Data-generating Process Research Articles

EXPRESS: Identifying Competitors in Geographical Markets Using the CSIS Method

Identifying the most relevant competitors in a geographical market is crucial for businesses with a significant offline presence, such as hotels, restaurants, and retail stores. The specific location of a business and the geographical density of potential competitors are critical factors in determining the competitive structure. However, this task can be challenging when the potential number of competitors is large and the competition is asymmetric. In this study, we apply the Conditional Sure Independence Screening (CSIS) method to a system of demand functions for competitor identification. This method offers significant computational efficiency by estimating a marginal regression for each potential competitor, rather than a full model consisting of all potential competitors. To validate the effectiveness of the CSIS method and explore the boundary conditions of its performance, we conduct extensive simulation analyses under different spatial data-generating processes. Our findings demonstrate that the CSIS method outperforms multiple other variable selection methods and remains robust under spatial misspecifications. Then we apply the CSIS method to hotel competition in two U.S. geographical regions, illustrating how the competitive structure varies across geographical densities and market segments. Finally, we highlight how managers can strategically use the results and outline the potential of the method for other non-geographical applications.

The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration.

Machine learning methods are increasingly being used to predict clinical outcomes. Optimism is the difference in model performance between derivation and validation samples. The term "data hungriness" refers to the sample size needed for a modelling technique to generate a prediction model with minimal optimism. Our objective was to compare the relative data hungriness of different statistical and machine learning methods when assessed using calibration. We used Monte Carlo simulations to assess the effect of number of events per variable (EPV) on the optimism of six learning methods when assessing model calibration: unpenalized logistic regression, ridge regression, lasso regression, bagged classification trees, random forests, and stochastic gradient boosting machines using trees as the base learners. We performed simulations in two large cardiovascular datasets each of which comprised an independent derivation and validation sample: patients hospitalized with acute myocardial infarction and patients hospitalized with heart failure. We used six data-generating processes, each based on one of the six learning methods. We allowed the sample sizes to be such that the number of EPV ranged from 10 to 200 in increments of 10. We applied six prediction methods in each of the simulated derivation samples and evaluated calibration in the simulated validation samples using the integrated calibration index, the calibration intercept, and the calibration slope. We also examined Nagelkerke's R2, the scaled Brier score, and the c-statistic. Across all 12 scenarios (2 diseases × 6 data-generating processes), penalized logistic regression displayed very low optimism even when the number of EPV was very low. Random forests and bagged trees tended to be the most data hungry and displayed the greatest optimism. When assessed using calibration, penalized logistic regression was substantially less data hungry than methods from the machine learning literature.

Traffic Flow Outlier Detection for Smart Mobility Using Gaussian Process Regression Assisted Stochastic Differential Equations

Current methods for detecting outliers in traffic streaming data often struggle to capture real-time dynamic changes in traffic conditions and differentiate between genuine changes and anomalies. This study proposes a novel approach to outlier detection in traffic streaming data that effectively addresses stochasticity and uncertainty in observations. The proposed method utilizes Stochastic Differential Equations (SDEs) and Gaussian Process Regression (GPR). By employing SDEs, we can capture drift and diffusion estimates in traffic streaming data, providing a more comprehensive modeling of the data generation process. Integrating GPR allows precise Bayesian posterior inferences for outlier detection within the SDE framework. To improve practicality, we introduce a flexible threshold-setting mechanism using statistical testing to control the false positive rate. This adaptability helps strike a balance between model fitting and complexity in outlier detection. Compared to traditional SDE-based methods, our SDE-GPR outlier detection method demonstrates enhanced robustness and better adaptability to the complexities of traffic systems. This is evidenced through an empirical study using time series data collected in California, USA. Overall, this study introduces a more advanced and accurate approach to outlier detection in traffic streaming data, paving the way for improved real-time traffic condition monitoring and management.

Score-driven exponential random graphs: A new class of time-varying parameter models for temporal networks.

Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the exponential random graph models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on dynamic conditional score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.

