High Leverage Points Research Articles

How to make climate policy more effective? The search for high leverage points by the multidisciplinary Dutch expert team ‘Energy System 2050’

AbstractThe Netherlands aims to have a climate‐neutral society in 2050, for which a timely climate neutrality of the energy system is crucial. This is challenging as its geographical location with deep sea harbours and abundant low‐cost natural gas have led to a relatively high energy intensity of its economy as well as vast accumulation of fossil assets in industry. The energy system is strongly intertwined with other systems, and relevant knowledge is spread across scientific disciplines including finance, innovation, geography, governance, economics, and psychology. This is why the Dutch minister for Climate and Energy asked a team of experts from across all these disciplines for advice on how to achieve a climate‐neutral energy system. This study reports on the results of a participative modelling exercise with these experts that was organized to foster a shared understanding of the complexity of the Dutch energy system. The multidisciplinary approach identifies governance, fairness, and trust as high leverage points, and we propose policies that intervene in these variables. We contribute to the literature around climate policy by exhibiting the relevance of understanding the interrelations between the disciplines, leading to recommendations for climate policies that are more effective because they acknowledge and do justice to the interrelated nature of the energy system. Although other articles have proposed similar policies, our study is different because we suggest how the structure of the system can lead to tipping dynamics, thereby providing a new logic of why these policies deserve more attention.

Using leverage points to reconsider the sociopolitical drivers of exclusion from education

This article outlines how the international push for inclusive education cannot be aligned with current education systems centred on neoliberal ideals of individualism, measurement, and competition. The way that these systems are organised means that a proportion of (usually marginalised) students are necessarily excluded. In order to meaningfully address the global education crisis, that sees millions of children and young people either out of school or unengaged with learning, this ontological misalignment must be acknowledged, and discourse and engagement around it must be promoted. Drawing from the work of Donella Meadows, I argue that ‘high leverage points’ (information flows, rules, system goals, mindsets) are crucial places to intervene in education systems. Intervention at such points, while challenging, has the potential to lead to transformational and sustainable change; change that current ‘low leverage point’ initiatives fail to produce. I conclude with a call for further discourse and research into an examination of ‘high leverage points’ within education systems, how they interact, and how the development of an associated framework would be beneficial for researchers and development partners.

A Remedial Measure of Multicollinearity in Multiple Linear Regression in the Presence of High Leverage Points

The ordinary least squares (OLS) is the widely used method in multiple linear regression model due to tradition and its optimal properties. Nonetheless, in the presence of multicollinearity, the OLS method is inefficient because the standard errors of its estimates become inflated. Many methods have been proposed to remedy this problem that include the Jackknife Ridge Regression (JAK). However, the performance of JAK is poor when multicollinearity and high leverage points (HLPs) which are outlying observations in the X- direction are present in the data. As a solution to this problem, Robust Jackknife Ridge MM (RJMM) and Robust Jackknife Ridge GM2 (RJGM2) estimators are put forward. Nevertheless, they are still not very efficient because they suffer from long computational running time, some elements of biased and do not have bounded influence property. This paper proposes a robust Jackknife ridge regression that integrates a generalized M estimator and fast improvised Gt (GM-FIMGT) estimator, in its establishment. We name this method the robust Jackknife ridge regression based on GM-FIMGT, denoted as RJFIMGT. The numerical results show that the proposed RJFIMGT method was found to be the best method as it has the least values of RMSE and bias compared to other methods in this study.

Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach

BackgroundOutliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. Spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) structure.MethodWe propose the Spatial-Θ-Iterative Procedure for Outlier Detection (Spatial-Θ-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple countries.ResultsSimulation results showed that the masking and JD (Joint Detection) indicators of our Spatial-Θ-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Θ-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling process.ConclusionsOur proposed Spatial-Θ-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative superiority even in the presence of high leverage points. By successfully identifying outliers, our method enhances the overall understanding of the data and provides valuable insights for further analysis.

