Efficient routing and scheduling plans for a modern workforce are challenging to develop for many firms offering services to customers at their home. In this paper, we focus on those firms that provide technical or maintenance related assistance using heterogeneously skilled technicians working in the sharing economy. We present a model that minimizes the costs of routing and scheduling these technicians

When a branded firm offers a new product at a quality level different from that of its existing product(s), some bias is often present as consumers are affected by the quality of the existing product(s) when evaluating the quality of the new one(s). Consequently, this product offering creates a forward spillover effect and, in turn, might even impact consumers’ utility from the existing product, referred

The eigenvalue method, suggested by the developer of the extensively used Analytic Hierarchy Process methodology, exhibits right-left asymmetry: the priorities derived from the right eigenvector do not necessarily coincide with the priorities derived from the reciprocal left eigenvector. This paper offers a comprehensive numerical experiment to compare the two eigenvector-based weighting procedures

In many service systems such as movie theaters, sports clubs, or hotels, different customer segments share physical space, and the presence of customers from different segments may affect the utility a customer receives from the service. We consider a setting where two competing facilities offer service to two distinct types of customers and study whether and when the firms can benefit from using differential

Learning and forgetting (LaF) phenomena are characteristic of labor-intensive production and service industries. To mitigate the effects of LaF in a human-centric manufacturing system integrated with outsourcing, managers need to coordinate their decisions with partners for assigning operations and scheduling processes following a hierarchy. A model that addresses this should consider the expected

Optimal transport and Wasserstein distances are flourishing in many scientific fields as a means for comparing and connecting random structures. Here we pioneer the use of an optimal transport dist...

Modeling binary and categorical data is one of the most commonly encountered tasks of applied statisticians and econometricians. While Bayesian methods in this context have been available for decad...

Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to ...

In this paper, we study a method for finding robust solutions to multiobjective optimization problems under uncertainty. We follow the set-based minmax approach for handling the uncertainties which leads to a certain set optimization problem with the strict upper type set relation. We introduce, under some assumptions, a reformulation using instead the strict lower type set relation without sacrificing

Both workload peaks and lows contribute to lower employee well-being. Predictive employee workload analytics can empower management to undertake proactive prevention. For this purpose, we develop a real-time machine learning framework to predict and explain future workload in a challenging environment with variable and imbalanced workload: the digital control rooms for railway traffic management of

In the face of a significant exogenous shock, government intervention may be required at the level of an industry in order to preserve the market. By employing a game engineering approach, we develop a model to test the potential impact of varying types of bailout schemes on network oriented industries facing such a shock. Investigating the European aviation market, served by both legacy and low-cost

ABSTRACT In practice, the ultimate outcome of many important discrimination cases, e.g. the Wal-Mart, Nike and Goldman-Sachs equal pay cases, is determined at the stage when the plaintiffs request that the case be certified as a class action. The primary statistical issue at this time is whether the employment practice in question leads to a common pattern of outcomes disadvantaging most plaintiffs

This study examines the impact of loss aversion on the retailer’s financing mechanism preference under the consignment contract in a supply chain consisting of a retailer and a capital-constrained supplier. Both the retailer and the supplier could be loss averse. To help the supplier overcome financial distress, two financing mechanisms are available for the retailer as follows. (1) direct financing

In the aftermath of natural disasters there is a need to provide disaster relief services. These services are offered by diverse disaster relief personnel teams that are specialized in the provision of the required services, e.g., teams that set up temporary shelters, teams that are providing medical services. These services are provided during a rolling horizon and the demand and supply characteristics

Estimating treatment effects is one of the most challenging and important tasks of data analysts. Personalized medicine, digital marketing, and many other applications demand an efficient allocation of scarce treatments to those individuals who benefit the most. Uplift models support this allocation by estimating how individuals react to a treatment. A major challenge in uplift modeling concerns evaluation

Abstract Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural spike trains to document streams. The clustering property is achieved via a doubly stochastic formulation: first, a set of latent events is drawn from a Poisson process; then, each latent event generates a set of

Abstract In this paper, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vastly different sample sizes which frequently arises in data integration, genomics, neuroscience, and sensor networks. Existing works characterize the graph

Process outcome prediction pertains to the classification of ongoing cases of (business) processes into a given set of categorical outcomes. This field of research has seen a strong uptake in recent years due to advances in machine and deep learning. Although a recent shift has been made in the field of process outcome prediction to use models from the explainable artificial intelligence field, the

We study a firm producing and selling a technology-related product with asymmetric quality information to a destination market in two periods. A characteristic of the technology-related product is that its prevailing price may reduce throughout the product’s life cycle and therefore trigger the firm’s price guarantee window. We develop a two-period dynamic signaling game model in which the firm with

Abstract This paper addresses the deconvolution problem of estimating a square-integrable probability density from observations contaminated with additive measurement errors having a known density. The estimator begins with a density estimate of the contaminated observations and minimizes a reconstruction error penalized by an integrated squared m-th derivative. Theory for deconvolution has mainly

Abstract Children’s health studies support an association between maternal environmental exposures and children’s birth outcomes. A common goal is to identify critical windows of susceptibility–periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different

