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This research formulates, and numerically quantifies the optimal response that can be discovered in a design space characterized by main effects, and two-way and three-way interactions. In an experimental design setup, this can be conceptualized as the response of the best treatment combination of a $$2^k$$ 2 k full factorial design. Using Gaussian and Uniform priors for the strength of main effects and interaction effects, this study enables a practitioner to make estimates of the maximum possible improvement that is possible through design space exploration. For basic designs up to two factors, we construct the full distribution of the optimal treatment. Whereas, for values of $$k\ge 3$$ k ≥ 3 , we analytically formulate two indicators of a greedy heuristic of the expected value of the optimal treatment. We present results for these formulations up to $$k=7$$ k = 7 factors and validate these through simulations. Finally, we also present an illustrative case study of the power loss in disengaged wet clutches, which confirms our findings and serves as an implementation guide for practitioners.

This study investigates the important role of attendant factors, such as road traffic victims’ access to trauma centres, the robustness of health infrastructure, and the responsiveness of police and emergency services in the incidence of Road Traffic Injuries (RTI) during the pandemic-induced COVID-19 lockdowns. The differential effects of the first and second waves of the pandemic concerning perceived health risk and legal restrictions provide us with a natural experiment that helps us differentiate between the impact of attendant factors and the standard relationship between mobility and Road Traffic Injuries. The authors use the auto-regressive recurrent neural network method on two population levels–Tamil Nadu (TN), a predominantly rural state, and Chennai, the most significant metropolitan city of the state, to draw causal inference through counterfactual predictions on daily counts of road traffic deaths and Road Traffic Injuries. During the first wave of the pandemic, which was less severe than the second wave, the traffic flow was correlated to Road Traffic Death/Road Traffic Injury. In the second wave’s partial and post lockdown phases, an unprecedented fall of over 70% in Road Traffic Injury—Grievous as against Road Traffic Injury—Minor was recorded. Attendant factors, such as the ability of the victim to approach relief centres, the capability of health and other allied infrastructures, transportation and medical treatment of road traffic crash victims, and minimal access to other emergency services, including police, assumed greater significance than overall traffic flow in the incidence of Road Traffic Injury in the more severe second wave. These findings highlight the significant role these attendant factors play in producing the discrepancy between the actual road traffic incident rate and the officially registered rate. Thus, our study enables practitioners to observe the mobility-adjusted actual incidence rate devoid of factors related to reporting and registration of accidents.

The use of Association Rule Mining techniques in diverse contexts and domains has resulted in the creation of numerous interestingness measures. This, in turn, has motivated researchers to come up with various classification schemes for these measures. One popular approach to classify the objective measures is to assess the set of mathematical properties they satisfy in order to help practitioners select the right measure for a given problem. In this research, we discuss the insufficiency of the existing properties in literature to capture certain behaviors of interestingness measures. This motivates us to present a novel approach to analyze and classify measures. We refer to this as a rate of change analysis (RCA). In this analysis a measure is described by how it varies if there is a unit change in the frequency count $(f_{11},f_{10},f_{01},f_{00})$, for different pre-existing states of the frequency counts. More formally, we look at the first partial derivative of the measure with respect to the various frequency count variables. We then use this analysis to define two new properties, Unit-Null Asymptotic Invariance (UNAI) and Unit-Null Zero Rate (UNZR). UNAI looks at the asymptotic effect of adding frequency patterns, while UNZR looks at the initial effect of adding frequency patterns when they do not pre-exist in the dataset. We present a comprehensive analysis of 50 interestingness measures and classify them in accordance with the two properties. We also present empirical studies, involving both synthetic and real-world datasets, which are used to cluster various measures according to the rule ranking patterns of the measures. The study concludes with the observation that classification of measures using the empirical clusters share significant similarities to the classification of measures done through the properties presented in this research.

