We develop a simple game theoretic model to study the impact of a natural disaster on the coordination of defensive efforts by a target state (G) and an ally (A) in relation to the choice to stage an attack by a terrorist (T), to examine how the realization of a natural disaster can impact strategic choices in such a setting. We focus on “long term impacts” in which a natural disaster increases costs

Abstract This study develops a new limited information estimator for random intercept Multilevel Structural Equation Models (MSEM). It is based on the Model Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) estimator, which has been shown to be an excellent alternative or supplement to maximum likelihood (ML) in SEMs (Bollen, 1996 Bollen, K. A. (1996). An alternative two stage least

There are very few simulation experiments in the literature of multiple input-output production frontiers. Among these studies, simulations are usually designed for the case of one single output, according to their purpose of the studies. In this study, a simulation framework is proposed to examine whether a multiple input-output production frontier model is satisfactory, according to its objectives

Replacing fossil fuels by renewable liquid fuels produced from electricity, biomass, and carbon dioxide can substantially reduce emissions in the transport sector. A multitude of planning tasks arise as the widespread use of renewable fuels requires extensive investments in renewable electricity generation, fuel production plants, storage facilities, and pipeline-based transport infrastructures. Hence

Stochastic multistage decision problems appear in many - if not all - application areas of Operations Research. While to define such problems is easy, to solve them is quite difficult, since they are of infinite dimension. Numerical solution can only be found by solving an approximate, easier problem. In this paper, we show good approximations can be found, where we emphasize the recursive structure

In this paper, we discuss the order batching problem in manual order picking systems. In such systems, order pickers move through a warehouse to collect items that are requested by customers. Customer orders have to be grouped into picking orders of limited size to ensure that the total length of the picker tours to collect all items is minimized. Motivated by real-world restrictions, we assume that

The container relocation problem, also known as the block(s) relocation problem, is one of the most studied optimization problems in container terminals. The problem aims at minimizing the total number of relocations for retrieving containers from a storage yard according to a specific order. The purpose of this study is to develop an efficient iterative deepening branch-and-bound algorithm for exactly

Abstract The present study evaluated the performance of robust variance estimation (RVE) and random-effects TSSEM with missing at random (MAR) data under realistic conditions. The performance of the two methods was compared in terms of the first stage of MASEM entailing the pooling of correlation estimates and in the second stage when the SEM model is estimated. Findings from this study indicated that

Abstract This study develops a method of measuring the i mportance of latent predictors and testing their importance ordering. A popular measure for relative importance, called dominance analysis, is extended to structural equation models such that the contribution to the variation of the outcome variable is attributed to each latent predictor. This measure is computed through the average R-squared

A recursive time aggregation-disaggregation (RAD) heuristic is proposed to solve large-scale multidimensional and multiperiod precedence-constrained knapsack problems (MMPKP) in which a profit is maximized by filling the knapsack in multiple periods while satisfying minimum and maximum resource consumption constraints per period as well as precedence constraints between items. An important strategic

Increased environmental awareness has resulted in consumers’ willingness to pay more for products with a smaller environmental footprint. Additionally, emissions trading schemes reward manufacturing firms who produce products with lower-than-average emissions rates. We examine how a manufacturer makes effective sourcing decisions, even if a supplier holds a responsibility certificate that only discloses

Abstract Despite the widespread application of bifactor models, little research has considered required sample sizes for this type of model. As universal sample size recommendations are often misleading, we illustrate how to determine sample size requirements of bifactor models using Monte Carlo simulations in R. Furthermore, we present results of an extensive simulation study investigating the effects

We present decentralized models for coordinating order acceptance and release planning under load-dependent lead times. Our centralized models use detailed information at the item level, while the decentralized models decompose the decision process into order acceptance and order release subproblems that are solved sequentially. Demand uncertainty is addressed by implementing the proposed models in

This paper aims at integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. Specifically, our study develops a novel efficient iterated greedy algorithm based on reinforcement learning. The main novelty of the proposed algorithm is its new perturbation mechanism, which incorporates Q-learning to select appropriate perturbation operators during the

