Quantile Regression Research Articles

A Novel Approach to Enhancing the Accuracy of Prediction in Ship Fuel Consumption

Ship fuel consumption plays a crucial role not only in understanding ships’ energy efficiency but also in gaining insights into their emissions. However, enhancing the accuracy of these predictions poses significant challenges due to data limitations and the methods employed. Due to these factors, such as data variability and equipment characteristics, ship fuel consumption exhibits certain fluctuations under specific conditions. Previous fuel consumption prediction methods primarily generate a single specific value, making it difficult to capture the volatility of and variability in fuel consumption. To overcome this limitation, this paper proposes a novel method that integrates Gaussian process prediction with quantile regression theory to perform interval predictions of ship fuel consumption, providing a range of possible outcomes. Through comparative analyses with traditional methods, the possibility of using the method is verified and its results are validated. The results indicate the following: (1) at a 95% confidence level, the proposed method achieves a prediction interval coverage probability of 0.98 and a prediction interval normalized average width of 0.123, which are significantly better than those of the existing backpropagation neural network (BPNN) and gradient boosting decision tree (GBDT) quantile regression models; (2) the prediction accuracy of the proposed method is 92% for point forecasts; and (3) the proposed method is applicable to main datasets, including both noon report and sensor datasets. These findings provide valuable insights into interval predictions of ship fuel consumption and highlight their potential applications in related fields, emphasizing the importance of accurate interval predictions in intelligent energy efficiency optimization.

US Funds’ returns-based ESG extraction and implementation: a multifaceted quantile regression approach

ABSTRACT This study introduces a novel ESG intrinsic-based return factor and its application in asset pricing. This factor is extracted using a parallelized rolling window estimation and extreme value-weighted quantile portfolios. It carries a positive risk premium, indicating that investors are willing to assess its risk exposure. We further show that higher returns can be obtained in the top 30% quantiles using a long-only trading strategy. We apply a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to explain US fund returns and address the needs of investors seeking to optimize their investment strategies. This model surpasses traditional benchmark models by performing deep quantile estimation and considering the nonlinear relationships between fund returns and six firm-based characteristics. This approach empowers investors by explaining the core principles of impact investing and highlighting how our constructed ESG risk factor can generate competitive returns even in volatile markets when its risk is well assessed. Highlights A sustainable smart beta factor construction is ensured ESG Long-only trading strategy grants higher returns in the top 30% quantiles Using ML, we further assessed the need to implement ESG factors into asset pricing Positive ESG risk premium reveals the investors’ interest in impact investing US market remains promising for positive ESG investment signals

The association between health literacy and quality of life of patients with type 2 diabetes mellitus: A cross-sectional study.

Health literacy-driven interventions in patients with type 2 diabetes have been found to play an important role in achieving glycemic control and enhancing diabetic self-management outcomes. The present study aimed to examine the role of health literacy as a predictor of quality of life among diabetic patients in Jordan. This cross-sectional study enrolled 400 Type 2 diabetic patients visiting the endocrinology department at the outpatient clinic at Al Basheer Hospital in Amman, Jordan. The data were collected between 1st of August and 28th of December 2023, using the validated Jordanian Diabetic Health Literacy Questionnaire and the EuroQol-5D tool. A quantile regression analysis was conducted to explore the factors associated with health-related quality of life among the study participants. This study included 68.8% females, with a median age of 58 (50-64) years. The median EQ5-D index score was 0.66 (0.41-0.78). Findings from regression analysis indicated as patients' age increased, their quality of life scores significantly decreased (-0.004, 95%CI (-0.006, -0.001), p = 0.002). Additionally, higher JDHLQ scores were significantly associated with higher EQ5-D scores (0.012, 95% CI (0.006-0.018), p<0.001). Moreover, patients with only an elementary education had significantly lower EQ5-D scores compared to those with a postgraduate education (-0.106, 95%CI (-0.190, -0.023), p = 0.013). The findings of the present study emphasize the importance of including health literacy assessments and interventions in the diabetes care plans of patients in Jordan.

