Parsimonious Method Research Articles

Inequality in measuring scholarly success: Variation in the h-index within and between disciplines.

Scholars and university administrators have a vested interest in building equitable valuation systems of academic work for both practical (e.g., resource distribution) and more lofty purposes (e.g., what constitutes "good" research). Well-established inequalities in science pose a difficult challenge to those interested in constructing a parsimonious and fair method for valuation as stratification occurs within academic disciplines, but also between them. The h-index, a popular research metric, has been formally used as one such method of valuation. In this article, we use the case of the h-index to examine how the distribution of research metrics reveal within and between discipline inequalities. Using bibliometric data from 1960-2019 on over 50,000 high performing scientists-the top 2% most frequently cited authors-across 174 disciplines, we construct random effects within-between models predicting the h-index. Results suggest significant within-discipline variation in several forms, specifically sole-authorship and female penalties. Results also show that a sole authorship penalty plays a significant role in well-known between-discipline variation. Field-specific models emphasize the "apples-to-oranges," or incommensurable, property of cross-discipline comparison with significant heterogeneity in sole-authorship and female penalties within fields. In conclusion, we recommend continued caution when using the h-index or similar metrics for valuation purposes and the prioritization of substantive valuations from disciplinary experts.

Examining Retention Methods in Factor Analysis: A Comparison of Polychoric and Pearson Correlations for Categorical and Continuous Data.

In the last two decade, it has become clear that retention methods must utilize polychoric correlation instead of Pearson correlation to eliminate drawbacks such as underestimation of the magnitude of the relationship between latent variables that result in spurious findings (Bernstein & Teng, 1989). In the present study, the literature review will be examined and compared using Monte Carlo simulation to determine the most parsimonious method of retention for categorical data. With continuous variables, the majority of researchers still implement Cattell’s scree test (Henson & Roberts, 2006) and Kaiser-Guttman-1 rule (Velicer et al., 2000), because these procedures are the default in popular statistical packages, such as SPSS and SAS. The present study will examine two of the most accurate methods: MAP (Minimum Average Partial) and PA (Parallel Analysis) along with Very Simple Structure (VSS) with categorical variables.

Total capacity and LOS estimation of innovative and conventional roundabouts through macroscopic fundamental diagrams

Traffic congestion remains a significant problem, especially in urban areas. To avoid congestion phenomena, several measures can be adopted, including the construction of roundabouts and the control of the measures of effectiveness (MOEs) at intersections. This article compares different types of roundabouts by estimating the deterministic fundamental diagram (MFD) and using microscopic traffic simulations. The deduction of MFD for different roundabout types (single and double-lane, flower and turbo-roundabout) is based on reasonably simplified traffic and boundary conditions. MFDs are estimated through several OD traffic matrices, considering as outputs the values of the macroscopic traffic variable deduced in time intervals of 5 min and 1 h. The findings from this research, in terms of total capacity, travel times, and level of service (LOS) estimation, may be useful for traffic and highway engineers in many realistic applications to partially face congestion phenomena in urban areas. MFDs allow us to determine the best roundabout type based on the traffic demand and represent a parsimonious method for monitoring and controlling traditional and unconventional or smart roundabouts, even in the presence of connected and automated vehicles (CAVs).

Subphenotypes in Acute Respiratory Distress Syndrome: Universal Steps Toward Treatable Traits.

Patients with acute respiratory distress syndrome (ARDS) have severe respiratory impairment requiring mechanical ventilation resulting in high mortality. Despite extensive research, no effective pharmacological interventions have been identified in unselected ARDS, which has been attributed to the considerable heterogeneity. The identification of more homogeneous subgroups through phenotyping has provided a novel method to improve our pathophysiological understanding, trial design, and, most importantly, patient care through targeted interventions. The objective of this article is to outline a structured, stepwise approach toward identifying and classifying heterogeneity within ARDS and subsequently derive, validate, and integrate targeted treatment options. We present a 6-step roadmap toward the identification of effective phenotype-targeted treatments: development of distinct and reproducible subphenotypes, derivation of a possible parsimonious bedside classification method, identification of possible interventions, prospective validation of subphenotype classification, testing of subphenotype-targeted intervention prospectively in randomized clinical trial (RCT), and finally implementation of subphenotype classification and intervention in guidelines and clinical practice. Based on this framework, the current literature was reviewed. Respiratory physiology, lung morphology, and systemic inflammatory biology subphenotypes were identified. Currently, lung morphology and systemic inflammatory biology subphenotypes are being tested prospectively in RCTs.

