arXiv subjects

Weng Kee Wong

Publications and source records attributed to Weng Kee Wong.

At least 19 recordsLinked to original sources

arXiv2026

Single and multi-objective optimal designs for group testing experiments with a focus on screening for an infectious disease

Group testing techniques are widely used in resource-constrained settings, such as infectious-disease screening, blood safety, DNA library screening, and industrial inspection, where the efficient use of limited testing resources depends critically on how the initial study is designed. This paper discusses various ways that group testing experiments can be designed more efficiently and flexibly, under a user-specified optimality criterion and cost structure. We construct optimal designs to estimate model parameters beyond the $D$-optimality criterion to include the $A$-, $c$-, $E$-optimality, and extend the framework for finding optimal designs with multiple objectives. For large studies, we use a general theory and obtain various types of optimal approximate designs. When sample sizes are small, we propose two algorithms to construct highly efficient exact designs under realistic budget constraints. Additionally, we investigate properties of the proposed designs under various operational uncertainties and create a Shiny app to facilitate implementation of the proposed designs. To fix ideas, we focus on finding highly efficient group testing designs for a Chlamydia screening trial with imperfect assays under budget constraints and show the advantages of our optimal designs over current methods.

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arXiv2026

Gradient flow for finding E-optimal designs

The $E$-optimality criterion for a regression model maximizes the smallest eigenvalue of the information matrix and becomes non-differentiable when this eigenvalue has multiplicity greater than one. Working in the $2$-Wasserstein space, we show that the Wasserstein gradient at an empirical measure coincides, up to a constant factor, with the Euclidean particle gradient for smooth criteria such as $D$- and $L$-optimality, and that the approximation gap for equal-weight $N$-particle designs vanishes at an explicit rate. The main challenge is the nonsmooth $E$-criterion, for which the Wasserstein gradient does not exist. We replace it with a constrained Wasserstein steepest-ascent field obtained by maximizing feasible directional derivatives over the tangent cone of the design space, and prove that the resulting flow satisfies an exact energy identity and that every limit point is first-order stationary. The particle ascent computation reduces to a convex semidefinite programme whose dimension equals the multiplicity of the smallest eigenvalue. In numerical comparisons on second-order response surface models and a seven-dimensional logistic regression model, the constrained Wasserstein steepest-ascent method attains near-optimal $E$-criterion values and is markedly more reliable than particle swarm optimization in higher-dimensional settings. The framework applies more broadly to other nonsmooth minimax criteria in optimal design, and a numerical experiment on the minimax-single-parameter criterion confirms that the method attains the theoretical optimum.

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arXiv2025

LORDs: Locally Optimal Restricted Designs for Phase I/II Dose-Finding Studies

We propose Locally Optimal Restricted Designs (LORDs) for phase I/II dose-finding studies that focus on both efficacy and toxicity outcomes. As an illustrative application, we find various LORDs for a 4-parameter continuation-ratio (CR) model defined on a user-specified dose range, where ethical constraints are imposed to prevent patients from receiving excessively toxic or ineffective doses. We study the structure and efficiency of LORDs across several experimental scenarios and assess the sensitivity of the results to changes in the design problem, such as adjusting the dose range or redefining target doses. Additionally, we compare LORDs with a more heuristic phase I/II design and show that LORDs offer more statistically efficient and ethical benchmark designs. A key innovation in our work is the use of a nature-inspired metaheuristic algorithm to determine dose-finding designs. This algorithm is free from assumptions, fast, and highly flexible. As a result, more realistic and adaptable designs for any model and design criterion with multiple practical constraints can be readily found and implemented. Our work also is the first to suggest how to modify and informatively select the next set of doses for the next study for enhanced statistical inference.

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arXiv2025

Metaheuristics is All You Need

Optimization plays an important role in tackling public health problems. Animal instincts can be used effectively to solve complex public health management issues by providing optimal or approximately optimal solutions to complicated optimization problems common in public health. BAT algorithm is an exemplary member of a class of nature-inspired metaheuristic optimization algorithms and designed to outperform existing metaheuristic algorithms in terms of efficiency and accuracy. It's inspiration comes from the foraging behavior of group of microbats that use echolocation to find their target in the surrounding environment. In recent years, BAT algorithm has been extensively used by researchers in the area of optimization, and various variants of BAT algorithm have been developed to improve its performance and extend its application to diverse disciplines. This paper first reviews the basic BAT algorithm and its variants, including their applications in various fields. As a specific application, we apply the BAT algorithm to a biostatistical estimation problem and show it has some clear advantages over existing algorithms.