This paper explores the use of designed experiments in an online environment. Motivated by real-world examples, we model a scenario where the practitioner is given a finite set of units and needs to select a subset of these which are expended toward a one-shot, multi-factor designed experiment. Following this phase, the designer is left with the remaining set of unused units to implement any learnings from the experiments. With this setting, we answer the key design question of how much to experiment, which translates to choosing the number of replicates for a given design. We construct a Bayesian framework that captures the expected cumulative gain across the entire set of units. We derive theoretical results for the optimal number of replicates for all two-level, full and fractional factorial designs with seven factors or fewer. We conduct simulations that serve as validation of the theoretical results, as well as enabling us to explore scenarios and techniques of analysis that are not captured in the theoretical studies. Our overall results indicate that the optimal allocation of units for experimentation varies from 1 to $$20\%$$ 20 % of the total units available, which is mainly governed by the experimental environment and the total number of units. We conclude that experimenting with the optimal number of replicates recommended by our study can lead to a cumulative improvement which is 80–95% greater than the expected cumulative improvement gained when a practitioner chooses the number of replicates randomly.

Data from Global Positioning Systems (GPS) and fare-meters in For-Hire vehicles (FHVs) have been used for various applications – both in research as well as organizational decision-making. The utility of such exercises largely depend on the accuracy of the data. This study looks at an environment where the data is partially mislabeled. Specifically, we take a common real-world setting where vehicle operators choose to render transportation services to customers without the use of a fare-meter, often by negotiating a fixed rate with the customer. This practice, which to different degrees, has been observed and documented across urban areas in the world, leads to various undesirable effects. In this study, we seek to identify cases of such behavior in the dataset. Typically, a supervised learning classifier could be built to predict the occupancy status from GPS traces, which can then be used, to look for anomalies between the predicted and stated behaviors. However, in our case the training dataset also contains instances of incorrect tagging. We address this problem by casting it as one of learning from Positive and Unlabeled instances (PU Learning) . This is owing to the fact that we observe the phenomenon of one-sided label noise, where trips tagged ‘vacant’ by the taximeter could be truly vacant or occupied, whereas trips tagged ‘occupied’ are expected to be occupied in reality as well. To support this novel formulation, we apply three state-of-the-art PU Learning algorithms on a real-world trajectory data set from an organization plying 170 active vehicles over a period of two months. We compare these to the baselines of standard supervised learning. Validation is carried out by the organization through alternate channels of investigation which is not indicated in the data set. The results show that the PU Learners provide a significant improvement in classification across a range of metrics when compared to the baseline approaches. This translates to a significant increase in identifying or reclassifying the mislabeled rides.

Primary Caregivers are the fulcrum in the physician-caregiver-child triad. Existing literature discusses static multi-component interventions in detail. In long-term treatments, dynamic intervention design is needed as the environment and situations of the families are dynamic. The objectives of this study are (a) to identify the components of the primary caregiver's perception of the physician's value with reference to the effectiveness of consultation and relationships with the former and with the child; (b) to establish the role of this perception in designing dynamic interventions, and (c) to describe the perception's potential influence on adherence. A PRISMA, chronological, and morphological analysis of the literature is carried out about caregivers' adherence in the pediatric long-term treatment context. We define communication and consultation as the functional, whereas relationship as the emotional component of the caregiver's perception of the physician. We propose a theoretical model that incorporates intervention as an integral component of care. Adherence happens as a response to changing situations and hence fluctuates. Hence, a dynamic intervention design to benefit the child should be incorporated into care through the caregiver-physician bridge. Future research should explore how intervention needs change and the driving reasons for understanding the static and dynamic components of interventions.

This research studies the use of predetermined experimental plans in a live setting with a finite implementation horizon. In this context, we seek to determine the optimal experimental budget in different environments using a Bayesian framework. We derive theoretical results on the optimal allocation of resources to treatments with the objective of minimizing cumulative regret, a metric commonly used in online statistical learning. Our base case studies a setting with two treatments assuming Gaussian priors for the treatment means and noise distributions. We extend our study through analytical and semi-analytical techniques which explore worst-case bounds, the presence of unequal prior distributions, and the generalization to k treatments. We determine theoretical limits for the experimental budget across all possible scenarios. The optimal level of experimentation that is recommended by this study varies extensively and depends on the experimental environment as well as the number of available units. This highlights the importance of such an approach which incorporates these factors to determine the budget.