Impact of greenhouse gas emission, renewable energy, and economic growth on health expenditure in Southeast Asia: A comparative analysis of econometric models

Purpose — The study explores the effects of greenhouse gas emissions, renewable energy, and economic growth on health expenditures across Southeast Asia while comparing the performance of different econometric models for accuracy in analysis.Method — To analyze the relationships among variables, the study employs three econometric models, the Autoregressive Distributed Lag Model, the co-integration Model, and the Quantile Regression Model, using annual data from 2000 to 2020.Findings — The results reveal that greenhouse gas emissions and GDP significantly influence health expenditure in all three models. However, the significance of renewable energy consumption varies, with only the quantile regression model indicating a significant relationship with health expenditure. Model comparison based on Mean Squared Error (MSE) suggests that the autoregressive distributed lag (ARDL) model provides the most accurate predictions. Also, it found that there is a short-run and long-run causal effect of GHG and GDP on health expenditure, as well as health spending on GDP.Implication — This study helps to understand how economic growth, environmental factors, and healthcare spending interact to develop sustainable policies to address health and environmental problems in Southeast Asia.Originality — This research contributes to the body of knowledge examining the impact of economic and environmental factors on health expenditures in Southeast Asia through a comparative analysis of different econometric models.

An antimicrobial resistance gene situationer in the backyard swine industry of a Philippine City

Antimicrobial resistance (AMR) as a result of antimicrobial overuse and misuse in agriculture is a growing concern, especially in the predominant but poorly regulated backyard or smallholder swine farms of the Philippines. A city-scale surveillance of antimicrobial resistance genes (ARGs) was conducted through selected backyard swine farms to obtain a comprehensive understanding of the AMR situation in the backyard sector of Davao City, Philippines. The ARGs encoding resistance to four antimicrobial classes (β-lactams: blaCTX−M, blaOXA, blaSHV, blaTEM, mecA; quinolones: qnrS and qnr-A2; macrolides: ermA and ermB; tetracycline: tetM) were quantified in swine feces via real-time PCR, and point maps were generated to visualize the ARG geographic distribution. The ARGs bla-TEM, ermB, qnrS, and tetM were detected in all farms at relatively intermediate to high levels, consistently representing the four most used antimicrobial classes in Philippine farms. All the investigated ARGs were present in some local barangay units, indicating potential emergence of AMR hotspots. A 0.5 quantile regression analysis identified specific backyard farm attributes associated with ARG occurrence and diversity, which may influence the ecological spread of the ARGs. These findings provide support for evidence-based guidelines to reinforce a One Health framework for the Philippine National Action Plan to Combat AMR.

Exploring the drivers of ecological footprint: Impacts of road transportation infrastructure, transport tax, and environment technologies

This study explores the dynamic effects of road transportation infrastructure, transport taxes, economic growth, trade openness, and environmental technologies on the ecological footprint using three quantile regression techniques: Simultaneous, Powell, and Method of Moments Quantile Regression (MMQR). These variables are selected for their significant influence on both economic outcomes and environmental sustainability, reflecting the interconnectedness of transportation systems, government policies, and technological advancements in shaping ecological impacts. Based on panel data from 19 European countries between 1995 and 2020, the results reveal that investments in road infrastructure and higher transport taxes contribute to reducing ecological footprint. At the middle and high quantiles, advancements in environment-related technologies further decrease the ecological footprint, whereas trade openness tends to worsen ecological quality. Importantly, the relationship between GDP and the ecological footprint follows an inverted U-shape, confirming the Environmental Kuznets Curve (EKC) hypothesis across all quantiles, which suggests that economic growth can support environmental sustainability in the long run. These findings are validated by robustness checks using fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegration regression (CCR) models. Key policy implications include the need for targeted investments in road infrastructure and transport tax strategies, tailored to national contexts, along with technology-driven strategies to curb environmental degradation. Lastly, the panel causality test reveals unidirectional causality from economic growth, road transportation infrastructure, and environment-related technologies to ecological footprint, highlighting their significance as short-term contributors to environmental improvement.

The impact of risk aversion on dynamic conditional correlations across markets: Application of DCC-EGARCH and quantile regression

The impact of risk aversion on dynamic conditional correlations across markets: Application of DCC-EGARCH and quantile regression

Prenatal exposure to neonicotinoid insecticides, fetal endocrine hormones and birth size: Findings from SMBCS