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arXiv2025

Failure of Optimal Design Theory? A Case Study in Toxicology Using Sequential Robust Optimal Design Framework

This paper presents a quasi-sequential optimal design framework for toxicology experiments, specifically applied to sea urchin embryos. The authors propose a novel approach combining robust optimal design with adaptive, stage-based testing to improve efficiency in toxicological studies, particularly where traditional uniform designs fall short. The methodology uses statistical models to refine dose levels across experimental phases, aiming for increased precision while reducing costs and complexity. Key components include selecting an initial design, iterative dose optimization based on preliminary results, and assessing various model fits to ensure robust, data-driven adjustments. Through case studies, we demonstrate improved statistical efficiency and adaptability in toxicology, with potential applications in other experimental domains.

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arXiv2024

Applications of Nature-Inspired Metaheuristic Algorithms for Tackling Optimization Problems Across Disciplines

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.

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arXiv2024

Particle swarm optimization with Applications to Maximum Likelihood Estimation and Penalized Negative Binomial Regression

General purpose optimization routines such as nlminb, optim (R) or nlmixed (SAS) are frequently used to estimate model parameters in nonstandard distributions. This paper presents Particle Swarm Optimization (PSO), as an alternative to many of the current algorithms used in statistics. We find that PSO can not only reproduce the same results as the above routines, it can also produce results that are more optimal or when others cannot converge. In the latter case, it can also identify the source of the problem or problems. We highlight advantages of using PSO using four examples, where: (1) some parameters in a generalized distribution are unidentified using PSO when it is not apparent or computationally manifested using routines in R or SAS; (2) PSO can produce estimation results for the log-binomial regressions when current routines may not; (3) PSO provides flexibility in the link function for binomial regression with LASSO penalty, which is unsupported by standard packages like GLM and GENMOD in Stata and SAS, respectively, and (4) PSO provides superior MLE estimates for an EE-IW distribution compared with those from the traditional statistical methods that rely on moments.

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arXiv2024

Mathematical programming tools for randomization purposes in small two-arm clinical trials: A case study with real data

Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate-adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.

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arXiv2023

Trajectory-aware Principal Manifold Framework for Data Augmentation and Image Generation

Data augmentation for deep learning benefits model training, image transformation, medical imaging analysis and many other fields. Many existing methods generate new samples from a parametric distribution, like the Gaussian, with little attention to generate samples along the data manifold in either the input or feature space. In this paper, we verify that there are theoretical and practical advantages of using the principal manifold hidden in the feature space than the Gaussian distribution. We then propose a novel trajectory-aware principal manifold framework to restore the manifold backbone and generate samples along a specific trajectory. On top of the autoencoder architecture, we further introduce an intrinsic dimension regularization term to make the manifold more compact and enable few-shot image generation. Experimental results show that the novel framework is able to extract more compact manifold representation, improve classification accuracy and generate smooth transformation among few samples.

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arXiv2022

Dual Path Structural Contrastive Embeddings for Learning Novel Objects

Learning novel classes from a very few labeled samples has attracted increasing attention in machine learning areas. Recent research on either meta-learning based or transfer-learning based paradigm demonstrates that gaining information on a good feature space can be an effective solution to achieve favorable performance on few-shot tasks. In this paper, we propose a simple but effective paradigm that decouples the tasks of learning feature representations and classifiers and only learns the feature embedding architecture from base classes via the typical transfer-learning training strategy. To maintain both the generalization ability across base and novel classes and discrimination ability within each class, we propose a dual path feature learning scheme that effectively combines structural similarity with contrastive feature construction. In this way, both inner-class alignment and inter-class uniformity can be well balanced, and result in improved performance. Experiments on three popular benchmarks show that when incorporated with a simple prototype based classifier, our method can still achieve promising results for both standard and generalized few-shot problems in either an inductive or transductive inference setting.

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arXiv2021

An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic

In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems with a focus on the current pandemic. In particular, we give applications of modern digital technology, statistical methods, data platforms and data integration systems to improve diagnosis and treatment of diseases in clinical research and novel epidemiologic tools to tackle infection source problems, such as finding Patient Zero in the spread of epidemics. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort to continuously create more effective methodologies and powerful tools to transfer data information into knowledge that enables informed decision making.

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arXiv2021

Orthogonal Subsampling for Big Data Linear Regression

The dramatic growth of big datasets presents a new challenge to data storage and analysis. Data reduction, or subsampling, that extracts useful information from datasets is a crucial step in big data analysis. We propose an orthogonal subsampling (OSS) approach for big data with a focus on linear regression models. The approach is inspired by the fact that an orthogonal array of two levels provides the best experimental design for linear regression models in the sense that it minimizes the average variance of the estimated parameters and provides the best predictions. The merits of OSS are three-fold: (i) it is easy to implement and fast; (ii) it is suitable for distributed parallel computing and ensures the subsamples selected in different batches have no common data points; and (iii) it outperforms existing methods in minimizing the mean squared errors of the estimated parameters and maximizing the efficiencies of the selected subsamples. Theoretical results and extensive numerical results show that the OSS approach is superior to existing subsampling approaches. It is also more robust to the presence of interactions among covariates and, when they do exist, OSS provides more precise estimates of the interaction effects than existing methods. The advantages of OSS are also illustrated through analysis of real data.

