Most papers which explored so far macroeconomic variables took into account income and wealth. Equally important as the previous macroeconomic variables is the expenditure or consumption, which shows the amount of goods and services that a person or a household purchased. Using statistical distributions from Physics, s…
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48 results for “Expenditure distribution”
We empirically investigate distributions of individual consumption expenditure f or four commodity categories conditional on fixed income levels. The data stems from the Family Expenditure Survey carried out annually in the United Kingdom. W e use graphical techniques to test for normality and lognormality of these dis…
This work presents an empirical study of the evolution of the consumer expenditure distribution in India during 1982-2007. We have used the National Sample Survey Organization data and analysed the expenditure distribution for the urban and rural sectors. It is found that this distribution is a mixture of two distribut…
The paper models income, wealth, and expenditure using various distributions and thermodynamic principles.
problem Analyzing income, wealth, and expenditure distributions using statistical physics.
method Applying logistic, Fermi-Dirac, and polynomial distributions to income, wealth, and expenditure data.
result Fermi-Dirac distribution fits well for upper income segments and annual data.
Study decomposes racial healthcare disparities via shifts in mediator distributions.
problem Racial disparities in healthcare expenditures and their underlying drivers.
method Framework decomposing disparities into mediator distribution shifts and residual components, using MEPS data.
result Substantial disparities persist even when mediators are equalized, suggesting unmeasured or structural factors.
Polynomial distribution can be applied to dynamical systems in certain situations. Macroeconomic systems characterized by economic variables such as income and wealth can be modelled similarly using polynomials. We extend our previous work to data regarding income from a more diversified pool of countries, which contai…
Super learner with Huber loss improves cost prediction and causal effect estimation in healthcare expenditure data.
problem Challenges in modeling healthcare expenditure distributions with standard super learning methods.
method Proposes a super learner using Huber loss, a robust loss function that down-weights outliers.
result Demonstrates appreciable finite-sample gains in cost prediction and causal effect estimation.
We approximate the distribution of total expenditure of a retail company over warranty claims incurred in a fixed period [0, T], say the following quarter. We consider two kinds of warranty policies, namely, the non-renewing free replacement warranty policy and the non-renewing pro-rata warranty policy. Our approximati…
Measuring Value in Healthcarephysics.soc-ph
A statistical description and model of individual healthcare expenditures in the US has been developed for measuring value in healthcare. We find evidence that healthcare expenditures are quantifiable as an infusion-diffusion process, which can be thought of intuitively as a steady change in the intensity of treatment …
Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers. We used expenditure data related t…
Study assesses health plan risk measures for Solvency Capital Requirement.
problem Assessing risk measures for health plans to meet Solvency Capital Requirement.
method Three-part regression model with three GLMs for claim counts, episode allocation, and severity.
result Reduction in regression models compared to traditional methods.
We extend the exploration regarding dynamical approach of macroeconomic variables by tackling systematically expenditure using Statistical Physics models (for the first time to the best of our knowledge). Also, using polynomial distribution which characterizes the behavior of dynamical systems in certain situations, we…
Study shows racial bias in health data, which can be reduced with simple techniques.
problem Racial bias in health indicators measured by the Medical Expenditure Panel Survey (MEPS).
method Used publicly available and nationally representative MEPS data to show bias in predictive models for care management.
result Racial bias can be significantly reduced using simple mitigation techniques.
ChatGPT and Corporate Policiesq-fin.CP
ChatGPT scores corporate investment plans, predicting future spending and returns.
problem Measuring and predicting corporate investment plans.
method Created a firm-level ChatGPT investment score based on conference calls.
result The investment score predicts future capital expenditures and returns.
Alternative model predicts health insurance reimbursement based on contract limitations.
problem Estimating the ratio of reimbursement to health care expenditures after deductibles and copayments.
method Proposes a Zero-One Inflated Beta regression model using GAMLSS.
result The model provides a dependency structure between reimbursement and contract limitations.
Optimizes profit in targeted marketing across multiple markets with varying marketing expenditures.
problem Maximizing profit in a sequential marketing strategy with multiple markets and varying marketing costs.
method Near-optimal algorithms in an adversarial bandit setting, proving regret bounds for different demand curve types.
result Proved near-optimal regret bounds for the profit-maximization problem in targeted marketing.
Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercisescs.LG
Machine learning predicts exercise load from heart rate data post-exercise.
problem Monitoring energy expenditure in real life.
method Machine learning methods (linear regression, etc.) applied to heart rate data.
result Random forest and k-nearest neighbors classifiers predict load levels accurately.