BackgroundNeonicotinoid insecticides (NNIs) were reported to be endocrine disruptors and cause adverse health effects in human. However, epidemiological evidence about the effect of prenatal NNIs exposure on birth outcome and hormones remains limited. ObjectivesThis study aimed to explore the effects of prenatal NNIs exposure on neonatal birth size and endocrine hormones, and assess the potential mediating role of hormones. MethodsThe study included 860 mother–child pairs from the Sheyang Mini Birth Cohort Study. 12 parent NNIs (p-NNIs) and 6 metabolites of NNIs (m-NNIs) were measured in maternal urine samples collected on their delivery days, and 5 thyroid hormones and 2 sex hormones were analyzed in cord serum. The concentrations of p-NNIs and its specific metabolite(s) were summed to characterize the role of each class of NNIs. Generalized linear model and weighted quantile sum regression were used to examine the impact of prenatal NNIs exposure on birth size and endocrine hormones, and potential mediating roles of hormones were further explored using mediation analysis. ResultsHigher detection frequencies of m-NNIs were observed than those in p-NNIs. A decrease in neonatal head circumference for gestation age z-score was associated with a 10-fold increase in 5-OH-IMI (β = -0.15, 95 %CI: −0.26, −0.03), ∑DIN (β = -0.22, 95 %CI: −0.40, −0.03), ∑IMI (β = -0.20, 95 %CI: −0.35, −0.06) and ∑NNIs (β = -0.23, 95 %CI: −0.42, −0.04). ∑IMI and ∑DIN were the major contributors to the significantly negative mixture effect and no sex-specific effect was observed. Negative associations were observed between ∑DIN and TT3 (β = -0.013, 95 %CI: −0.025, −0.002), ∑IMI and T (β = -0.035, 95 %CI: −0.065, −0.004), respectively. Furthermore, TT3 and T demonstrated 6.7 % and 6.1 % mediating effects on the negative association of prenatal ∑DIN and ∑IMI exposure with head circumference. ConclusionsOur findings suggested the potential endocrine disruptive properties of NNIs and their impacts on head circumference. Endocrine hormones may partly mediate these associations.

An ADMM approach for unit commitment with considering dynamic line rating

To enhance the transmission capabilities in power system scheduling, this paper develops a unit commitment model that incorporates dynamic transmission line capacities and proposes an efficient solving algorithm. A multi-scenario unit commitment model that integrates dynamic transmission line capacities is introduced, using quantile regression to construct a data-driven capacity increase model based on historical environmental data. The model is solved using Lagrangian relaxation and the Alternating Direction Method of Multipliers (ADMM) to decouple dynamic constraints, allowing the dual problem to be decomposed into sub-problems and solved iteratively. The proposed model and algorithm are validated using the IEEE-118 and IEEE-300 test cases, demonstrating their effectiveness in handling dynamic transmission line capacities and improving scheduling performance.The approach provides a robust and flexible solution for power system scheduling, enhancing reliability and economic efficiency.

Partial Quantile Tensor Regression

Tensors, characterized as multidimensional arrays, are frequently encountered in modern scientific studies. Quantile regression has the unique capacity to explore how a tensor covariate influences different segments of the response distribution. In this work, we propose a partial quantile tensor regression (PQTR) framework, which novelly applies the core principle of the partial least squares technique to achieve effective dimension reduction for quantile regression with a tensor covariate. The proposed PQTR algorithm is computationally efficient and scalable to a large tensor covariate. Moreover, we uncover an appealing latent variable model representation for the PQTR algorithm, justifying a simple population interpretation of the resulting estimator. We further investigate the connection of the PQTR procedure with an envelope quantile tensor regression (EQTR) model, which defines a general set of sparsity conditions tailored to quantile tensor regression. We prove the root-n consistency of the PQTR estimator under the EQTR model, and demonstrate its superior finite-sample performance compared to benchmark methods through simulation studies. We demonstrate the practical utility of the proposed method via an application to a neuroimaging study of post traumatic stress disorder (PTSD). Results derived from the proposed method are more neurobiologically meaningful and interpretable as compared to those from existing methods.

