Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood

Hantush’s model is widely used for predicting rise in water table in response to groundwater recharge. Several approximate methods of the Hantush mound function, S(α, β) have been developed to overcome the limitations of Hantush’s tabulated values of the S(α, β) function. These approximate methods have their own advantages and disadvantages, and it is difficult to identify the most accurate and computationally efficient S(α, β) estimation method. In this study, performance of four different algebraic approximate S(α, β) estimation methods are compared with the Hantush method using the published data. The four different methods considered are Swamee and Ohja (1997) (SO), Singh (2012) (SI), Vatankhah (2013) (VA) and Gauss-Legendre quadrature (GL) method with various Gaussian points (GP). Seven statistical accuracy and computation efficiency indicators are used to assess the performance of different S(α, β) estimation methods. The GL method with 100 to16 GPs is found to be the most accurate S(α, β) estimation method. This is followed by the SO, GL with 14 to 12 GPs, VA, GL with 10 GP, SI, and GL with 9 to 3 GPs. A good trade off between accuracy and efficiency is found with the SO, VA, and GL method with 14, 12 and 10 GPs. Comprehensive analysis of different S(α, β) estimation methods, and their ranking based on overall performance index will be helpful in modelling water table rise due to groundwater recharge, optimum design of recharge basin, and evaluation of effectiveness of recharge basins in groundwater recharging.

Data are distributed across different sites due to computing facility limitations or data privacy considerations. Conventional centralized methods—those in which all datasets are stored and processed in a central computing facility—are not applicable in practice. Therefore, it has become necessary to develop distributed learning approaches that have good inference or predictive accuracy while remaining free of individual data or obeying policies and regulations to protect privacy. In this article, we introduce the basic idea of distributed learning and conduct a selected review on various distributed learning methods, which are categorized by their statistical accuracy, computational efficiency, heterogeneity, and privacy. This categorization can help evaluate newly proposed methods from different aspects. Moreover, we provide up-to-date descriptions of the existing theoretical results that cover statistical equivalency and computational efficiency under different statistical learning frameworks. Finally, we provide existing software implementations and benchmark datasets, and we discuss future research opportunities.

A comparison of alternative methods for the estimation of dynamic factor demand models under non-static expectations

We present a novel method for fast and dense 3D scene flow estimation which optimizes consistency and smoothness in both intensity and depth data while considering computing efficiency for the real-world applications. 3D scene flow estimation is an attractive problem with the advent of commodity RGB-D cameras. Naive extensions of recent variational optical flow techniques show promising but limited successes. Due to their primitive priors, solutions from total variation approaches prefer unrealistic constant motion. To overcome these problems and consider the computational efficiency, we adopt an image-guided total generalized variation (ITGV) regularization. As demonstrated with experimental results, the proposed method outperforms both in terms of accuracy and speed compared to the existing variational approaches.

For a steam heating network (SHN), which is one of the most important parts of the integrated energy systems in an industrial park, it is essential to provide accurate and reliable estimation of hydraulic and thermal states, to ensure its operational safety and economy. Considering two different time-scale characteristics of the hydraulic and thermal processes in the SHN, this article proposes a mechanism and data-driven dual estimation method for the coupling hydraulic-thermal dynamic states. Due to the fact that the existing coupled estimation methods with one time scale may suffer from heavy computational burden, a data-driven dual sequential acrlong DSE method of the SHN is proposed based on the two-time scales, in which the dynamics of slow thermal states can also be captured when performing the acrlong SE of the fast hydraulic process. Furthermore, to improve the computational and communicational efficiency, a distributed interaction strategy based on the nodal transformation matrix is designed for large-scale steam systems. To verify the effectiveness of the proposed method, a single pipeline system and two real-world industrial superheated steam networks are employed. Compared to other state-of-the-art methods, the proposed method achieves the best tradeoff between the estimation accuracy and computational efficiency.

This paper demonstrates that well-known parameter estimation methods for Gaussian fields place different emphasis on the high and low frequency components of the data. As a consequence, the relative importance of the frequencies under the objective of the analysis should be taken into account when selecting an estimation method, in addition to other considerations such as statistical and computational efficiency. The paper also shows that when noise is added to the Gaussian field, maximum pseudolikelihood automatically sets the smoothing parameter of the model equal to one. A simulation study then indicates that generalised cross-validation is more robust than maximum likelihood un- der model misspecification in smoothing and image restoration problems. This has implications for Bayesian procedures since these use the same weightings of the frequencies as the likelihood.