We provide a data-driven framework to conduct a risk assessment, including data pre-processing, exploration, and statistical modeling, on a portfolio of full-service maintenance contracts. These contracts cover all maintenance-related costs for a fixed, upfront fee during a predetermined horizon. Charging each contract a price proportional to its risk prevents adverse selection by incentivizing low

This study analyzes the effects of farmers’ risk on productivity where the production function is generalized to be specific to risk variables. This resulted in a semiparametric smooth-coefficient (SPSC) production function. The novelty of the SPSC approach is that it can explain the direct and indirect channels through which risk can affect productivity. The study uses several measures of risk, including

The COVID-19 pandemic has had an unprecedented impact on global health and the economy since its inception in December, 2019 in Wuhan, China. Non-pharmaceutical interventions (NPI) like lockdowns and curfews have been deployed by affected countries for controlling the spread of infections. In this paper, we develop a Mixed Integer Non-Linear Programming (MINLP) epidemic model for computing the optimal

We introduce and solve a new multi-commodity Vehicle Routing Problem, motivated by a case study of milk collection in Chile. Different grades of raw milk are collected from a number of farms scattered over a large area and transported to a single plant, allowing milk blending at the trucks. Previous works allow blending different grades of milk in the trucks, but the resulting blend is classified as

Abstract Selecting relevant features associated with a given response variable is an important problem in many scientific fields. Quantifying quality and uncertainty of a selection result via false discovery rate (FDR) control has been of recent interest. This paper introduces a data-splitting method (referred to as “DS”) to asymptotically control the FDR while maintaining a high power. For each feature

Abstract Different from traditional practice that considers factor analysis as either exploratory or confirmatory, different amounts of substantive information can be available in between the confirmatory and exploratory extremes under the partially confirmatory approach. Based on Bayesian Lasso methods, three models were recently proposed for various types of data under the new approach: the partially

Abstract Latent class analysis (LCA) assigns individuals to mutually exclusive classes based on response patterns to a set of indicators. A primary assumption made is local independence, which suggests class indicators are uncorrelated within each class. When the indicators are correlated and unmodeled, parameter estimates can be severely biased. We provide a comprehensive resource for applied researchers

Abstract The social relations model is a statistical tool that allows the analysis of group dynamics as dyadic interactions between individuals. Within a multiple group structural equation modeling framework, Wald-tests and likelihood ratio tests based on (1) equality constraints among model parameters and (2) Lagrange multipliers for restrictions among non-linear parameter transformations are presented

Abstract Under the framework of Latent Variable Modelling, three statistical methods, namely, the free baseline method (FR), the Benjamini-Hochberg method (B–H), and the alignment method (AM), were applied to identify the noninvariant measurement parameters at the indicator level through a simulation study. Model noninvariance was manipulated through varying the locations, degrees and magnitudes of

Nowadays, fast delivery services have created the need for high-density warehouses. The puzzle-based storage system is a practical way to enhance the storage density, however, facing difficulties in the retrieval process. In this work, a deep reinforcement learning algorithm, specifically the Double&Dueling Deep Q Network, is developed to solve the multi-item retrieval problem in the system with general

We review dynamic programming (DP) algorithms utilized to solve offline deterministic single-machine scheduling problems. We classify DP algorithms based on problem properties and provide insights on how these properties facilitate the use of specific types of DP algorithms. These properties center on whether jobs in a schedule can be naturally partitioned into subsets or whether a complete schedule

Individual treatment effect models allow optimizing decision-making by predicting the effect of a treatment on an outcome of interest for individual instances. These predictions allow selecting instances to treat in order to optimize the overall efficiency and net treatment effect. In this article, we extend upon the expected value framework and introduce a cost-sensitive causal classification boundary

Abstract For many decades now, Bayesian Model Averaging (BMA) has been a popular framework to systematically account for model uncertainty that arises in situations when multiple competing models are available to describe the same or similar physical process. The implementation of this framework, however, comes with a multitude of practical challenges including posterior approximation via Markov Chain

Abstract We adapt the social game “Two truths and a lie” to a classroom setting to give an activity that introduces principles of statistical measurement, uncertainty, prediction, and calibration, while giving students an opportunity to meet each other. We discuss how this activity can be used in a range of different statistics courses.