Measuring How AI Innovations and Financial Accessibility Influence Environmental Sustainability in the G-7: The Role of Globalization with Panel ARDL and Quantile Regression Analysis

This study investigates the impact of AI innovation on environmental sustainability in the G-7 region from 2010 to 2022. Additionally, it tests the Load Capacity Curve (LCC) hypothesis in relation to financial accessibility, globalization, and urbanization. Cross-sectional dependence and slope homogeneity tests reveal the presence of cross-sectional dependence and heterogeneity issues. Panel unit root and panel cointegration tests confirm that the variables are free from unit root problems and are cointegrated in the long run. To identify significant factors influencing environmental sustainability, this study employs Panel ARDL and Quantile Regression methods. Both methods confirm the LCC hypothesis in the G-7 region, demonstrating a U-shaped relationship between income and the load capacity factor. The results indicate that AI innovation and financial accessibility are significantly positively correlated with the load capacity factor, while globalization and urbanization are negatively correlated, leading to lower environmental sustainability. To validate the robustness of the Panel ARDL and Quantile Regression results, Driscoll-Kraay standard errors, Augmented Mean Group, and Common Correlated Effects Mean Group estimation approaches are applied, all of which support the initial findings. Furthermore, the D-H causality test reveals unidirectional causality from economic growth, financial accessibility, globalization, and urbanization to the load capacity factor, and bidirectional causality between AI innovation and the load capacity factor.

Testing Forecasting Properties of Different Approaches to Interval Forecasting (Using the Example of Inflation in Russia)

The paper contains the main results of the study of the quality of interval forecasts based on quantile regression and smooth quantile regression models relative to interval forecasts based on OLS models for Russian inflation. All models in the work were built from the logic of the possibility of forecasting in real-time, in connection with which the authors used inflation that was not cleared of seasonality in the expression month to the same month of the previous year. For each forecast horizon, the authors developed a separate model from lagged values of inflation and regressors so that the known values of lagged variables were always used on the right side of the equation.The authors tested a wide range of specifications – from very compact autoregressive models with one or two lags to wide ones with three additional regressors in addition to inflation lags. The best quality from the point of view of CRPS (Continuous Ranked Probability Score) metrics typical for such a task were the specifications that included the only additional variable – the USD/RUB exchange rate. As the confidence interval narrowed and at longer horizons, quantile and smooth quantile models became increasingly better relative to OLS models according to the CRPS metric.According to the authors, in the presence of inflation targeting, qualitative inflation forecasts can be used by the Bank of Russia in conducting monetary policy when forecasting future inflation dynamics and stress-testing the Russian economy. In general, this can help increase the economic agents’ confidence in the Bank of Russia within the concept of rational expectations

Maternal mitochondrial DNA copy number and methylation as possible predictors of pregnancy outcomes in a Michigan pregnancy cohort

ABSTRACT Background: Little is understood about the role of mitochondria in pregnancy-related adaptations. Therefore, we evaluated associations of maternal early-to-mid pregnancy mitochondrial DNA copy number (mtDNAcn) and mtDNA methylation with birth size and gestational length. Methods: Michigan women (n=396) provided venous bloodspots at median 11 weeks gestation to quantify mtDNAcn marker NADH-ubiquinone oxidoreductase chain 1 (ND1) using real-time quantitative PCR and mtDNA methylation at several regions within four mitochondria-specific genes using pyrosequencing: MTTF (mitochondrially encoded tRNA phenylalanine), DLOOP (D-loop promoter region, heavy strand), CYTB (cytochrome b), and LDLR (D-loop promoter region, light strand). We abstracted gestational length and birthweight from birth certificates and calculated birthweight z-scores using published references. We used multivariable linear regression to evaluate associations of mtDNAcn and mtDNA methylation with birthweight and birthweight z-scores. Cox Proportional Hazards Models (PHMs) and quantile regression characterized associations of mitochondrial measures with gestational length. We also considered differences by fetal sex. Results: Using linear regression and Cox PHMs, mtDNAcn was not associated with birth outcomes, whereas associations of mtDNA methylation with birth outcomes were inconsistent. However, using quantile regression, mtDNAcn was associated with shorter gestation in female newborns at the upper quantiles of gestational length, but with longer gestational length in males at the lower quantiles of gestational length. Maternal LDLR, DLOOP, and MTTF methylation was associated with longer gestational length in females at the upper quantiles and in males at lower gestational length quantiles. Conclusions: Maternal mtDNAcn and mtDNA methylation were associated with gestational length in babies born comparatively early or late, which could reflect adaptations in mitochondrial processes that regulate the length of gestation.