A parsimonious Bayesian predictive model for forecasting new reported cases of West Nile disease

Upon researching predictive models related to West Nile virus disease, it is discovered that there are numerous parameters and extensive information in most models, thus contributing to unnecessary complexity. Another challenge frequently encountered is the lead time, which refers to the period for which predictions are made and often is too short. This paper addresses these issues by introducing a parsimonious method based on ICC curves, offering a logistic distribution model derived from the vector-borne SEIR model. Unlike existing models relying on diverse environmental data, our approach exclusively utilizes historical and present infected human cases (number of new cases). With a year-long lead time, the predictions extend throughout the 12 months, gaining precision as new data emerge. Theoretical conditions are derived to minimize Bayesian loss, enhancing predictive precision. We construct a Bayesian forecasting probability density function using carefully selected prior distributions. Applying these functions, we predict month-specific infections nationwide, rigorously evaluating accuracy with probabilistic metrics. Additionally, HPD credible intervals at 90%, 95%, and 99% levels is performed. Precision assessment is conducted for HPD intervals, measuring the proportion of intervals that does not include actual reported cases for 2020–2022.

A typological approach to language change in contact situations

Abstract Language contact phenomena have increasingly been researched from different historical linguistic, sociolinguistic and areal-typological perspectives. However, since most of this research is based on case studies, an assessment of contact phenomena from a worldwide comparative perspective has been missing in the literature. In this article, we draw inspiration from historical linguistics and language typology to present a new typological approach for evaluating evidence that given linguistic domains have been affected by language contact. This method has three parts: (1) a new approach to sampling, (2) the analysis of typological data, and (3) making probabilistic inferences about language contact. We argue that this is a parsimonious method for evaluating contact effects that can serve as a starting point for the further development of typological approaches to language contact.

Comparative analysis of anticholinergic burden scales to explain iatrogenic cognitive impairment in schizophrenia: results from the multicenter FACE-SZ cohort.

The anticholinergic properties of medications are associated with poorer cognitive performance in schizophrenia. Numerous scales have been developed to assess anticholinergic burden and yet, there is no consensus indicating which anticholinergic burden scale is more relevant for patients with schizophrenia. We aimed to identify valid scales for estimating the risk of iatrogenic cognitive impairment in schizophrenia. We identified 27 scales in a literature review. The responses to neuropsychological tests of 839 individuals with schizophrenia or schizoaffective disorder in the FACE-SZ database were collected between 2010 and 2021. We estimated the association between objective global cognitive performance and the 27 scales, the number of psychotropic drugs, and chlorpromazine and lorazepam equivalents in bivariable regressions in a cross-sectional design. We then adjusted the bivariable models with covariates: the predictors significantly associated with cognitive performance in multiple linear regressions were considered to have good concurrent validity to assess cognitive performance. Eight scales, the number of psychotropic drugs, and drug equivalents were significantly associated with cognitive impairment. The number of psychotropic drugs, the most convenient predictor to compute, was associated with worse executive function (Standardized β = -0.12, p = .004) and reasoning (Standardized β = -0.08, p = .037). Anticholinergic burden, the number of psychotropic drugs, and drug equivalents were weakly associated with cognition, thus suggesting that cognitive impairment in schizophrenia and schizoaffective disorder is explained by factors other than medication. The number of psychotropic drugs was the most parsimonious method to assess the risk of iatrogenic cognitive impairment.

Consumer-company identification: a review and empirical contribution

PurposeFor decades, consumer identification and motivation, either alone or jointly, have been essential constructs for behavioral researchers. The resultant output is significant in terms of both quality and quantity. However, at a deeper level, a lack of conceptual clarity in the relationship between these constructs has led to theoretical and practical irregularities, which this study aims to address.Design/methodology/approachAn online questionnaire was distributed to sport consumers aged over 18 participating in an online panel, prompted 293 completed responses. Structural equations modeling was used to examine the data.FindingsFindings show that identification mediates the effects of intrinsic and extrinsic motivation on sport supporters’ loyalty and explain 90% of the variance in that construct. In addition, identification mediates the adverse effects of extrinsic motivation on loyalty and strengthens loyalty when levels of satisfaction decline.Originality/valueThis study extends previous work by providing a theoretical perspective that clarifies the relationship between motivation and consumer identification; deepens theory by empirically observing the relationship at different levels of consumer satisfaction; and presents a parsimonious, valid and reliable method that managers can leverage to strengthen sport supporters’ loyalty.