Abstract In randomized trials, once the total effect of the intervention has been estimated, it is often of interest to explore mechanistic effects through mediators along the causal pathway between the randomized treatment and the outcome. In the setting with two sequential mediators, there are a variety of decompositions of the total risk difference into mediation effects. We derive sharp and valid

Production scheduling that involves maintenance activities and resource constraints plays a crucial role in manufacturing and service environments of the modern age. While research on the combination of production-maintenance scheduling and production-resource scheduling is constantly increasing, limited research is available on the integration of all three aforementioned scheduling problems. To unite

This study deals with an extension of the resource-constrained project scheduling problem (RCPSP) by constraints on material flows released during the execution of project activities. These constraints arise from limited processing capacities for materials and maximum inventories of intermediate storage facilities. Production scheduling problems with converging material flows have been studied extensively

The recently-defined class of integer programming games (IPG) models situations where multiple self-interested decision makers interact, with their strategy sets represented by a finite set of linear constraints together with integer requirements. Many real-world problems can suitably be cast in this way, hence anticipating IPG outcomes is of crucial value for policy makers. Nash equilibria have been

Capturing business process specifics using a model is essential to effectively manage, control, and instruct the process participants with their roles and tasks. A normative process model is an invaluable source of information, not only for human inspection but also for software supporting and controlling the process. The actual process execution likely deviates from the normative model and the magnitude

Summary Direct regression for the cumulative incidence function (CIF) has become increasingly popular since the Fine and Gray model was suggested [ Fine and Gray,?1999] due to its more direct interpretation on the probability risk scale. We here consider estimation within the Fine and Gray model using the theory of semiparametric efficient estimation. We show that the Fine and Gray estimator is semiparametrically

Abstract In a modern observational study based on healthcare databases, the number of observations and of predictors typically range in the order of 105～106 and of 104～105. Despite the large sample size, data rarely provide sufficient information to reliably estimate such a large number of parameters. Sparse regression techniques provide potential solutions, one notable approach being the Bayesian

Abstract Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is utilized in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study

This study considers a single-period make-to-order system in which a manufacturer produces two products: product 1 (2) with tighter (looser) specifications. Production of these two products involves a specific type of materials in a common process. Due to quality discrepancies, new materials purchased from the supplier are used in the production of both product types from which spent materials can

In the service industry, a service provider may sell a collection of service activities as a package, also known as a service bundle. Empirical studies indicate that the customer’s ex-post perception of a service bundle depends on not only the utility of each activity, but also the sequence of the activities being delivered. The latter can be measured by certain sequence effects, such as the utility

Abstract Mechanisms of behavior change are the processes through which interventions are hypothesized to cause changes in outcomes. Latent growth curve mediation models (LGCMM) are recommended for investigating the mechanisms of behavior change because LGCMM models establish temporal precedence of change from the mediator to the outcome variable. The Correlated Augmented Mediation Sensitivity Analyses

Abstract Longitudinal research often involves relatively small samples and missing values. Under these conditions, Bayesian estimation can still result in accurate parameter estimates for latent growth models (LGMs). However, researchers were limited in their options for assessing model fit. Several new (approximate) model fit indices have been introduced into the Bayesian structural equation modeling

Abstract The choice of prior specification plays a vital role in any Bayesian analysis. Prior-data disagreement occurs when the researcher’s prior knowledge is not in agreement with the evidence provided by the data. We examined the ability of the Data Agreement Criterion (DAC) and Bayes Factor (BF) to detect prior-data disagreement in SEM through a simulation study. The design included four sample

Abstract While latent class (LC) modeling using bias-adjusted stepwise approaches has become widely popular, little is known on how these methods are affected by missing values. Using synthetic data sets, we illustrate under which conditions missing values introduce biases in the estimates of the relationship between class membership and auxiliary variables. We apply three-step LC analysis with both