Robust Hausman Pretest for Panel Data Model in the Presence of Heteroscedasticity and Influential Observations

The classical Hausman pretest (HT) is used to specified the right model between random and fixed effect panel data models. However, in the presence of heteroscedastic error variances and influential observations (IOs) in the data set, it may not correctly identify the right model. Therefore, this motivated us to proposed a new method termed Robust Hausman Test (RHTFIID) which employed residuals from weighted least square (WLS) instead of OLS in the construction of heteroscedasticity consistent covariance matrix (HCCM) estimator. The weighting method is based on an efficient High Leverage Points (HLPs) detection method called Fast Improvised Influential Distance (FIID) which down weight only vertical outliers and bad HLPs. The good HLPs were allowed in the estimation as they might contribute to the precision of the estimate. The result indicates that the new proposed RHTFIID outperformed the existing classical Hausman pretest by identifying the right model with and without heteroscedasticity and influential observations.

Improved Least Squares Support Vector Machine Model Based on Grey Wolf Optimizer Algorithm for Predicting CO2–Crude Oil Minimum Miscibility Pressure

The minimum miscibility pressure (MMP) is an important reference parameter in the study of CO2 oil drive systems. In response to the problems of time‐consuming and costly prediction of MMP by conventional experimental methods, an improved least squares support vector machine (LSSVM) model based on grey wolf optimizer (GWO) algorithm is proposed to predict the CO2–crude oil MMP. Based on Pearson correlation analysis, reservoir temperature, C5+ molecular weight, intermediate component mole fraction, and volatile component mole fraction are selected as independent variables of the model, and MMP is the dependent variable. A total of 51 MMP experimental data are collected, of which 35 are used to fine‐tune the model's parameters and 16 are used to verify the model's reliability. The high leverage point method is used to detect anomalies in all experimental data to check the reliability of the model, and the abnormality of only one piece of experimental data is identified. Finally, a comparison of the model with other intelligent models is found. The absolute relative deviation of the GWO‐LSSVM model is 2.24% for the training data and 3.48% for the test data, which provides high adaptability and accuracy.

Multidimensional school features associated with physical activity among youth at risk of obesity: an exploratory principal component and generalized estimating equation analysis

BackgroundSchools may be high-leverage points for the promotion of physical activity (PA), yet little is known about school built and social environments among youth at high risk of obesity.PurposeTo characterise school built and social environments that may be salient for PA and to examine associations between school PA environments and PA in youth at risk of obesity.MethodsData from youth attending 206 schools (314 youth in 2005–2008, and 129 youth in 2008–2010) within the QUALITY cohort study, a longitudinal investigation of youth at high risk of obesity were used. Features of schools, based on built, policy/programming and social environments were identified using principal components (PC) analysis. Gender-stratified generalized estimating equation (GEE) models were used to explore associations between school features and accelerometer measured mean counts per minute (MCPM), mean daily moderate-to-vigorous physical activity (MVPA) and the odds of meeting MVPA guidelines cross-sectionally and prospectively using 90% confidence intervals.ResultsNine PCs were identified. Associations were observed between PA and 7 of the 9 PCs. The social environment seemed to be particularly important. Social Norms to Promote PA was associated with an increase in girls’ baseline MCPM and MVPA. High Willingness to Promote PA was associated with boys’ MCPM, MVPA, and odds of meeting MVPA guidelines, at both baseline and follow-up.ConclusionSchool built and social contexts may be associated with PA cross-sectionally and over time. Further studies are necessary to confirm the direction and magnitude of effects and to establish their relevance to school-based health promotion efforts.

The Dynamics of Sustainable Material Selection for Green-Certified Projects

Material selection is a challenging process in which several parameters should be considered for green-certified projects. Hence, this study investigates the dynamics of sustainable material selection (SMS) across the project life cycle using system thinking. A dynamic model was developed based on the Leadership in Energy and Environmental Design where relevant environmental categories and credits were represented, and their dependency coefficients (DCs) were computed. This pinpointed the effect of SMS on the point-accrual pattern across project phases, showing that the high leverage points were demonstrated for the operation phase. The result showed that SMS extended beyond the efficient use of materials, which indicated a DC of 100% in the Materials and Resources category to affect other sustainable categories, i.e., the indoor environmental quality, energy efficiency, and sustainability of the project site comprised 68.8%, 57.6%, and 50% of DCs, respectively. Thus, optimal SMS could earn the project 48% of the total possible points, putting it in the ‘Certified’ level. Accordingly, the proposed model can be used to evaluate the level of certification as well as the building performance therein.