Measuring Multi-Dimensional Mobile Behavior Effect on Inclusive Finance: Evidence from China

Credit Invisible is one key area that many countries put much effort to solve in decades. According to the 2020 World Bank statistics, for example, there are over 500 million Chinese and 45 million American, classified as credit invisible who don’t have banking and finance history in bank or credit bureau, making them difficult to borrow money from financial institution. Previous studies adopted different non-financial information to evaluate one’s credit worthiness and status to address this issue. However, they provide little information about how real mobile user interactions can be used to solve this issue in inclusive finance. This paper proposes a novel data generative framework to fusion APP data, call detail record data and SMS data with a total of 4,689 attributes derived from a large-scale mobile dataset. We then construct a unique set of mobile behavior-driven credit risk factors based on statistical diversity, intensity, consistency, and regularity of mobile user behavior characterizing user preferences, attitudes, geolocation, and temporal patterns. Empirical analysis demonstrates that the newly discovered mobile behavior factors are useful as new inputs for credit scoring and proves the factors representing new source of positive and negative credit information. Decision tree analysis and Quantile regression are conducted to validate effect of these factors to credit default. It facilitates credit assessment based on non-financial data for the credit invisible people, which promoting inclusive finance to larger community in society. We also analyze implications of mobile user characterization findings in relation to credit default which helps decision makers to optimize credit policy and product design.

Using Four Machine Learning Methods to Analyze the Association Between Polycyclic Aromatic Hydrocarbons and Visual Impairment in American Adults: Evidence from NHANES

The causes of visual impairment are complex and may be influenced by exposure to environmental pollutants. Using data from the 2003–2004 National Health and Nutrition Examination Survey (NHANES), we examined the association between exposure to ten polycyclic aromatic hydrocarbons (PAHs) and vision problems in 1149 U.S. adults. We employed various supervised learning methods, including variable selection techniques such as Lasso and elastic net, weighted quantile sum regression (WQS), and Bayesian kernel machine regression (BKMR), to assess the association between PAHs and the occurrence of visual impairments. The mediation effects between urinary 2-fluorene and inflammation were evaluated using mediation analysis. Both the lasso and elastic net models consistently identified two specific PAH congeners, 2-fluorene and 1-phenanthrene, as significant predictors. The WQS regression revealed a positive relationship between the PAH mixture and visual impairment, with notable contributions from urinary 2-fluorene (weight = 0.39) and 9-fluorene (weight = 0.21). BKMR analysis indicated that the likelihood of visual impairment increases with higher PAH exposure, showing a general upward trend. This trend also revealed a positive association between visual impairment and exposure to four specific PAH metabolites, including 2-fluorene. A significant mediation effect was observed for alkaline phosphatase (p = 0.03), with a proportion mediated of 10.48%. Our findings suggest a significant association between PAHs and visual impairment, with multiple statistical models consistently emphasizing the crucial role of 2-fluorene exposure. This study highlights the importance of considering environmental pollutants as significant contributors to visual health outcomes, providing insights for preventing visual impairment.

Variable selection in macroeconomic stress test: a Bayesian quantile regression approach

AbstractThe key assumption in stress test scenarios is that selected risk factors are useful in predicting banks’ tail risks under severe economic conditions. We argue that high-dimensional Bayesian quantile regression models with shrinkage priors are ideal for identifying those factors. We illustrate our methods by identifying key drivers for banks with different asset sizes from a high-dimensional database. We found that leverage indicators, asset prices, and labor market measures are the best predictors of banks’ performance. The usefulness of our methods is further demonstrated by a forecast comparison between the selected variables and those used in the regulatory stress tests.

Can public hospital reform reduce medical resource mismatches? Evidence from China

BackgroundThe mismatch of medical resources is a significant issue in global healthcare, undermining both service accessibility and system efficiency. In China, despite the implementation of the “Healthy China” strategy, persistent mismatches remain due to factors such as industrialization, urbanization, and population aging. This study empirically evaluates the impact of Public Hospital Reform (PHR) on mitigating these mismatches.MethodsA Difference-in-Differences (DD) approach is applied to panel data from 300 cities spanning 2010 to 2021, using the phased implementation of PHR as a quasi-natural experiment. This allows for a comparative analysis of changes in resource allocation between cities that adopted the reform and those that did not. Quantile regression assesses the effects of PHR across varying levels of resource mismatch, while mechanism tests investigate how PHR influences mismatches through cost reduction and supply expansion.ResultsPHR is found to reduce medical resource mismatches by 13.9%, primarily driven by cost reductions and increased resource supply. The effects are more pronounced at both lower and higher levels of mismatch, with a limited impact at mid-levels. Furthermore, the reform’s effectiveness diminishes as it is extended to more cities, suggesting a potential saturation effect.ConclusionsThis study demonstrates that PHR significantly alleviates medical resource mismatches in China. The findings underscore the need to focus on cost control and resource supply in future healthcare reforms, providing key insights for policymakers in developing countries facing similar healthcare resource challenges.