A composite likelihood approach to (co)variance components estimation

Literature has reported differences in the epidemiology or natural history of non-communicable diseases among both the male and female sexes. Stratification of multimorbidity burden based on sex is crucial to identify and implement targeted prevention and control interventions for chronic diseases. To determine the burden of hypertension, type-2 diabetes mellitus, and obesity; and to compare the related multimorbidity among male and female patients. The study was a retrospective analysis of 375 802 medical records from primary care centers. Data was extracted from March 2022 to March 2023. A multivariate probit estimation methodology was employed using a 3-equations multivariate multiple probit model to jointly estimate the association of a person's sex with the diagnosis of the 3 chronic conditions: obesity, diabetes, and hypertension. A multinomial logistic regression analysis was conducted to allow each unique combination of these 3 chronic diseases. Females had a relatively higher proportion of obesity (58.1% vs 41.2%), obesity and diabetes only (58.9% vs 41.1%), obesity and hypertension (63.6% vs 36.4%), and joint diagnosis with 3 conditions (65.7% vs 34.3%). Females' participants consistently had a significantly higher likelihood of diagnosis compared with males except for diabetes (OR = 0.59, 95% CI: 0.56-0.62) and the combination of only diabetes and hypertension (OR = 0.67, 95% CI: 0.61-0.74). The likelihood of other combinations ranged from 1.04 (95% CI: 0.98-1.10) for only hypertension to 2.30 (95% CI: 2.10-2.53) for the joint diagnosis of all 3 conditions. An increased likelihood of a single or combined occurrence of 3 chronic conditions was observed with increased age. The multimorbidity distribution for diabetes mellitus, hypertension, and obesity differs significantly among male and female patients. The overall burden of morbidity, and mortality, however, tends to rise after 46 years of age, with the highest burden among individuals above 60 years of age.

The exponential distribution remains a cornerstone of probability theory, particularly valued in reliability engineering, survival analysis, and risk management for its computational efficiency and distinctive memoryless property. While mathematically elegant, this distribution's constrained single-parameter framework presents significant limitations when modeling complex empirical phenomena, especially those displaying non-monotonic failure patterns. Academic literature documents numerous extensions to address these constraints, including the inverse exponential distribution and its exponentiated variants, which achieve greater modeling flexibility by introducing additional shape parameters. Building upon these developments, the current study presents an enhanced probability model through the innovative synthesis of the Inverse Exponential distribution with the Sine Topp-Leone Exponentiated-G (STLEG) family, yielding the novel Sine Topp-Leone Exponentiated Inverse Exponential (STLEIE) Distribution. Our investigation encompasses a rigorous analysis of the model's fundamental statistical properties, including its survival characteristics, hazard function behavior, moment structure, quantile properties, and order statistics. For parameter estimation, we implement and compare two established methodologies: the widely-used Maximum Likelihood Estimation (MLE) approach and the Maximum Product of Spacing (MPS) technique, both renowned for their statistical efficiency. Comprehensive Monte Carlo simulations demonstrate the consistent performance of these estimators, with both bias and Root Mean Squared Error (RMSE) metrics showing progressive improvement as sample sizes increase. Empirical validation through application to two real-world datasets reveals the STLEIE distribution's superior modeling capabilities compared to existing alternatives, as evidenced by multiple goodness-of-fit criteria. The concluding comparative analysis substantiates the practical utility of the proposed model, confirming its enhanced flexibility and improved performance across diverse data scenarios, thereby offering researchers and practitioners a more robust analytical tool for complex statistical modeling applications.

This article develops an incremental learning algorithm based on quadratic inference function (QIF) to analyze streaming datasets with correlated outcomes such as longitudinal data and clustered data. We propose a renewable QIF (RenewQIF) method within a paradigm of renewable estimation and incremental inference, in which parameter estimates are recursively renewed with current data and summary statistics of historical data, but with no use of any historical subject-level raw data. We compare our renewable estimation method with both offline QIF and offline generalized estimating equations (GEE) approach that process the entire cumulative subject-level data all together, and show theoretically and numerically that our renewable procedure enjoys statistical and computational efficiency. We also propose an approach to diagnose the homogeneity assumption of regression coefficients via a sequential goodness-of-fit test as a screening procedure on occurrences of abnormal data batches. We implement the proposed methodology by expanding existing Spark’s Lambda architecture for the operation of statistical inference and data quality diagnosis. We illustrate the proposed methodology by extensive simulation studies and an analysis of streaming car crash datasets from the National Automotive Sampling System-Crashworthiness Data System (NASS CDS). Supplementary materials for this article are available online.