Heteroscedasticity of unknown form: a comparison of five heteroscedasticity-consistent covariance matrix (hccm) estimators

Regression model applications frequently involve violations of the homoscedasticity assumption and the presence of high leverage points (HLPs). The Heteroscedasticity-Consistent Covariance Matrix (HCCM) estimator's impact in the presence of heteroscedasticity of an unknown form was investigated in this study. The effectiveness of five variations of HCCM namely White’s estimator (HC0), White-Hinkley (HC1), Mackinnon White (HC2), Davison –Mackinnon (HC3), and Cribari-Neto (HC4) were accessed to identify the optimal Heteroscedasticity-Consistent Covariance Matrix (HCCM) estimator. In the study a simulated dataset was analysed using the Econometric View Software Version 12. The Breush-Pagan Godfery’s test for heteroscedasticity was applied and p-value of 0.0123 was obtained indicating presence of heteroscedasticity in the model. Applying the HCCM estimators and comparing the Heteroskedasticity-consistent standard errors estimates showed that HCO had 124.104, HC1 had 1189.222, HC2 had 1175.282, HC3 had 1106.94 and HC4 had 1140.707. These results reveal that HC3 and HC4 produced smaller errors compared to HC0, HC1 and HC2. The study hence comes to the conclusion that when doing inferential tests using OLS regression, the use of HCSE estimator increases the researcher's confidence in the accuracy and potency of those tests. This study therefore suggests that to ensure that findings are not affected by heteroscedasticity; researchers should use HCCM estimator but precisely HC3 and HC4, as the presented better results in comparison to others.

Accounting for outliers in optimal subsampling methods

Nowadays, in many different fields, massive data are available and for several reasons, it might be convenient to analyze just a subset of the data. The application of the D-optimality criterion can be helpful to optimally select a subsample of observations. However, it is well known that D-optimal support points lie on the boundary of the design space and if they go hand in hand with extreme response values, they can have a severe influence on the estimated linear model (leverage points with high influence). To overcome this problem, firstly, we propose a non-informative “exchange” procedure that enables us to select a “nearly” D-optimal subset of observations without high leverage values. Then, we provide an informative version of this exchange procedure, where besides high leverage points also the outliers in the responses (that are not necessarily associated to high leverage points) are avoided. This is possible because, unlike other design situations, in subsampling from big datasets the response values may be available. Finally, both the non-informative and informative selection procedures are adapted to I-optimality, with the goal of getting accurate predictions.

Robust and smoothing variable selection for quantile regression models with longitudinal data

In this paper, we propose a penalized weighted quantile estimating equations (PWQEEs) method to obtain sparse, robust and efficient estimators for the quantile regression with longitudinal data. The PWQEE incorporates the within correlations in the longitudinal data by Gaussian copulas and can also down-weight the high leverage points in covariates to achieve double-robustness to both the non-normal distributed errors and the contaminated covariates. To overcome the obstacles of discontinuity of the PWQEE and nonconvex optimization, a local distribution smoothing method and the minimization–maximization algorithm are proposed. The asymptotic properties of the proposed method are also proved. Furthermore, finite sample performance of the PWQEE is illustrated by simulation studies and a real-data example.

Out of Adversity Comes Opportunity: Smart-Colpo National Program for the Elimination of Carcinoma Cervix in a Post–COVID-19 World

Background Carcinoma cervix is one of the leading causes of death among women worldwide. The World Health Organization has put forth the 90-70-90 global strategy for the elimination of cervical cancer as a public health problem. It calls for 70% women to be screened at least once in their lifetime. However, this rate is as low as 1.9% for India and even lower for many other countries, making the target insurmountable, especially in resource-constrained settings. The COVID-19 pandemic made this even more challenging. Objective This study aimed to identify bottlenecks and high leverage points and propose a technology-driven, national-level program for improving the screening of carcinoma cervix. Methods Detailed process mapping was done to identify potential bottlenecks. A counterfactual approach was used to identify high leverage points for impact using “What if” scenarios. These findings were used to build program theory–based logic models to propose a national-level program for carcinoma cervix prevention. Results Availability, accessibility, affordability, skewed distribution of infrastructure, cost implications, and limited specialist workforce were identified as bottlenecks. The COVID-19 pandemic put a strain on existing resources and worsened the situation. The existing network of primary health care workers, changes in health-seeking behavior due to COVID-19 with the increasing role of tele-health, conducive political milieu with initiatives such as Digital India Mission, and a booming start-up ecosystem were identified as high leverage points through the counterfactual approach. Using these findings, a national program was designed with program theory–based logic modeling. Conclusions The journey from <2% of women screened at least once in their lifetime all the way to 70% would need a drastic increase in funding and resource allocation, which is unlikely considering the current conditions. COVID-19 has not only been an adversity but also opened new thinking and opportunities. Artificial intelligence–driven, cost-effective, easy-to-use, and widely acceptable solutions such as “Smart-Colpo” can be a game changer to achieve the World Health Organization targets. Conflicts of Interest None declared.