Exploring value relevance of EVA over different stages of business lifecycle

Purpose This study aims to determine whether the financial ecosystem is reliable for evaluating a company’s financial performance at various lifecycle stages or whether value-based performance metrics like Economic Value Added (EVA) are the most comprehensive indicators regardless of the company’s lifecycle stage. The evaluation will analyse the importance of value-based and accounting performance benchmarks (EVA, Return on Capital Employed, Return on Equity and Earnings per Share) in creating shareholder value, as measured by market value added, throughout the company’s lifecycle. Design/methodology/approach This study is based on a robust empirical analysis of 228 Indian firms listed on the National Stock Exchange Nifty 500 Index from 2006 to 2023. By considering firms of different sizes and ages, the authors aim to capture and analyse any distinct impact across various stages of their lifecycles. Using quantile regression for analysis equips the authors to effectively address extreme events such as the 2008 global financial crisis and the COVID-19 pandemic, making the method particularly suitable for conditionally distributed samples based on size and age. Findings The research reveals that various performance indicators are crucial at different stages of a company’s lifecycle for generating wealth for shareholders. However, considering the significance of the EVA measure, it is recommended that policymakers standardize the calculation of EVA and mandate its disclosure. Originality/value The authors comprehensively analysed the economic value addition relative to a company’s size and age, going beyond previous studies that focused solely on specific size categories. This offers managers valuable insights into aligning business performance measures with the various stages of the business lifecycle.

Analyzing risk contagion and volatility spillover across multi-market capital flow using EVT theory and C-vine Copula

There exists a potential interdependence among the United States markets, alongside an exceptional dependence on the East Asian stock markets. This transmission of risks is similarly evident in funds that are traded within markets. The current study seeks to uncover the pathways of risk contagion among various financial markets. This study examines how risks of funds spread across borders among China, the United States, and East Asia using Extreme Value Theory (EVT) and C vine copula quantile regression. It uses models such as AR (1), EGARCH (1, 1), Peak over Threshold (POT), and Copula methods to predict volatility events and correlation patterns during times of high volatility versus regular periods. Notably, the United States exhibits a risk transmission effect on the East Asian market compared to China. Furthermore, the findings indicate that, in times of high volatility, the risk spillover effect is comparatively weak, which is contrary to the situation of the US market. The study suggests that China's financial impact could progressively rise due to initiatives aimed at sector integration and enhancing financial independence. These findings have enlightening consequences for macro-prudential regulatory agencies, emphasizing the necessity of effective regulation to address cross-border risk spillovers. International investors can benefit from these results by incorporating risk-hedging strategies, accurately evaluating derivatives, and making wise investment decisions.

Portion Size Norms of Discretionary Foods and Eating Settings: A Repeated Cross-Sectional Study.

The increase in serving sizes of energy-dense nutrient-poor discretionary foods over time, with attractively presented large servings and package sizes, has led to portion distortion and a new 'normal' for serving sizes. Little data exists on the variations of portion size norms of discretionary foods across settings. This study aimed to examine the differences in the range of normal portion sizes of commonly consumed foods between home and out-of-home settings (coffee shops, restaurants). A repeated cross-sectional design was used, with nine selected discretionary foods and beverages included in a validated online image-series questionnaire. Participants completed the questionnaire at two time points to report their normal portion sizes in home and out-of-home settings. Quantile regression models were used to examine differences in the range of normal portion sizes (17th to 83rd percentile, representing the majority of the study population) between settings. A final sample of 295 participants was included in the analysis (51% females, mean age 40 ± 14 years). The ranges of normal portion sizes did not differ by settings for all test foods except for sugar-sweetened beverages (SSB) tested in both containers and glassware. SSB showed smaller normal portion sizes at home compared to fast food restaurants (in a bottle/can and in a glass/cup; p < 0.001). These findings suggest that the portion size norms of many discretionary foods are mostly consistent in home and out-of-home settings. As the typical serving sizes available to consumers in the out-of-home settings are large, it is essential to establish practical serving size guidelines directed at the food industry to increase the availability of smaller size options and empower consumers towards better portion control.