This study proposes a modified VAR-deGARCH model, denoted by M-VAR-deGARCH, for modeling asynchronous multivariate financial time series with GARCH effects and simultaneously accommodating the latest market information. A variational Bayesian (VB) procedure is developed for the M-VAR-deGARCH model to infer structure selection and parameter estimation. We conduct extensive simulations and empirical studies to evaluate the fitting and forecasting performance of the M-VAR-deGARCH model. The simulation results reveal that the proposed VB procedure produces satisfactory selection performance. In addition, our empirical studies find that the latest market information in Asia can provide helpful information to predict market trends in Europe and South Africa, especially when momentous events occur.

In practical applications, rapid prediction and optimization of heat transfer performance are essential for premixed methane impinging flame jets (PMIFJs). This study uses computational fluid dynamics (CFD) combined with a methane detailed chemical reaction mechanism (GRI–Mech 3.0) to study the equivalence ratio (ϕ), Reynolds number (Re) of the mixture, and the normalized nozzle–to–plate distance (H/d) on the heat transfer performance of PMIFJs. Moreover, the Kriging model (KM) was used to construct a prediction model of PMIFJ heat transfer performance. A genetic algorithm (GA) was used to determine the maximum likelihood function (MLE) of the model parameters for constructing KM and identify the points with the maximum root mean square error (RMSE) as the new infilled points for surrogate–based optimization (SBO). Combining these methods to analyze the simulation results, the results show that the global heat transfer performance of PMIFJs is enhanced with the increase in ϕ, the increase in Re, and the decrease in H/d. Sensitivity analysis points out that Re and ϕ significantly affect enhanced heat transfer, while H/d has a relatively small effect. In addition, GA was also used to search for the optimal heat transfer performance, and the global heat transfer performance at specific conditions was significantly enhanced. This study deepens the understanding of the heat transfer mechanism of impinging flame jets and provides an efficient method framework for practical applications.

In computer experiments, Gaussian process (GP) models are commonly used for emulation. However, when both qualitative and quantitative factors are in the experiments, emulation using GP models becomes challenging. In particular, when the qualitative factors contain many categories in the experiments, existing methods in the literature become cumbersome due to the curse of dimensionality. Motivated by the computer experiments for the design of a cooling system, a new tree-based GP is proposed that emulates computer models with many-category qualitative factors, which we call category tree GP. The proposed method incorporates a tree structure to split the categories of the qualitative factors, and GP or mixed-input GP models are employed for modeling the simulation outputs in the leaf nodes. The splitting rule takes into account the cross-correlations between the categories of the qualitative factors, which have been shown by a recent theoretical study to be a crucial element for improving the prediction accuracy. In addition, a pruning procedure based on the cross-validation error is proposed to ensure the prediction accuracy. The application to the design of a cooling system indicates that the proposed method not only enjoys marked computational advantages and produces accurate predictions, but also provides valuable insights into the cooling system by discovering the tree structure.

This study proposes a non-parametric ICA method, called ECOPICA, which describes the joint distribution of data by empirical copulas and measures the dependence between recovery signals by an independent test statistic. We employ the grasshopper algorithm to optimize the proposed objective function. Several acceleration tricks are further designed to enhance the computational efficiency of the proposed algorithm under the parallel computing framework. Our simulation and empirical analysis show that ECOPICA produces better and more robust recovery performances than other well-known ICA approaches for various source distribution shapes, especially when the source distribution is skewed or near-Gaussian.