Endogeneity in stochastic frontier models with 'wrong' skewness: copula approach without external instruments

Stochastic frontier models commonly assume positive skewness for the inefficiency term. However, when this assumption is violated, efficiency scores converge to unity. The potential endogeneity of model regressors introduces another empirical challenge, impeding the identification of causal relationships. This paper tackles these issues by employing an instrument-free estimation method that extends joint estimation through copulas to handle endogenous regressors and skewness issues. The method relies on the Gaussian copula function to capture dependence between endogenous regressors and composite errors with a simultaneous consideration of positively or negatively skewed inefficiency. Model parameters are estimated through maximum likelihood, and Monte Carlo simulations are employed to evaluate the performance of the proposed estimation procedures in finite samples. This research contributes to the stochastic frontier models and production economics literature by presenting a flexible and parsimonious method capable of addressing wrong skewness of inefficiency and endogenous regressors simultaneously. The applicability of the method is demonstrated through an empirical example.

Parsimonious BWM (BWM-P) for prioritizing ad appeals in the Metaverse world

Metaverse has made sense for organizations regarding new opportunities it is likely to offer, and studies on Metaverse-related subjects in different areas have increased incredibly. Advertising is an area where Metaverse-related studies are few. For the first time, this paper prioritizes Metaverse ad appeals and performs the Parsimonious Best-worst method (BWM-P) for this purpose. BWM-P, an improved version of the BWM method, allows consistent results to be achieved quickly by reducing the number of pairwise comparisons. Regarding the findings, Metaverse experience is the paramount Metaverse ad appeal, followed by product familiarity and NFT of advertisement, respectively. Further, standardization and Metaverse bonuses are the other significant Metaverse ad appeals. The present research contributes to existing knowledge by analyzing ad appeals to ensure the effectiveness of advertising strategies in the Metaverse world.

An AHP parsimonious based approach to handle manufacturing errors in production processes

This paper introduces a new hybrid tool to classify manufacturing errors in production processes. Our tool is a hybrid procedure based on the combination of the parsimonious AHP method with a clustering method. Parsimonious AHP permits to use a much smaller number of pairwise comparisons with respect to classical-AHP. At the same time, the use of a clustering method, allows for the assignment of the alternatives on the basis of their closeness to each other and not on the basis of some reference profiles as happens in several other methods. Furthermore, some veto systems help the decision maker to better define how the errors belong to different classes in a participative way. The approach has been tested in a largest automotive plants in Italy. This approach has provided the company with a participative and robust theoretically funded tool that supports the understanding of the seriousness of the manufacturing errors. From a methodological point of view, the hybrid tools, for the first time, combine the newly developed parsimonious AHP with a clustering method. From a practical point of view, this paper has introduced a new tool to classify and improve the production quality of the processes to an extremely competitive sector.

Meaning and prediction of 'excess mortality': a comparison of Covid-19 and pre-Covid-19 mortality data in 31 Eurostat countries from 1965 to 2021.

Determining 'excess mortality' makes it possible to compare the burden of disasters between countries and over time, and thus also to evaluate the success of mitigation measures. However, the debate on coronavirus disease 2019 (Covid-19) has exposed that calculations of excess mortalities vary considerably depending on the method and its specification. Moreover, it is often unclear what exactly is meant by 'excess mortality'. We define excess mortality as the excess over the number of deaths that would have been expected counter-factually, that is without the catastrophic event in question. Based on this definition, we use a very parsimonious calculation method, namely the linear extrapolation of death figures from previous years to determine the excess mortality during the Covid-19 pandemic. But unlike most other literature on this topic, we first evaluated and optimized the specification of our method using a larger historical data set in order to identify and minimize estimation errors and biases. The result shows that excess mortality rates in the literature are often inflated. Moreover, they would have exhibited considerable excess mortalities in the period before Covid-19, if this value had already been of public interest at that time. Three conclusions can be drawn from this study and its findings: (i) All calculation methods for current figures should first be evaluated against past figures. (ii) To avoid alarm fatigue, thresholds should be introduced which would differentiate between 'usual fluctuations' and 'remarkable excess'. (iii) Statistical offices could provide more realistic estimates.