Abstract Causal mediation analysis is a popular approach for investigating whether the effect of an exposure on an outcome is through a mediator to better understand the underlying causal mechanism. In recent literature, mediation analysis with multiple mediators has been proposed for continuous and dichotomous outcomes. In contrast, methods for mediation analysis for an ordinal outcome are still underdeveloped

Uncertainty is a key feature of any machine learning model and is particularly important in neural networks, which tend to be overconfident. This overconfidence is worrying under distribution shifts, where the model performance silently degrades as the data distribution diverges from the training data distribution. Uncertainty estimation offers a solution to overconfident models, communicating when

Current government subsidies on prevention fail to effectively incentive primary care providers and patients with chronic diseases, resulting in ineffective prevention. It hinders the shift from “the treatment-based” to “the combination of prevention and treatment” philosophy for chronic diseases. To encourage primary care provider and patients with chronic diseases to make better prevention efforts

This paper focuses on the tactical planning problem faced by a shipper which seeks to secure transportation and warehousing capacity, such as containers, vehicles or space in a warehouse, of different sizes, costs, and characteristics, from a carrier or logistics provider, while facing different sources of uncertainty. The uncertainty can be related to the loads to be transported or stored, the cost

Abstract We consider fixed boundary flows with canonical interpretability as principal components extended on non-linear Riemannian manifolds. We aim to find a flow with fixed starting and ending points for noisy multivariate data sets lying near an embedded non-linear Riemannian manifold. In geometric terms, the fixed boundary flow is defined as an optimal curve that moves in the data cloud with two

Abstract Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly infinite sets, which can be impractical. This paper addresses this computational challenge through the development of a novel generalised Bayesian inference procedure suitable for discrete intractable likelihood

Abstract Pairwise likelihood methods are commonly used for inference in parametric statistical models in cases where the full likelihood is too complex to be used, such as multivariate count data. Although pairwise likelihood methods represent a useful solution to perform inference for intractable likelihoods, several computational challenges remain. The pairwise likelihood function still requires

Abstract This study considers an estimator for the asymptotic variance-covariance matrix in time-series quantile regression models which is robust to the presence of heteroskedasticity and autocorrelation. When regression errors are serially correlated, the conventional quantile regression standard errors are invalid. The proposed solution is a quantile analogue of the Newey-West robust standard errors

Abstract Shapley value is originally a concept in econometrics to fairly distribute both gains and costs to players in a coalition game. In the recent decades, its application has been extended to other areas such as marketing, engineering and machine learning. For example, it produces reasonable solutions for problems in sensitivity analysis, local model explanation towards the interpretable machine

Published in Journal of the American Statistical Association (Just accepted, 2023)

Abstract Investigating the differential effect of treatments in groups defined by patient characteristics is of paramount importance in personalized medicine research. In some studies, participants are first classified as having or not of the characteristic of interest by diagnostic tools, but such classifiers may not be perfectly accurate. The impact of diagnostic misclassification in statistical

Abstract Many real-life applications involve estimation of curves that exhibit complicated shapes including jumps or varying-frequency oscillations. Practical methods have been devised that can adapt to a locally varying complexity of an unknown function (e.g. variable-knot splines, sparse wavelet reconstructions, kernel methods or trees/forests). However, the overwhelming majority of existing asymptotic

The problem of an expanding chain in a given area is considered. It may locate a new facility, vary the quality of its existing facilities, close some of them, or a combination of all these possibilities, whatever is the best to maximize its profit, given a budget for the expansion. A new competitive location and design model is proposed that allows all these possibilities. The resulting model is a

Abstract not available

The landscape of survival analysis is constantly being revolutionized to answer biomedical challenges, most recently the statistical challenge of censored covariates rather than outcomes. There are many promising strategies to tackle censored covariates, including weighting, imputation, maximum likelihood, and Bayesian methods. Still, this is a relatively fresh area of research, different from the

Melded confidence intervals were proposed as a way to combine two independent one-sample confidence intervals to obtain a two-sample confidence interval for a quantity like a difference or a ratio....

Abstract The rise of internet-based services and products in the late 1990’s brought about an unprecedented opportunity for online businesses to engage in large scale data-driven decision making. Over the past two decades, organizations such as Airbnb, Alibaba, Amazon, Baidu, Booking.com, Alphabet’s Google, LinkedIn, Lyft, Meta’s Facebook, Microsoft, Netflix, Twitter, Uber, and Yandex have invested

One important strategic question in sustainable operations is how explicitly internalizing the societal impact of undesirable outputs (UO) would affect a company's relative competitiveness: the discrepancy between eco-efficiency and productive efficiency. This paper presents a DEA approach to evaluating the impact of considering UO on productive efficiency. The main challenge to be overcome is that

The energy consumption in water distribution systems (WDSs) is significant. Improving the efficiency of pump operation can significantly reduce energy costs. However, optimal pump operation is a nonconvex mixed-integer nonlinear programming (MINLP) problem, which can be challenging to solve. A feasible approach is to linearize the problem and convert it into a mixed-integer linear programming (MILP)