Longitudinal studies have been conducted in various fields, including medicine, economics and the social sciences. In this paper, we focus on longitudinal ordinal data. Since the longitudinal data are collected over time, repeated outcomes within each subject may be serially correlated. To address both the within-subjects serial correlation and the specific variance between subjects, we propose a Bayesian cumulative probit random effects model for the analysis of longitudinal ordinal data. The hypersphere decomposition approach is employed to overcome the positive definiteness constraint and high-dimensionality of the correlation matrix. Additionally, we present a hybrid Gibbs/Metropolis-Hastings algorithm to efficiently generate cutoff points from truncated normal distributions, thereby expediting the convergence of the Markov Chain Monte Carlo (MCMC) algorithm. The performance and robustness of our proposed methodology under misspecified correlation matrices are demonstrated through simulation studies under complete data, missing completely at random (MCAR), and missing at random (MAR). We apply the proposed approach to analyze two sets of actual ordinal data: the arthritis dataset and the lung cancer dataset. To facilitate the implementation of our method, we have developed BayesRGMM, an open-source R package available on CRAN, accompanied by comprehensive documentation and source code accessible at https://github.com/kuojunglee/BayesRGMM/ .

The traditional way in statistics to find optimal designs for regression models is an analytical approach. Technical conditions that may be restrictive in practice are sometimes imposed to obtain the analytical results. Even then, the mathematical technique is invariably not amendable to find an optimal design under a different criterion or for the same criterion with a slightly changed model, suggesting that developing flexible and effective algorithms to search for the optimum is very useful. In particular, numerical results from an algorithm can be helpful to find analytical descriptions of optimal designs. As an example, particle swarm optimization has been shown to be quite effective for finding optimal designs for hard design problems and this paper demonstrates how its output can be used to find new analytic A-optimal approximate designs for the Gamma and inverse Gaussian models, each with the inverse link function. The methodology is quite general and may be applied to find analytical A-optimal designs for other models, like the Poisson model with the log link function, or other types of optimal designs.

Objective: To determine whether feeding progression patterns in the first eight postnatal weeks, depicted by clustering analysis of daily enteral feeding volume, are associated with longitudinal head-circumference (HC) growth and neurodevelopmental outcomes in extremely preterm (EP) infants. Methods: 200 infants who were admitted at gestational ages 23–27 weeks between 2011 and 2018; survived to discharge; and underwent longitudinal HC growth measurements at birth, term-equivalent age (TEA), corrected age (CA) 6-month, 12-month, and 24-month; and neurodevelopmental assessment using the Bayley Scales of Infant Development at CA 24 months were included for analysis. Results: kmlShape analysis identified two distinct enteral feeding progression patterns: fast progression in 131 (66%) infants and slow progression in 69 (34%) infants. Compared to the fast progression group, the slow progression group showed significantly lower daily enteral volumes after day 13, was older in postnatal age reaching full feeding, had a higher rate of Delta z scores of HC (zHC) < −1 (p < 0.001) between birth and TEA, and displayed lower longitudinal zHC from TEA to CA 24 months. The slow progression group also showed higher rates of microcephaly [42% vs. 16%, p < 0.001; adjusted odd ratio (aOR): 3.269, p = 0.001] and neurodevelopmental impairment (NDI) (38% vs. 19%, p = 0.007; aOR: 2.095, p = 0.035) at CA 24 months. For NDI, the model including feeding progression patterns showed a lower Akaike information criterion score and a better goodness of fit than the model that did not include feeding patterns. Conclusion: Characterizing feeding progression pattern may help identify EP infants at high-risk of head-size growth faltering and NDI at early childhood.

Designing longitudinal studies is generally a very challenging problem because of the complex optimization problems. We show the popular nature-inspired metaheuristic algorithm, Particle Swarm Optimization (PSO), can find different types of optimal exact designs for longitudinal studies with different correlation structures for different types of models. In particular, we demonstrate PSO-generated D-optimal longitudinal studies for the widely used Michaelis-Menten model with various correlation structures agree with the reported analytically derived locally D-optimal designs in the literature when there are only 2 observations per subject, and their numerical D-optimal designs when there are 3 and 4 observations per subject. We further show the usefulness of PSO by applying it to generate new locally D-optimal designs to estimate model parameters when there are 5 or more observations per subject. Additionally, we find various optimal longitudinal designs for a growth curve model commonly used in animal studies and for a nonlinear HIV dynamic model for studying T-cells in AIDS subjects. In particular, c-optimal exact designs for estimating one or more functions of model parameters (c-optimality) were found, along with other types of multiple objectives optimal designs.