The Descent–Ascent Algorithm for DC Programming

We introduce a bundle method for the unconstrained minimization of nonsmooth difference-of-convex (DC) functions, and it is based on the calculation of a special type of descent direction called descent–ascent direction. The algorithm only requires evaluations of the minuend component function at each iterate, and it can be considered as a parsimonious bundle method as accumulation of information takes place only in case the descent–ascent direction does not provide a sufficient decrease. No line search is performed, and proximity control is pursued independent of whether the decrease in the objective function is achieved. Termination of the algorithm at a point satisfying a weak criticality condition is proved, and numerical results on a set of benchmark DC problems are reported. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms – Continuous. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0142 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0142 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Examining the Water Scarcity Vulnerability in US River Basins Due To Changing Climate

AbstractAccurate assessment of changes in water availability with changing climate is vital for effective mitigation and adaptation. In this research, we employ a parsimonious Budyko curve method to evaluate changes in water availability under low‐ (SSP126) and high‐emission (SSP585) scenarios for 331 river basins in the contiguous United States. We also assess the relative role of changes in precipitation (∆P) and potential evapotranspiration (∆PET) with changing climate on the increase in water availability vulnerability. Results highlight that around 43% (28%) of basins are projected to experience increased vulnerability to changing climate in high‐emission (low‐emission) scenarios. Sub‐humid basins are most often impacted, while arid and semi‐arid basins exhibit lower sensitivity to changes. Intriguingly, ∆PET emerges as the dominant control on vulnerability, surpassing ∆P, particularly under SSP585 scenario. The analysis prompts water managers to focus on long‐term mitigation planning and scientists to further constraint climate and water budget forecasts in affected basins.

A parsimonious approach to predict regions affected by sewer-borne contaminants in urban aquifers

Leaky urban drainage networks (UDNs) exfiltrating wastewater can contaminate aquifers. Detailed knowledge on spatiotemporal distributions of water-dissolved, sewer-borne contaminants in groundwater is essential to protect urban aquifers and to optimize monitoring systems. We evaluated the effect of UDN layouts on the spreading of sewer-borne contaminants in groundwater using a parsimonious approach. Due to the UDN’s long-term leakage behavior and the existence of non-degradable sewer-borne contaminants (equivalent to a conservative and constant contaminant source), we employed a concept of horizontal line sources to mimic the UDN layout. This does not require the consideration of bio-degradation processes or temporal delay and effectively bypasses the vadose zone, thus reducing computational requirements associated with a full simulation of leakages. We used a set of synthetic leakage scenarios which were generated using fractals and are based on a real-world UDN layout. We investigated the effects of typical leakage rates, varying groundwater flow directions, and UDN’s layouts on the shape of the contaminant plume, disregarding the resulted concentration. Leakage rates showed minimal effects on the total covered plume area, whereas 89% of the variance of the plume’s geometry is explained by both the UDN’s layout (e.g., length and level of complexity) and groundwater flow direction. We demonstrated the potential of applying this approach to identify possible locations of groundwater observation wells using a real UDN layout. This straightforward and parsimonious method can serve as an initial step to strategically identify optimal monitoring systems locations within urban aquifers, and to improve sewer asset management at city scale.

A parsimonious methodological framework for short-term forecasting of groundwater levels

Groundwater plays a significant role as a strategic resource in reducing the impact of droughts. In spite of its importance, there are still many groundwater bodies in which there is not enough monitoring data to define classic distributed mathematical models to forecast future potential levels. The main aim of this study is to propose and evaluate a novel parsimonious integrated method for the short-term forecasting of groundwater levels. It has low requirements in term of data, and it is operational and relatively easy to apply. It uses geostatistics, optimal meteorological exogenous variables and artificial neural networks. We have illustrated our method in the aquifer “Campo de Montiel” (Spain). The analysis of optimal exogenous variables revealed that, in general, the wells with stronger correlations with precipitation are located closer to the central part of the aquifer. NAR, which does not consider secondary information, is the best approach for 25.5 % of the cases and is associated with well locations with lower R2 between groundwater levels and precipitation. Amongst the approaches with exogenous variables, the ones that use effective precipitation have been selected more times as the best experiments. NARX and Elman using effective precipitation had the best approaches with 21.6 % and 29.4 % of the cases respectively. For the selected approaches, we obtained a mean RMSE of 1.14 m in the test and 0.76, 0.92, 0.92, 0.87, 0.90, and 1.05 m for the forecasting test for months 1 to 6 respectively for the 51 wells, but the accuracy of the results can vary depending on the well. The interquartile range of the RMSE is around 2 m for the test and forecasting test. The uncertainty of the forecasting is also considered by generating multiple groundwater level series.