Climate change and economic growth: Some critical reflections

Global warming may affect the economic development and, thus, the welfare of people around the world. Therefore, the economic effects of a changing climate should be known in order to be able to design appropriate policy responses. In the economics literature, one research field empirically analyzes the growth effects of global warming. But often those studies do not account for economic variables that have turned out to be significant in explaining economic growth. In addition, they frequently fail to check for the robustness of their outcomes. This can give rise to biased results regarding the growth process and, therefore, does not necessarily reflect the true data-generating process. Hence, the question comes up: how valid and reliable the results are. Therefore, economic analyses should be undertaken that study the robustness of the results as regards the integration of fundamental economic variables. When policy recommendations are made on how to deal with global warming, we argue that they should be based on robust results only. If that does not hold, economic policy risks being inadequate, giving rise to substantial welfare losses.

A bayesian shared component spatial modeling approach for identifying the geographic pattern of local associations: a case study of young offenders and violent crimes in Greater Toronto Area

Background settingTraditional spatial or non-spatial regression techniques require individual variables to be defined as dependent and independent variables, often assuming a unidirectional and (global) linear relationship between the variables under study. This research studies the Bayesian shared component spatial (BSCS) modeling as an alternative approach to identifying local associations between two or more variables and their spatial patterns.MethodsThe variables to be studied, young offenders (YO) and violent crimes (VC), are treated as (multiple) outcomes in the BSCS model. Separate non-BSCS models that treat YO as the outcome variable and VC as the independent variable have also been developed. Results are compared in terms of model fit, risk estimates, and identification of hotspot areas.ResultsCompared to the traditional non-BSCS models, the BSCS models fitted the data better and identified a strong spatial association between YO and VC. Using the BSCS technique allowed both the YO and VC to be modeled as outcome variables, assuming common data-generating processes that are influenced by a set of socioeconomic covariates. The BSCS technique offered smooth and easy mapping of the identified association, with the maps displaying the common (shared) and separate (individual) hotspots of YO and VC.ConclusionsThe proposed method can transform existing association analyses from methods requiring inputs as dependent and independent variables to outcome variables only and shift the reliance on regression coefficients to probability risk maps for characterizing (local) associations between the outcomes.

A Cross-Sectional Study of Serum Levels of Several Vitamins in Healthy and Unhealthy Periodontal Conditions

Vitamins are examples of micronutrients, which are nutrients needed in very minute quantities. They're crucial to metabolism and keeping tissues working properly. The purpose of this study was to determine whether vitamin deficiencies are associated with an increased risk of periodontitis. There was a total of 110 participants: 55 with chronic periodontitis spread throughout the mouth and 55 periodontally healthy individuals. Clinical parameters that were assessed included: Clinical attachment loss (in millimetres) and gingival index (in millimetres). Vitamin B12, vitamin D, folate, as well as vitamin E levels in serum samples were measured. Also measured were Beta-carotene and Beta-cryptoxanthin levels. The acquired data was aggregated and analysed using statistical programmes. All periodontal status clinical measures in the periodontitis group were statistically significantly different from those in the healthy volunteers group (p < 0.0001). Vitamin A precursors Beta-carotene and Beta-cryptoxanthin, folate, D, vitamins B12, and E, and vitamin E were all significantly lower in the periodontitis group compared to the healthy volunteers, but the difference was only statistically significant for vitamins B12 as well as D (p < 0.05). Finally, we conclude that serum micronutrient levels, particularly those of Vitamin A, Vitamin B12, and Vitamin D, may be modifiable risk factors for periodontal disease. In addition to practicing good dental hygiene on a regular basis, eating a diet rich in the right kinds of vitamins may play a significant part in warding off periodontitis.