Abstract Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood

Asymptotic statistical inference on productivity and production efficiency, using nonparametric envelopment estimators, is now available thanks to the basic central limit theorems (CLTs) developed in Kneip, Simar, and Wilson (2015). They provide asymptotic distributions of averages of Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH) estimators of production efficiency. As shown in their

We analyse the role of new weak and strong commensurability conditions on efficiency measures and especially on productivity measurement. If strong commensurability fails, then a productivity index (indicator) may exhibit a homogeneity bias yielding inconsistent and contradictory results. In particular, we show that the Luenberger productivity indicator is sensitive to proportional changes in the input-output

Consensus reaching process (CRP) is a dynamic and interactive method used to reach a group decision. Now that social networks and mobile internet are prominent features in daily life, more experts are able to take participate in decision making in the network and their opinions are influenced by others in the decision-making process. Therefore, how to use the difference of opinions and the relationships

This paper presents a two-stage hybrid manufacturing system in which the first stage produces semifinished products to store as intermediate inventory in a make-to-stock mode, whereas the second stage produces customized products from semifinished goods in a make-to-order mode. The system faces external demand for semifinished goods with a compound Poisson process and has the option of controlling

(2022). Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. Journal of the American Statistical Association: Vol. 117, No. 537, pp. 527-527.

(2022). An Introduction to Acceptance Sampling and SPC with R. Journal of the American Statistical Association: Vol. 117, No. 537, pp. 528-528.

(2022). Rejoinder. Journal of the American Statistical Association: Vol. 117, No. 537, pp. 128-132.

(2022). Comments on “Factor Models for High-Dimensional Tensor Time Series”. Journal of the American Statistical Association: Vol. 117, No. 537, pp. 124-127.

(2022). Comment on “Factor Models for High-Dimensional Tensor Time Series”. Journal of the American Statistical Association: Vol. 117, No. 537, pp. 118-123.

We consider the irregular strip packing problem of rasterized shapes, where a given set of pieces of irregular shapes represented in pixels should be placed into a rectangular container without overlap. The rasterized shapes provide simple procedures of the intersection test without any exceptional handling due to geometric issues, while they often require much memory and computational effort in high-resolution

Assortment planning is a central piece in the revenue management strategy of every retail company. In this paper, we study a robust assortment optimization problem for substitutable products under a sequential ranking-based choice model and a cardinality constraint. Our choice model captures the increasing customer frustration of finding multiple products unavailable as a factor affecting purchasing

This study incorporates pricing decision into a congested facility location problem with immobile servers. The operations of facilities are modelled as queueing systems with price- and distance-sensitive streams of customer arrivals. Given a set of potential locations, a central service provider decides on the location of facilities, the allocation of customers to open facilities and also the service

Abstract Double blind randomized controlled trials are traditionally seen as the gold standard for causal inferences as the difference-in-means estimator is an unbiased estimator of the average treatment effect in the experiment. The fact that this estimator is unbiased over all possible randomizations does not, however, mean that any given estimate is close to the true treatment effect. Similarly

This paper provides a novel characterization of decision makers’ nonmonotonic risk preferences beyond risk aversion, prudence, temperance, etc. Specifically, it establishes a framework that uses decision-makers’ choices between binary lotteries to characterize their nonmonotonic risk preferences. This framework extends the seminal work of Eeckhoudt and Schlesinger [Eeckhoudt, L. and H. Schlesinger

We consider newsvendor models with a primary supplier whose production is subject to random capacity. In order to hedge against this supply risk the retailer contracts with a reliable backup supplier to reserve capacity in advance, acquiring the option to use it after the delivery from the primary supplier and either before or after the demand realization. For both cases we identify the conditions

Efficient solid waste management is one of the most relevant issues for urban communities. With regard to the service type of household waste collection, there are two approaches in practice: when collecting household waste via a door-to-door system, the collection vehicles drive down all the streets to empty the garbage cans on the curb. Using a bring system, waste is accumulated at central collection

Abstract We propose a new method, the Monte Carlo confidence interval (MCCI) method, for studying measurement invariance. This method allows researchers to examine the invariance of all items simultaneously by comparing the observed between-group differences in parameters with those obtained under the null hypothesis of invariance. We compare the performance of our method to two other methods: the