Gaussian process (GP) models are commonly used in the analysis of computer experiments. Variable selection in GP models is of significant scientific interest but existing solutions remain unsatisfactory. For each variable in a GP model, there are two potential effects with different implications: one is on the mean function, and the other is on the covariance function. However, most of the existing research on variable selection for GP models has focused only on one of the effects. To tackle this problem, we propose an indicator-based Bayesian variable selection procedure to take into account the effects from both the mean and covariance functions. A variable is defined to be inactive if both effects are not significant, and an indicator is used to represent the variable being active or not. For active variables, the proposed method adopts different prior assumptions to capture the two effects. The performance of the proposed method is evaluated by both simulations and real applications in computer experiments.

We present a TriSNAR modeling framework for understanding the dynamic interactions of multiple markets for Bitcoin trading, including market efficiency, and for identifying influential exchanges in the global trading network. We consider two types of influential exchanges from the perspectives of investors, regulators, and policymakers: exchanges that are market leaders and exchanges potentially used for market manipulation. Among 194 Bitcoin exchanges, we find that exchange Kraken was the leading exchange prior to the market frenzy of 2017. We also find a fraud-related exchange (Bitfinex) where some other exchanges display a similar role in the price discovery network than this exchange, raising questions about whether they may also be used for fraudulent activities. In addition, price discovery shows that the Bitcoin exchange network has been decreasing in efficiency from 2015 to 2017, and it has been increasingly efficient since 2018. We investigate the finite sample and asymptotic properties of TriSNAR. Compared to alternative methods, TriSNAR outperforms in terms of accuracy, runtime, and ability to discover multi-market network structures.

AimTo determine whether early‐life respiratory trajectories are associated with neurodevelopmental impairment (NDI) in infants born very and extremely preterm.MethodThe daily type of respiratory supports in the first 8 weeks after birth were analysed in 546 infants (285 males, 261 females; median gestational age = 28.0 weeks, interquartile range = 3 weeks), comprising 301 infants born very preterm (gestation = 28–30 weeks) and 245 infants born extremely preterm (gestation <28 weeks), who survived to discharge from 2004 to 2018 and received follow‐up assessment by Bayley Scales of Infant and Toddler Development at a corrected age of 24 months. NDI included cognition or motor impairment, moderate and severe cerebral palsy, or visual and hearing impairment.ResultsClustering analysis identified three respiratory patterns with increasing severity: improving; slowly improving; and delayed improvement. These were significantly associated with increasing rates of NDI in infants born very and extremely preterm and smaller head circumference in infants born extremely preterm (both p < 0.001). By day 28, the proportion of infants who were under different categories of ventilation support significantly differed according to the three trajectory groups in infants born very and extremely preterm (both p < 0.05). Models that included adverse respiratory trajectories demonstrated more negative impacts on neurodevelopment than those without.InterpretationAn adverse early‐life respiratory trajectory was associated with NDI at follow‐up, especially in infants born extremely preterm, suggesting a lung–brain axis of preterm birth.What this paper adds Clustering analysis identified three respiratory trajectories with increasing severity in infants born preterm. Increasing severity of respiratory trajectories was associated with increasing rates of neurodevelopmental impairment. Adverse respiratory trajectories had a significantly negative impact on neurodevelopmental outcomes.