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arXiv2021

Particle swarm optimization in constrained maximum likelihood estimation a case study

The aim of paper is to apply two types of particle swarm optimization, global best andlocal best PSO to a constrained maximum likelihood estimation problem in pseudotime anal-ysis, a sub-field in bioinformatics. The results have shown that particle swarm optimizationis extremely useful and efficient when the optimization problem is non-differentiable and non-convex so that analytical solution can not be derived and gradient-based methods can not beapplied.

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arXiv2021

Exploration Enhancement of Nature-Inspired Swarm-based Optimization Algorithms

Nature-inspired swarm-based algorithms have been widely applied to tackle high-dimensional and complex optimization problems across many disciplines. They are general purpose optimization algorithms, easy to use and implement, flexible and assumption-free. A common drawback of these algorithms is premature convergence and the solution found is not a global optimum. We provide sufficient conditions for an algorithm to converge almost surely (a.s.) to a global optimum. We then propose a general, simple and effective strategy, called Perturbation-Projection (PP), to enhance an algorithm's exploration capability so that our convergence conditions are guaranteed to hold. We illustrate this approach using three widely used nature-inspired swarm-based optimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT) and competitive swarm optimizer (CSO). Extensive numerical experiments show that each of the three algorithms with the enhanced PP strategy outperforms the original version in a number of notable ways.

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arXiv2021

Statistical Analysis of Complex Computer Models in Astronomy

We introduce statistical techniques required to handle complex computer models with potential applications to astronomy. Computer experiments play a critical role in almost all fields of scientific research and engineering. These computer experiments, or simulators, are often computationally expensive, leading to the use of emulators for rapidly approximating the outcome of the experiment. Gaussian process models, also known as Kriging, are the most common choice of emulator. While emulators offer significant improvements in computation over computer simulators, they require a selection of inputs along with the corresponding outputs of the computer experiment to function well. Thus, it is important to select inputs judiciously for the full computer simulation to construct an accurate emulator. Space-filling designs are efficient when the general response surface of the outcome is unknown, and thus they are a popular choice when selecting simulator inputs for building an emulator. In this tutorial we discuss how to construct these space filling designs, perform the subsequent fitting of the Gaussian process surrogates, and briefly indicate their potential applications to astronomy research.

astro-ph.IM↗

arXiv2020

T-optimal designs for multi-factor polynomial regression models via a semidefinite relaxation method

We consider T-optimal experiment design problems for discriminating multi-factor polynomial regression models where the design space is defined by polynomial inequalities and the regression parameters are constrained to given convex sets. Our proposed optimality criterion is formulated as a convex optimization problem with a moment cone constraint. When the regression models have one factor, an exact semidefinite representation of the moment cone constraint can be applied to obtain an equivalent semidefinite program. When there are two or more factors in the models, we apply a moment relaxation technique and approximate the moment cone constraint by a hierarchy of semidefinite-representable outer approximations. When the relaxation hierarchy converges, an optimal discrimination design can be recovered from the optimal moment matrix, and its optimality is confirmed by an equivalence theorem. The methodology is illustrated with several examples.

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arXiv2017

Optimal group testing designs for estimating prevalence with uncertain testing errors

We construct optimal designs for group testing experiments where the goal is to estimate the prevalence of a trait by using a test with uncertain sensitivity and specificity. Using optimal design theory for approximate designs, we show that the most efficient design for simultaneously estimating the prevalence, sensitivity and specificity requires three different group sizes with equal frequencies. However, if estimating prevalence as accurately as possible is the only focus, the optimal strategy is to have three group sizes with unequal frequencies. On the basis of a chlamydia study in the U.S.A., we compare performances of competing designs and provide insights into how the unknown sensitivity and specificity of the test affect the performance of the prevalence estimator. We demonstrate that the locally D- and Ds-optimal designs proposed have high efficiencies even when the prespecified values of the parameters are moderately misspecified.

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arXiv2016

Optimal discrimination designs for semi-parametric models

Much of the work in the literature on optimal discrimination designs assumes that the models of interest are fully specified, apart from unknown parameters in some models. Recent work allows errors in the models to be non-normally distributed but still requires the specification of the mean structures. This research is motivated by the interesting work of Otsu (2008) to discriminate among semi-parametric models by generalizing the KL-optimality criterion proposed by López-Fidalgo et al. (2007) and Tommasi and López-Fidalgo (2010). In our work we provide further important insights in this interesting optimality criterion. In particular, we propose a practical strategy for finding optimal discrimination designs among semi-parametric models that can also be verified using an equivalence theorem. In addition, we study properties of such optimal designs and identify important cases where the proposed semi-parametric optimal discrimination designs coincide with the celebrated T -optimal designs.

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