Pareto that factorizes the random vector into a radial component and an independent angular component. The former follows a univariate Pareto distribution, and the latter is defined on the sur...
This study presents a novel optimal fuzzy logic control (FLC) strategy based on feedback linearization for the regulation of multivariable nonlinear systems. Building upon an enhanced Takagi–Sugeno (T-S) model previously developed by the author...
Many geodetic measurement data can be modelled as a multivariate time series consisting of a deterministic (“functional”) model describing the trend, and a stochastic model of the correlated noise. These data are also often affected by outliers and t...
Expectation Maximization (EM) iterations for Gaussian mixture models (GMMs) are highly sensitive to initial parameters, which calls for developing robust initialization methods. For multidimensional GMMs, the problem is even more severe than for univ...
Multivariate regression models for real-time coastal air quality forecasting were suggested from 18 to 27 March 2015, with a total of 15 kinds of hourly input data (three-hours-earlier data of PM and gas with meteorological parameters from Kangnung (...
The multivariate random coefficient autoregression (RCAR) process is widely used in time series modeling applications. Random autoregressive coefficients are usually assumed to be independent and identically distributed sequences of random variables....
Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1) artificial intelligence, (2) statistical methods,...
17 July 2021
Bronchiolitis has a high morbidity in children under 2 years old. Respiratory syncytial virus (RSV) is the most common pathogen causing the disease. At present, there is only a costly humanized monoclonal RSV-specific antibody to prevent RSV. However...
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