High-dimensional time series data appear in many scientific areas in the current data-rich environment. Analysis of such data poses new challenges to data analysts because of not only the complicated dynamic dependence between the series, but also the existence of aberrant observations, such as missing values, contaminated observations, and heavy-tailed distributions. For high-dimensional vector autoregressive (VAR) models, we introduce a unified estimation procedure that is robust to model misspecification, heavy-tailed noise contamination, and conditional heteroscedasticity. The proposed methodology enjoys both statistical optimality and computational efficiency, and can handle many popular high-dimensional models, such as sparse, reduced-rank, banded, and network-structured VAR models. With proper regularization and data truncation, the estimation convergence rates are shown to be almost optimal in the minimax sense under a bounded (2+2ϵ)th moment condition. When ϵ≥1, the rates of convergence match those obtained under the sub-Gaussian assumption. Consistency of the proposed estimators is also established for some ϵ∈(0,1), with minimax optimal convergence rates associated with ϵ. The efficacy of the proposed estimation methods is demonstrated by simulation and a U.S. macroeconomic example.

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Precision medicine is an increasingly important area of research. Due to the heterogeneity of individual characteristics, patients may respond differently to treatments. One of the most important goals for precision medicine is to develop individualized treatment rules (ITRs) involving patients’ characteristics directly. As an interesting topic in clinical research, many statistical methods have been developed in recent years to find optimal ITRs. For binary treatments, outcome weighted learning (OWL) was proposed to find a decision function of patient characteristics maximizing the expected clinical outcome. Treatments with hierarchical structure are commonly seen in practice. In hierarchical scenarios, how to estimate ITRs is still unclear. We propose a new framework named hierarchical outcome-weighted angle-based learning (HOAL) to estimate ITRs for treatments with hierarchical structure. Statistical properties including Fisher consistency and convergence rates of the proposed method are presented. Simulations and an application to a type 2 diabetes study under linear and nonlinear learning show the highly competitive performance of our proposed procedure in both numerical accuracy and computational efficiency.

Induced pluripotent stem cells (iPSCs) offer an unprece-dented opportunity to study human physiology and disease at the cellular level. They also have the potential to be leveraged in the practice of precision medicine, for example, personalized drug testing. This statement comprehensively describes the provenance of iPSC lines, their use for cardiovascular disease modeling, their use for precision medicine, and strategies through which to promote their wider use for biomedical applications. Human iPSCs exhibit properties that render them uniquely qualified as model systems for studying human diseases: they are of human origin, which means they carry human genomes; they are pluripotent, which means that in principle, they can be differentiated into any of the human body's somatic cell types; and they are stem cells, which means they can be expanded from a single cell into millions or even billions of cell progeny. iPSCs offer the opportunity to study cells that are genetically matched to individual patients, and genome-editing tools allow introduction or correction of genetic variants. Initial progress has been made in using iPSCs to better understand cardiomyopathies, rhythm disorders, valvular and vascular disorders, and metabolic risk factors for ischemic heart disease. This promising work is still in its infancy. Similarly, iPSCs are only just starting to be used to identify the optimal medications to be used in patients from whom the cells were derived. This statement is intended to (1) summarize the state of the science with respect to the use of iPSCs for modeling of cardiovascular traits and disorders and for therapeutic screening; (2) identify opportunities and challenges in the use of iPSCs for disease modeling and precision medicine; and (3) outline strategies that will facilitate the use of iPSCs for biomedical applications. This statement is not intended to address the use of stem cells as regenerative therapy, such as transplantation into the body to treat ischemic heart disease or heart failure.

This note provides a comprehensive overview of tools for predicting observables in the Standard Model effective field theory (SMEFT) at both tree level and one loop using event generators. We evaluate three primary methodologies–event reweighting, separate simulation of squared matrix elements, and full SMEFT process simulation–focusing on their statistical performance, computational efficiency, and potential biases. Each approach is assessed in terms of its accuracy, highlighting trade-offs between precision and resource demands. Practical insights into their applicability for high-energy physics analyses are offered, with particular attention to processes where SMEFT effects are significant. Additionally, we discuss the role of helicity in reweighting strategies and its impact on the quality of predictions. By comparing the methods across various LHC processes, this note provides guidance for selecting the most effective strategy for various SMEFT studies, ensuring robust predictions while optimizing computational resources.