A Time Series Synthetic Control Causal Evaluation of the UK’s Mini-Budget Policy on Stock Market

In this paper, we propose a modified synthetic control causal analysis for time series data with volatility in terms of absolute value of return outcomes taken into account in constructing the prediction of potential outcomes for time series causal analysis. The consistency property of the synthetic weight parameter estimators is developed theoretically under a time series data-generating process framework. The application to evaluate the UK’s mini-budget policy, announced by the then Chancellor on 23 September 2022, which had significant implications for the stock market, is examined and analysed. Comparisons with traditional synthetic control and synthetic difference in difference (DID) methods for evaluation of the effect of the mini-budget policy on the UK’s stock market are also discussed.

Modeling extreme events: Univariate and multivariate data-driven approaches

AbstractThis article summarizes the contribution of team genEVA to the EVA (2023) Conference Data Challenge. The challenge comprises four individual tasks, with two focused on univariate extremes and two related to multivariate extremes. In the first univariate assignment, we estimate a conditional extremal quantile using a quantile regression approach with neural networks. For the second, we develop a fine-tuning procedure for improved extremal quantile estimation with a given conservative loss function. In the first multivariate sub-challenge, we approximate the data-generating process with a copula model. In the remaining task, we use clustering to separate a high-dimensional problem into approximately independent components. Overall, competitive results were achieved for all challenges, and our approaches for the univariate tasks yielded the most accurate quantile estimates in the competition.

Bias Mitigation via Synthetic Data Generation: A Review

Artificial intelligence (AI) is widely used in healthcare applications to perform various tasks. Although these models have great potential to improve the healthcare system, they have also raised significant ethical concerns, including biases that increase the risk of health disparities in medical applications. The under-representation of a specific group can lead to bias in the datasets that are being replicated in the AI models. These disadvantaged groups are disproportionately affected by bias because they may have less accurate algorithmic forecasts or underestimate the need for treatment. One solution to eliminate bias is to use synthetic samples or artificially generated data to balance datasets. Therefore, the purpose of this study is to review and evaluate how synthetic data can be generated and used to mitigate biases, specifically focusing on the medical domain. We explored high-quality peer-reviewed articles that were focused on synthetic data generation to eliminate bias. These studies were selected based on our defined inclusion criteria and exclusion criteria and the quality of the content. The findings reveal that generated synthetic data can help improve accuracy, precision, and fairness. However, the effectiveness of synthetic data is closely dependent on the quality of the data generation process and the initial datasets used. The study also highlights the need for continuous improvement in synthetic data generation techniques and the importance of evaluation metrics for fairness in AI models.

Efficient approximate Bayesian inference for quantifying uncertainty in multiscale animal movement models

It is becoming increasingly important for wildlife managers and conservation ecologists to understand which resources are selected or avoided by an animal and how to best predict future spatial distributions of animal populations in the long term. However, inferring the patterns of space use by animals is a challenging multiscale inference problem, and formal uncertainty quantification of parameter estimates is an essential component of models that provide useful predictions across scales. In this study, we develop an approximate Bayesian inference framework for step selection models of animal movement which quantifies the uncertainty in estimates of resource selection and avoidance parameters within the Bayesian paradigm. The framework allows joint inference of movement and resource selection parameters of animals and is multiscale in that parameters inferred from fine scale movement steps scale to produce predictions of long-term patterns of space use. Our analysis focuses on simulated movement data in which we test the performance of our framework by altering movement parameters in the data-generating process. In our simulations, individuals respond to two environmental covariates and we employ all combinations of positive and negative selection coefficients corresponding to attraction to an environmental feature and avoidance of an environmental feature, respectively. In all scenarios, we recover the movement parameters used for the simulation of synthetic movement data using variational inference, an approximate Bayesian method, allowing us to formally quantify the uncertainty associated with each parameter for varying data set sizes. Our framework successfully recovered all combinations of movement parameters of the simulated data and accurately captured their posterior distributions given the available data suggesting that the framework is reliable and suitable for inferring how animals select resources and move on a landscape.Notably, our analysis shows that even for reasonably large data sets (circa 10,000 observations) there can still be considerable uncertainty associated with resource selection parameters which can in turn lead to inaccurate predictions of long term space use if not properly incorporated into the modelling approach. To further illustrate the utility of our approach, we also present a case study of its application to an example data set consisting of GPS locations of a fisher (Martes pennanti) Our approach will be of interest to ecologists looking to address conservation questions such as when and where animals are likely to spend most of their time. Furthermore, the approach could be used to predict new suitable areas for conservation based on how GPS collared animal use or avoid resources while including uncertainty around the predictions, thereby helping to make informed management decisions.