There is a growing interest in understanding, as opposed to predicting, the repayment behavior of customers of financial institutions that provide loans. This study proposes a modified Hidden Markov Model (HMM) based clustering, which clusters repayment sequences across selected subsets of the HMM parameters. We demonstrate that different implementations of this modified adaptation helps us gain an in-depth understanding of various drivers that are hard to directly observe but nevertheless govern repayment. These include drivers such as the ability to repay (financial health of the customer) or the intention to repay independent of the ability (willful defaulting and unintentional delinquency). Algorithmically, we achieve this partially constrained HMM clustering (PC-HMM) by placing constraints on the expectation-maximization (EM) algorithm where a subset of parameters are used to cluster the repayments via the estimation process, while the other parameters are learned globally across all repayments. We compare our approach with three other baselines on a real-world loan repayment data set. We use an exogenous variable to validate and benchmark the clusters. We conclude our study with the observation that the ability to cluster on selective parameters, in conjunction with the structural construct of HMMs, enables the discovery of substantially more meaningful business insights than the baselines.

In this paper we propose the Augmented-UCB (AugUCB) algorithm for a fixed-budget version of the thresholding bandit problem (TBP), where the objective is to identify a set of arms whose quality is above a threshold. A key feature of AugUCB is that it uses both mean and variance estimates to eliminate arms that have been sufficiently explored; to the best of our knowledge this is the first algorithm to employ such an approach for the considered TBP. Theoretically, we obtain an upper bound on the loss (probability of mis-classification) incurred by AugUCB. Although UCBEV in literature provides a better guarantee, it is important to emphasize that UCBEV has access to problem complexity (whose computation requires arms' mean and variances), and hence is not realistic in practice; this is in contrast to AugUCB whose implementation does not require any such complexity inputs. We conduct extensive simulation experiments to validate the performance of AugUCB. Through our simulation work, we establish that AugUCB, owing to its utilization of variance estimates, performs significantly better than the state-of-the-art APT, CSAR and other non variance-based algorithms., Comment: 7 pages, Accepted at Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017, 2515-2521

Pediatric treatment adherence is a complex multidimensional construct due to the presence of entities other than the patient/child. The primary caregivers, physician and the patient form the triad ...

This article documents a meta-analysis of 113 data sets from published factorial experiments. The study quantifies regularities observed among factor effects and multifactor interactions. Such regularities are known to be critical to efficient planning and analysis of experiments and to robust design of engineering systems. Three previously observed properties are analyzed: effect sparsity, hierarchy, and heredity. A new regularity is introduced and shown to be statistically significant. It is shown that a preponderance of active two-factor interaction effects are synergistic, meaning that when main effects are used to increase the system response, the interaction provides an additional increase and that when main effects are used to decrease the response, the interactions generally counteract the main effects. © 2006 Wiley Periodicals, Inc. Complexity 11: 32– 45, 2006

This paper presents a conceptually simple and resource efficient method for robust parameter design. The proposed method varies control factors according to an adaptive one-factor-at-a-time plan while varying noise factors using a two-level resolution III fractional factorial array. This method is compared with crossed arrays by analyzing a set of four case studies to which both approaches were applied. The proposed method improves system robustness effectively, attaining more than 80% of the potential improvement on average if experimental error is low. This figure improves to about 90% if prior knowledge of the system is used to define a promising starting point for the search. The results vary across the case studies, but, in general, both the average amount of improvement and the consistency of the results are better than those provided by crossed arrays if experimental error is low or if the system contains some large interactions involving two or more control factors. This is true despite the fact that the proposed method generally uses fewer experiments than crossed arrays. The case studies reveal that the proposed method provides these benefits by exploiting, with high probability, both control by noise interactions and also higher order effects involving two control factors and a noise factor. The overall conclusion is that adaptive one-factor-at-a-time, used in concert with factorial outer arrays, is demonstrated to be an effective approach to robust parameter design providing significant practical advantages as compared to commonly used alternatives.