Elastic and anelastic adjoint tomography with and full Hessian kernels

SUMMARYThe elastic and anelastic structures of the Earth offer fundamental constraints for understanding its physical and chemical properties. Deciphering small variations in the velocity and amplitude of seismic waves can be challenging. Advanced approaches such as full-waveform inversion (FWI) can be useful. We rewrite the anelastic Fréchet kernel expression of Fichtner & van Driel using the displacement–stress formulation. We then derive the full Hessian kernel expression for viscoelastic properties. In these formulations, the anelastic Fréchet kernels are computed by the forward strain and a shift of the adjoint strain. This is complementary to the quality factor Q (i.e., inverse attenuation) Fréchet kernel expressions of Fichtner & van Driel that are explicit for the velocity–stress formulation. To reduce disk space and I/O requirements for computing the full Hessian kernels, the elastic full Hessian kernels are computed on the fly, while the full Hessian kernels for Q are computed by a combination of the on-the-fly approach with the parsimonious storage method. Applications of the Fréchet and full Hessian kernels for adjoint tomography are presented for two synthetic 2-D models, including an idealized model with rectangular anomalies and a model that approximates a subduction zone, and one synthetic 3-D model with an idealized geometry. The calculation of the full Hessian kernel approximately doubles the computationally cost per iteration of the inversion; however, the reduced number of iterations and fewer frequency stages required to achieve the same level of convergence make it overall computationally less expensive than the classical Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) FWI for the 2-D elastic tested models. We find that the use of full Hessian kernels provides comparable results to the L-BFGS inversion using the improved anelastic Fréchet kernels for the 2-D anelastic models tested for the frequency stage up to 0.5 Hz. Given the computational expense of the Q full Hessian kernel calculation, it is not advantageous to use it in Q inversions at this time until further improvements are made. For the 3-D elastic inversion of the tested model, the full Hessian kernel provides similar image quality to the L-BFGS inversion for the frequency stage up to 0.1 Hz. We observe an improved convergence rate for the full Hessian kernel inversion in comparison to L-BFGS at a higher frequency stage, 0.1–0.2 Hz, and we speculate that at higher frequency stages the use of full Hessian kernels may be more computationally advantageous than the classical L-BFGS for the tested models. Finally, we perform 3-D elastic and Q L-BFGS inversions simultaneously using the rederived Q kernels, which can reduce the computational cost of the inversion by about 1/3 when compared to the classical anelastic adjoint tomography using the additionally defined adjoint source. The recovered Q model is smeared when compared to the recovered elastic model at the investigation frequencies up to 0.5 Hz. Q inversion remains challenging and requires further work. The 2-D and 3-D full Hessian kernels may be used for other purposes for instance resolution analysis in addition to the inversions.

Parsimonious methodology for synthesis of silver and copper functionalized cellulose.

The online version contains supplementary material available at 10.1007/s10570-023-05099-7.

Stock Vulnerability and Resilience

The authors propose a parsimonious yet flexible statistical method for predicting the relative vulnerability or resilience of individual stocks to market drawdowns. The authors’ approach compares a stock’s unique circumstances—as reflected in popular factor attributes—to the circumstances of stocks that have proven vulnerable or resilient to previous market drawdowns. Unlike other approaches, the authors’ method allows the influence of each factor attribute to vary across stocks in a nonlinear, conditional way. The authors test their explicit method for predicting stock vulnerability and resilience out of sample using the five largest market drawdowns since the global financial crisis. The nonlinear composite scores the authors derive are reliably better predictors of cross-sectional return than any of the individual factor attributes or an ex post linear combination of factor attributes.

Stochastic Watershed Model Ensembles for Long‐Range Planning: Verification and Validation

AbstractDeterministic watershed models (DWMs) are used in nearly all hydrologic planning, design, and management activities, yet they cannot generate streamflow ensembles needed for hydrologic risk management (HRM). The stochastic component of DWMs is often ignored in practice, leading to a systematic bias in extreme events. Since traditional stochastic streamflow models used in HRM struggle to account for anthropogenic change, there is a need to convert DWMs into stochastic watershed models (SWMs) to generate ensembles for use in HRM. A DWM can be converted to an SWM using a post‐processing (pp) approach to add error to the DWM predictions. Many pp methods advanced in the area of flood forecasting are useful in HRM and for correcting extreme event biases. Selecting a suitable error model for pp is challenging due to nonnormality, skewness, heteroscedasticity, and autocorrelation. We develop a parsimonious pp method based on an autoregressive (AR) model of the logarithm of the ratio of the observations and simulations, which leads to AR model residuals that are approximately symmetric and independent. We document the value of pp for improving flood and low flow frequency analysis and we reintroduce the concepts of verification and validation of stochastic streamflow ensembles to ensure that the SWM can reproduce both statistics it was and was not designed to reproduce, respectively. These concepts are illustrated on a Massachusetts basin using the USGS Precipitation Runoff Modeling System, with an additional analysis indicating the approach may be applicable to 1,225 other sites across the United States.