The class of nature-inspired metaheuristic algorithms is increasingly used to tackle all kinds of optimization problems across disciplines. It also plays an important component in artificial intelligence and machine learning. Members in this class are general purpose optimization tools that virtually require no assumptions for them to be applicable. There are many such algorithms, and to fix ideas, we review one of its exemplary members called particle swarm optimization (PSO). We discuss the algorithm, its recent applications to find different types of efficient experimental designs, and provide resources, where codes for PSO and other metaheuristic algorithms and tutorials with examples are available. This article is categorized under: Algorithms and Computational Methods > Genetic Algorithms and Evolutionary Computing

In this paper, we propose Bayesian analysis methods dealing with longitudinal data involving repeated binary outcomes on subjects with dropouts. The proposed Bayesian methods implement probit models with random effects to capture heterogeneity and hypersphere decomposition to model the correlation matrix for serial correlation of repeated responses. We investigate the model robustness against misspecifications of the probit models along with techniques to handle missing data. The parameters of the proposed models are estimated by implementing an Markov chain Monte Carlo (MCMC) algorithm, and simulations were performed to provide a comparison with other models and validate the choice of prior distributions. The simulations show that when suitable correlation structures are specified, the proposed approach improves estimation of the regression parameters in terms of the mean percent relative error and the mean squared error. Finally, two real data examples are provided to illustrate the proposed approach.

AIM:To determine the risk patterns associated with transient hearing impairment (THI) and permanent hearing loss (PHL) of infants born very preterm who failed hearing screenings.METHOD:We enrolled 646 infants (347 males, 299 females) born at no more than 30 weeks' gestation between 2006 and 2020 who received auditory brainstem response screening at term-equivalent age. Audiological examinations of infants who failed the screening revealed THI, when hearing normalized, or PHL, defined as a persistent unilateral or bilateral hearing threshold above 20 dB. Principal component analysis (PCA) was used to characterize risk patterns.RESULTS:Among the 646 infants, 584 (90.4%) had normal hearing, 42 (6.5%) had THI, and 20 (3.1%) had PHL. Compared with the group with normal hearing, the THI and PHL groups had significantly higher rates of neurodevelopmental impairment at 24 months corrected age. PCA of risk patterns showed the THI group and especially the PHL group had more severe haemodynamic and respiratory instability. Moreover, severe intraventricular haemorrhage (IVH) was also a risk for PHL. Propensity score matching revealed an association of haemodynamic and respiratory instability with PHL.INTERPRETATION:In infants born preterm, the severity and duration of haemodynamic and respiratory instability are risk patterns for both THI and PHL; severe IVH is an additional risk for PHL.WHAT THIS PAPER ADDS:Neurodevelopmental delay was more common in infants born preterm who failed hearing screening. Principal component analysis revealed the risk patterns associated with hearing impairment. Haemodynamic-respiratory instability was associated with transient and permanent hearing impairment outcomes. Severe haemodynamic-respiratory instability and intraventricular haemorrhage was associated with permanent hearing loss.

We develop a variational Bayesian (VB) approach for estimating large-scale dynamic network models in the network autoregression framework. The VB approach allows for the automatic identification of the dynamic structure of such a model and obtains a direct approximation of the posterior density. Compared to Markov Chain Monte Carlo (MCMC) based sampling approaches, the VB approach achieves enhanced computational efficiency without sacrificing estimation accuracy. In the simulation study conducted here, the proposed VB approach detects various types of proper active structures for dynamic network models. Compared to the alternative approach, the proposed method achieves similar or better accuracy, and its computational time is halved. In a real data analysis scenario of day-ahead natural gas flow prediction in the German gas transmission network with 51 nodes between October 2013 and September 2015, the VB approach delivers promising forecasting accuracy along with clearly detected structures in terms of dynamic dependence.