Self-inspired learning for denoising live-cell super-resolution microscopy.

Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances.

Weaving Our Kuwentos (Stories) toward Ginhawa (Aliveness): Pilipinx American Social Work MotherScholars Enacting Praxes of Survival and Thrivance in the Academy

Five Pilipina American (PA) social work MotherScholars, from a doctoral student to an interim dean, used kuwentuhan (Pilipinx methodology) to amplify their survivance and thrivance despite attempted exclusion, reduction, and distortion as Pilipinos by coloniality/modernity. Grounded in decolonial feminism (the view that oppressions such as sexism and racism co-constitute coloniality and that unsettling oppressions disrupts hegemony) and Pinayism (an integrated framework revaluing the labor, intellect, and nurturance of mothering through a cultural lens), the authors work coalitionally across their PA diversity to re-center ginhawa (aliveness or sense of ease and wellness). Together, they embarked on an iterative self-study process of data generation and analysis that included presenting, recording, and transcribing two panel presentations at a premier social work conference, writing reflections and hay(na)ku poems about their experiences and processes, reading and rereading the data, and meeting and discussing the data, their process, and past and current events pertinent to the content. The stories highlight how the authors are living and enlivening decoloniality, and that, in so doing, they continue a lineage of those who have resisted coloniality/modernity and promoted thrivance. Collectively, these kuwentos (stories), reflections, hay(na)ku, and their weaving together, are memory, resistance, counter-storytelling, and healing.

On the estimation of population size-A comparison of capture-recapture and multiplier-benchmark methods.

Approaches to population size estimation are of importance across a wide spectrum of disciplines, especially when census and simple random sampling are impractical. The capture-recapture method and the multiplier-benchmark method are two commonly used approaches that use data that partially capture the target population and overlap in a known way. Due to similarities in required data structures, the approaches are often used interchangeably without a critical appraisal of the underlying assumptions, especially in the two-sample case. Here, we describe the similarities and differences of the sampling mechanisms and assumptions underlying both approaches. We emphasize that the capture-recapture method assumes data sources as random samples and describes two-way inclusion histories, while in multiplier-benchmark method, one source captures a fixed sub-population, and the one-way inclusion histories are modeled. We also discuss the implications of these differences through simulation and real data to guide the choice of method in practice. A careful study of the data structures, relationships, and data generation processes is crucial for assessing the appropriateness of using these methods.

Multiple-Instance Learning from Pairwise Comparison Bags

Multiple-instance learning (MIL) is a significant weakly supervised learning problem, where the training data consists of bags containing multiple instances and bag-level labels. Most previous MIL research required fully labeled bags. However, collecting such data is challenging due to the labeling costs or privacy concerns. Fortunately, we can easily collect pairwise comparison information, indicating one bag is more likely to be positive than the other. Therefore, we investigate a novel MIL problem about learning a bag-level binary classifier only from pairwise comparison bags. To solve this problem, we display the data generation process and provide a baseline method to train an instance-level classifier based on unlabeled-unlabeled learning. To achieve better performance, we propose a convex formulation to train a bag-level classifier and give a generalization error bound. Comprehensive experiments show that both the baseline method and the convex formulation achieve satisfactory performance, while the convex formulation performs better. 1

Greedy selection of optimal location of sensors for uncertainty reduction in seismic moment tensor inversion