Roadmap for outlier detection in univariate linear calibration in analytical chemistry: Tutorial review

AbstractAssessment of the adequacy of a proposed linear calibration curve is necessarily subjective in chemical analysis. If the outlier points in calibration are not identified and discarded, the constructed model will not have much validity and does not warrant the accuracy and precision of prediction step. Recognizing of influential points, outlier data, and discarding them is one of the steps in data processing that has been considered in various sciences. The outlier points can arise from (I) bad design of calibration set and (II) gross error in doing experiments. Therefore, we aimed to extract a map that recognizes the following issues: (A) the existence of data with high regression coverage that is far from the rest and will strongly affect the accuracy of the calibration equation, high leverage points; (B) large error in the experimental process: The recorded signal does not match the desired concentration; and (C) points with a concentration lower than the limit of quantification, which is calculated by considering the standard error of regression instead of the standard deviation of blank.The efficiency of the proposed roadmap will be reviewed, and this will give a new perspective on the calibration equation to avoid common mistakes in analytical chemistry. To achieve the above goal, visual and statistically significant tests will be used and all tests will be performed in a simple Microsoft Excel environment.

Robust Variable Selection and Regularization in Quantile Regression Based on Adaptive-LASSO and Adaptive E-NET

Robust Variable Selection and Regularization in Quantile Regression Based on Adaptive-LASSO and Adaptive E-NET

Leverage and Cook distance in regression with geostatistical data: methodology, simulation, and applications related to geographical information

Regression is often conducted assuming independent model errors. The detection of atypical values in regression (leverage and influential points) assumes independent errors. However, such independence could be unrealistic in geostatistics. In this article, we propose a methodology based on least squares and geostatistics to identify such values in spatial regression. Our procedure uses the hat matrix to detect leverage points. A modified Cook distance is employed to confirm whether these points are influential. The methodology is evaluated with stationary and non-stationary geostatistical data. We apply this methodology to real georeferenced data related to depth, dissolved oxygen, and temperature. First, an autoregressive model is fitted to depth data. Second, a regression between oxygen and temperature is estimated. In both models, spatial correlation is assumed to determine the parameters, leverage, and influential observations. Our methodology can be used in regression with geographical information to avoid misinterpreted results. Not considering this information may under- or over-estimate geographical indicators, such as the mean depth, which can affect the circulation of water masses or dissolved oxygen variability. Our results reveal that including spatial dependence to identify high leverage points is relevant and must be considered in any geostatistical analysis.

The Sports-Based Holistic Development Model: The General Public’s Transformation by Having a Meaningful Story Through Sport

Despite an increasing expectation of sports’ potential as contributors to society, not many studies have longitudinally examined their social, economic, and cultural outcomes. This study proposes an integrated conceptual model to enhance the holistic understanding of sports-based development initiative. Applying this model to the “Vision 2030: Live Better through Sport” road map in Singapore, the longitudinal study empirically shows that the public’s participation in grassroots sports activities is a fundamental engine of sports-based holistic development for multi-dimensional outcomes among grassroots, elite, and mega sports dynamics. Also, this study presents a general sport system model to describe the mechanism for social impacts and longitudinal outcomes in the sports-based development initiatives, which provides strategic guideline for project monitoring and evaluation for the actual outcomes. Importantly, the findings offer policy insights into the public’s transformation by having a meaningful story through sport as a high leverage point in the sport-based holistic development system.