Agricultural waste management in developing countries has become a challenging issue for rural planners due to the lack of an efficient planning tool. In the countries, farmers burnt agricultural waste at fields after each harvesting season to solve the issue. As a result, it has caused air and water pollution in the rural areas of the countries. In this paper, we present a mixed-integer nonlinear

Abstract During the COVID-19 pandemic, many institutions such as universities and workplaces implemented testing regimens with every member of some population tested longitudinally, and those testing positive isolated for some time. Although the primary purpose of such regimens was to suppress disease spread by identifying and isolating infectious individuals, testing results were often also used to

Abstract Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals (e.g., via previous outcomes and covariates). The optimal dynamic treatment regime is a regime that maximizes counterfactual welfare. We introduce a framework in which we can partially learn the optimal dynamic regime from observational data, relaxing the sequential randomization assumption commonly employed

Abstract Gamma ray bursts are flashes of light from distant, new-born black holes. CubeSats that monitor high-energy photons across different energy bands are used to detect these bursts. There is a need for computationally efficient algorithms, able to run using the limited computational resource onboard a CubeSats, that can detect when gamma ray bursts occur. Current algorithms are based on monitoring

Abstract– Networks analysis has been commonly used to study the interactions between units of complex systems. One problem of particular interest is learning the network’s underlying connection pattern given a single and noisy instantiation. While many methods have been proposed to address this problem in recent years, they usually assume that the true model belongs to a known class, which is not verifiable

Abstract The estimation of the central space is at the core of the sufficient dimension reduction (SDR) literature. However, it is well known that the finite-sample estimation suffers from collinearity among predictors. Cook et al. (2013) proposed the predictor envelope method under linear models that can alleviate the problem by targeting a bigger space – which not only envelopes the central information

In univariate time series forecasting, models are typically updated at every single review period. This practice, which includes specifying the optimal form of the model and estimating its parameters, theoretically allows the models to exploit new information and to respond quickly to possible structural breaks. We argue that such updates may be irrelevant in practice, also unnecessarily increasing

Innovation collaboration has become increasingly important for supply chain partners. In this study, we examine the impact of innovation collaboration on supply chain partners, specifically focusing on how suppliers can design strategies to motivate downstream buyers to engage in cost-reduction research and development (R&D) activities. We present a scenario in which two downstream manufacturers purchase

Selecting an appropriate logistics mode is crucial for overseas suppliers (OSs) who enter new markets through the cross-border e-commerce platform (CEP), but it is challenging because OSs are unfamiliar with the local markets. Thus motivated, we aim at a supply chain where an OS sells its product through a CEP that holds demand information advantages, and the OS faces competition from the domestic

Abstract Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences. One prominent motivation to acquire such data is to enable new discoveries of the underlying connectivity by combining multimodal

Due to increasing demand uncertainty and product variety, manufacturing systems must continually adapt to maintain productivity. Generally, decision-makers can maintain flexibility to account for these adaptations efficiently already in the design phase of manufacturing systems. Consequently, over the past few decades, a significant body of literature has addressed manufacturing system design associated

Dynamic wireless power transfer (DWPT) technology offers the promise of eliminating the limited driving range of electric buses (EBs), providing greater convenience and safety benefits through the ability to charge EBs in motion. It may be possible to effectively circumvent the drawbacks of EBs such that it becomes mainstream in the future EB market. In this paper, we study the robust DWPT deployment

Abstract Causal inference for extreme events has many potential applications in fields such as climate science, medicine and economics. We study the extremal quantile treatment effect of a binary treatment on a continuous, heavy-tailed outcome. Existing methods are limited to the case where the quantile of interest is within the range of the observations. For applications in risk assessment, however

Abstract In many applications, the interest is in treatment effects on random quantities of subjects, where those random quantities are not directly observable but can be estimated based on data from each subject. In this paper, we propose a general framework for conducting causal inference in a hierarchical data generation setting. The identifiability of causal parameters of interest is shown under

We study a single-machine scheduling problem which minimizes total earliness, tardiness and idleness costs. In this problem, n jobs with job-specific due dates and processing times need to be processed in a non-preemptive fashion. We assume that when the idle time between two jobs is strictly positive, an idleness cost will be generated which is affine in the idle time. A hybrid solution approach is

This paper makes an attempt to explore the relative value of such joint liability financing scheme over traditional individual financing scheme for two financially-constrained firms. We develop a risk-sharing non-cooperative Nash-game model for the two firms in which they make unilateral ordering and financial decisions based on the CJL financing agreement as to the additional benefit (i.e., unit financing

Manufacturers launch and advertise new generations of products through e-retailers. However, this advertising effort also benefits independent remanufacturers selling the previous generation of the same product online because of product recommendation algorithms and consumer category search. Through a stylized game-theoretic model, we investigate how independent, arm’s-length, and cooperative digital

In a simple connected graph G=(V,E), a subset of vertices S⊆V is a dominating set if any vertex v∈V∖S is adjacent to some vertex x from this subset. A number of real-life problems can be modeled using this problem which is known to be among the difficult NP-hard problems in its class. We formulate the problem as an integer liner program (ILP) and compare the performance with the two earlier existing