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (stations) from which to collect the observed data. The Shannon expected information gain is used as the objective function to search for the optimal network of sensors. A closed form for such objective is available due to the linear structure of the forward problem, as well as the Gaussian modeling of the observational errors and prior distribution. The resulting problem being inherently combinatorial, a greedy algorithm is deployed to sequentially select the sensor locations that form the best network for learning the moment tensor. Numerical results are presented to display the optimal network of sensors and how the uncertainty in the inferred seismic moment tensor contracts. The scenario of full three-dimensional velocity models or unknown earthquake source locations is treated as nuisance uncertainty, contributing to the overall uncertainty without being the focus of the inversion. This is addressed using a consensus approach over a set of realizations of the nuisance parameter. We analyzed the resulting network of stations for the moment tensor inversion under model misspecification, which reflects realistic data-generating processes.

Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity

Nonparametric two-sample testing is a classical problem in inferential statistics. While modern two-sample tests, such as the edge count test and its variants, can handle multivariate and non-Euclidean data, contemporary gargantuan datasets often exhibit heterogeneity due to the presence of latent subpopulations. Direct application of these tests, without regulating for such heterogeneity, may lead to incorrect statistical decisions. We develop a new nonparametric testing procedure that accurately detects differences between the two samples in the presence of unknown heterogeneity in the data generation process. Our framework handles this latent heterogeneity through a composite null that entertains the possibility that the two samples arise from a mixture distribution with identical component distributions but with possibly different mixing weights. In this regime, we study the asymptotic behavior of weighted edge count test statistic and show that it can be effectively recalibrated to detect arbitrary deviations from the composite null. For practical implementation we propose a Bootstrapped Weighted Edge Count test which involves a bootstrap-based calibration procedure that can be easily implemented across a wide range of heterogeneous regimes. A comprehensive simulation study and an application to detecting aberrant user behaviors in online games demonstrates the excellent non-asymptotic performance of the proposed test. Supplementary materials for this article are available online.

Effect of Differential Item Functioning on Computer Adaptive Testing Under Different Conditions.

This study provides an overview of the effect of differential item functioning (DIF) on measurement precision, test information function (TIF), and test effectiveness in computer adaptive tests (CATs). Simulated data for the study was produced and analyzed with the Rstudio. During the data generation process, item pool size, DIF type, DIF percentage, item selection method for CAT, and the test termination rules were considered changed conditions. Sample size and ability parameter distribution, Item Response Theory (IRT) model, DIF size, ability estimation method, test starting rule, and item usage frequency method regarding CAT conditions were considered fixed conditions. To examine the effect of DIF, measurement precision, TIF and test effectiveness were calculated. Results show DIF has negative effects on measurement precision, TIF, and test effectiveness. In particular, statistically significant effects of the percentage DIF items and DIF type are observed on measurement precision.

Automated Machine Learning and Asset Pricing

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can identify features of the data-generating process undetected by standard methods and rank the best-performing algorithms. Our tests use 95 years of CRSP data, from 1926 to 2021, encompassing the price history of the broad US stock market. Our findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors may be more attractive when more appropriate account is taken of the non-linear properties of the underlying assets.

A Novel Approach for Testing Fractional Cointegration in Panel Data Models with Fixed Effects

Fractional cointegration in time series data has been explored by several authors, but panel data applications have been largely neglected. A previous study of ours discovered that the Chen and Hurvich fractional cointegration test for time series was fairly robust to a moderate degree of heterogeneity across sections of the six tests considered. Therefore, this paper advances a customized version of the Chen and Hurvich methodology to detect cointegrating connections in panels with unobserved fixed effects. Specifically, we develop a test statistic that accommodates variation in the long-term cointegrating vectors and fractional cointegration parameters across observational units. The behavior of our proposed test is examined through extensive Monte Carlo experiments under various data-generating processes and circumstances. The findings reveal that our modified test performs quite well comparatively and can successfully identify fractional cointegrating relationships in panels, even in the presence of idiosyncratic disturbances unique to each cross-sectional unit. Furthermore, the proposed modified test procedure established the presence of long-run equilibrium between the exchange rate and labor wage of 36 countries’ agricultural markets.