A Systems Thinking Approach towards Single-Use Plastics Reduction in Food Delivery Business in Thailand

Sustainable consumption and production (SCP) is largely influenced by dynamics in the market system. Hence, this study adopts the systems thinking approach as a tool to understand the non-linearity and complexity of sustainable practices. The food delivery business has accelerated the plastic waste problem, especially during COVID-19 where restaurant dine-in was limited. This study aims to identify high leverage points, which contribute to system intervention strategies to improve Thailand’s single-use plastics (SUP) waste situation caused by the food delivery sector. Semi-structured interviews were conducted with relevant stakeholders (n = 14). A qualitative system dynamics model analysis and thematic analysis suggest that the key leverage points include (1) benefit alignment among all stakeholders, (2) cost minimization and profit maximization, (3) laws and regulations, (4) postconsumption waste management systems, and (5) research and development. In addition, to stimulate policy initiatives, this study suggests that system intervention strategies should include behavioral instruments (setting default and eco-labelling), market-based instruments (green-packaging procurement and subsidies), and system and infrastructure provision (waste management systems and deposit return models). Partnerships, research and development, and laws and regulations are proposed as supporting measures.

Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse data

ABSTRACT The main aim of this paper is to propose a novel method (RMD-MRCD-PCA) of identification of High Leverage Points (HLPs) in high-dimensional sparse data. It is to address the weakness of the Robust Mahalanobis Distance (RMD) method which is based on the Minimum Regularized Covariance Determinant (RMD-MRCD), which indicates a decrease in its performance as the number of independent variables (p) increases. The RMD-MRCD-PCA is developed by incorporating the Principal Component Analysis (PCA) in the MRCD algorithm whereby this robust approach shrinks the covariance matrix to make it invertible and thus, can be employed to compute the RMD for high dimensional data. A simulation study and two real data sets are used to illustrate the merit of our proposed method compared to the RMD-MRCD and Robust PCA (ROBPCA) methods. Findings show that the performance of the RMD-MRCD is similar to the performance of the RMD-MRCD-PCA for p close to 200. However, its performance tends to decrease when the number of p is more than 200 and worsens at p equals 700 and larger. On the other hand, the ROBPCA is not effective for less than 20% contamination as it suffers from serious swamping problems.

COMPARISONS BETWEEN ROBUST REGRESSION APPROACHES IN THE PRESENCE OF OUTLIERS AND HIGH LEVERAGE POINTS

The study aimed to compare a few robust approaches in linear regression in the presence of outlier and high leverage points. Ordinary least square (OLS) estimation of parameters is the most basic approach practiced widely in regression analysis. However, some fundamental assumptions must be fulfilled to provide good parameter estimates for the OLS estimation. The error term in the regression model must be identically and independently comes from a Normal distribution. The failure to fulfill the assumptions will result in a poor estimation of parameters. The violation of assumptions may occur due to the presence of unusual observations (which is known as outliers or high leverage points. Even in the case of only one single extreme value appearing in the set of data, the result of the OLS estimation will be affected. The parameter estimates may become bias and unreliable if the data contains outlier or high leverage point. In order to solve the consequences due to unusual observations, robust regression is suggested to help in reducing the effect of unusual observation to the result of estimation. There are four types of robust regression estimations practiced in this paper: M estimation, LTS estimation, S estimation, and MM estimation, respectively. Comparisons of the result among different types of robust estimator and the classical least square estimator have been carried out. M estimation works well when the data is only contaminated in response variable. But in the case of presence of high leverage point, M estimation cannot perform well.

Adaptive Governance in Fire-Prone Landscapes

Wildfire is an influential driver of social and ecological change that calls for adaptive governance on federal lands. We identify supporting and limiting elements of adaptive governance in the context of wildfire management on the Deschutes National Forest (DNF). We use key findings to propose actions and leverage points to increase the US Forest Service’s ability to engage in adaptive governance. Our research suggests that key supporting elements of adaptive governance within the Forest Service are generally found at the local scale. These supportive elements can be tapped to shed national-scale rigidity traps that lock the Forest Service into actions that have had problematic outcomes. The governance framework we used could be helpful in other research that aims to explore supporting and limiting elements of adaptive governance and identify high leverage points for change.