Exchange-type algorithms have been commonly used to construct optimal designs. As these algorithms may converge to a local optimum, the typical procedure requires the use of several randomly chosen initial designs. Thus, the search for the optimal design can be conducted by performing several independent optimizations. We propose a general framework that combines exchange algorithms with particle swarm intelligence techniques. The main strategy is to represent each initial design as a particle and make the algorithm share information from various converging paths from those initial designs. This amounts to conducting one coordinated optimization instead of several independent optimizations. The proposed general algorithm is called the particle swarm exchange (PSE) algorithm. We compare the performance of PSE with those of two commonly used exchange algorithms - the columnwise-pairwise (CP) exchange algorithm of Li and Wu (1997) for designs with structural requirements and the coordinate exchange algorithm of Meyer and Nachtsheim (1995) for designs without such requirements. In the context of model-robust discriminating designs, we demonstrate that PSE typically performs as well as or, very often, better than the corresponding pure exchange algorithms.

Establishing the different feeding trajectories based on daily enteral feeding data in preterm infants at different gestational ages (GAs), may help to identify the risks and extrauterine growth restriction (EUGR) outcomes associated with the adverse feeding pattern. In a single center, we retrospectively included 625 infants born at 23–30 weeks of gestation who survived to term-equivalent age (TEA) from 2009 to 2020. The infants were designated into three GA groups: 23–26, 27–28, and 29–30 weeks. The daily enteral feeding amounts in the first 56 postnatal days were analyzed to determine the feeding trajectories. The primary outcomes were EUGR in body weight and head circumference calculated, respectively, by the changes between birth and TEA. Clustering analysis identified two feeding trajectories, namely the improving and adverse patterns in each GA group. The adverse feeding pattern that occurred in 49%, 20%, and 17% of GA 23–26, 27–28, and 29–30 weeks, respectively, was differentiated from the improving feeding pattern as early as day 7 in infants at GA 23–26 and 27–28 weeks, in contrast to day 21 in infants at GA 29–30 weeks. The adverse feeding patterns were associated with sepsis, respiratory, and gastrointestinal morbidities at GA 23–26 weeks; sepsis, hemodynamic and gastrointestinal morbidities at GA 27–28 weeks; and preeclampsia, respiratory, and gastrointestinal morbidities at GA 29–30 weeks. Using the improving feeding group as a reference, the adverse feeding group showed significantly higher adjusted odds ratios of EUGR in body weight and head circumference in infants at GA 23–26 and 27–28 weeks. Identifying the early-life adverse feeding trajectories may help recognize the related EUGR outcomes of preterm infants in a GA-related manner.

A new stochastic search algorithm is proposed for solving information-criterion-based variable selection problems. The idea behind the proposed algorithm is to search for the best model for the previously specified information criterion using multiple search particles. These particles simultaneously explore the candidate model space and communicate with each other to share search information. A new stochastic stepwise procedure is proposed to update the model during the search for the best model by adding or deleting variables. The proposed algorithm can also be used to generate variable selection ensembles efficiently. Several examples are used to demonstrate the performances of the proposed algorithm. A parallel version of the proposed algorithm is also introduced to accelerate the performance in terms of computation time.

In this article, we present a Bayesian framework for multivariate longitudinal data analysis with a focus on selection of important elements in the generalized autoregressive matrix. An efficient Gibbs sampling algorithm was developed for the proposed model and its implementation in a comprehensive R package called MLModelSelection is available on the comprehensive R archive network. The performance of the proposed approach was studied via a comprehensive simulation study. The effectiveness of the methodology was illustrated using a nonalcoholic fatty liver disease dataset to study correlations in multiple responses over time to explain the joint variability of lung functions and body mass index. Supplementary materials for this article, including a standardized description of the materials needed to reproduce the work, are available as an online supplement.

Grouping selection arises naturally in many statistical modeling problems. Several group selection methods have been proposed in the last two decades. In this paper, we review the Bayesian group selection approaches for linear regression models. We start from the Bayesian indicator approach and then move to the Bayesian group LASSO methods. In addition, we also consider the Bayesian methods for the sparse group selection that can be treated as an extension of the group selection. Finally, we mention some extensions of Bayesian group selection for the generalized linear models and the multiple response models. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Dimension Reduction Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical Models > Model Selection