Paper improves accuracy of energy expenditure estimation from accelerometer data.
problem Noisy and unreliable accelerometer measurements for scientific settings.
method Bag-of-Words model applied to accelerometer data for classification and regression.
result Improved root mean-squared error of energy expenditure by 1.4 units.
Deep learning models accurately recognize and estimate physical activity types and energy expenditure from wrist accelerometer data.
problem Rigorous evaluation of wrist-worn accelerometers for assessing physical activity across the lifespan.
method Built deep learning networks to extract spatial and temporal representations from time-series data, recognizing physical activity types and estimating energy expenditure.
result Deep learning models achieved high performance: F1 scores of 0.82, 0.81, and 95 for sedentary, locomotor, and lifestyle activities, respectively; root mean square error of 1.1 for EE estimation.
New method clusters health codes for better risk adjustment.
problem Improving insurer incentives to attract lower-cost enrollees.
method Markov Chain Monte Carlo methods for clustering diagnostic codes.
result Methodology outperforms common alternatives in health expenditure prediction.
Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series modelstat.ML
Bayesian model predicts sales from ad spending.
problem Predicting sales from advertising expenditures.
method Bayesian structural time series model.
result Model can incorporate prior information and update with data.
Study analyzes Brazilian congress members' spending patterns.
problem Lack of transparency in government spending by congress members.
method Neural network-based approach for analyzing and visualizing spending patterns.
result Inspires new machine learning methodologies for government transparency.
Method detects multi-timescale consumer spending patterns from receipts.
problem Understanding and managing consumer behavior in high-dimensional data.
method Non-negative tensor factorization (NTF) to extract multi-timescale expenditure patterns.
result Consumption patterns are characterized based on spending behavior over different timescales.
Physicists' approach to studying socio-economic inequalities: Can humans be modelled as atoms?q-fin.GN
Physicists study socio-economic inequalities using atom-like models.
problem Understanding the mechanisms behind socio-economic inequalities and invariant features.
method Empirical data analysis and simple physics models.
result Income, wealth, and consumption distributions exhibit log-normal and power law features.
Proposes a mixed pension system combining PAYG and funded contributions to address sustainability.
problem Sustainability of public pension systems due to declining birth rates and increasing life expectancy.
method Combines a classical PAYG scheme with a funded investment scheme to ensure financial sustainability.
result Individuals contribute to a funded part, making them active participants in addressing demographic risks.
Preserves sensitive data distribution for privacy while maintaining utility.
problem Protecting individual privacy while preserving data utility for specific analyses.
method Combines distribution-preserving quantization and k-member clustering.
result Demonstrates improved privacy and utility in real-world applications.
The global financial crisis, beginning in 2008, took an historic toll on national economies around the world. Following equity market crashes, unemployment rates rose significantly in many countries: Italy was among those. What will be the impact of such large shocks on Italian healthcare finances? An empirical model f…
Effects of income redistribution on the evolution of cooperation in spatial public goods gamesphysics.soc-ph
Income redistribution boosts cooperation in spatial public goods games, but the effect depends on the model of redistribution.
problem The impact of income redistribution on cooperation in spatial public goods games.
method Two models of income redistribution were used: local and global. Spatial public goods games were employed to study the evolution of cooperation.
result The cooperation level is significantly higher with local redistribution of income, but lower with global redistribution.
LHIEM model predicts health, income, and employment over years.
problem Lack of path dependency in health policy simulations.
method Discrete-time microsimulation with Markov chain modules.
result Validates health care financing proposal through detailed modeling.
This paper uses open data to analyze foreign tourists' spending patterns in Colombia and the Netherlands.
problem Difficulty in identifying which domestic industries cater to foreign visitors.
method Use of open source data and anonymized transaction data to map tourist destinations and analyze spending behavior.
result Countries may observe different tourist patterns (concentration vs decentralization).
UCRL-CMDP algorithm optimizes RL with constraints on average costs.
problem Optimizing RL in MDPs with average cost constraints.
method Model-based RL algorithms maximizing reward while keeping costs within bounds.
result UCRL-CMDP algorithm's expected regret is upper-bounded by $T^{2\slash 3}$.
Estimates cost savings from early cancer diagnosis.
problem Improving early cancer diagnosis to reduce treatment costs.
method Combining published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis, and extrapolating to other cancer sites.
result Estimates U.S. national annual treatment cost-savings from early cancer diagnosis in the trillions.
Implementing large-scale information and communication technology (IT) projects carries large risks and easily might disrupt operations, waste taxpayers' money, and create negative publicity. Because of the high risks it is important that government leaders manage the attendant risks. We analysed a sample of 1,355 publ…
The study finds that the export shares of machinery and food/crude materials are significantly correlated with GDP.
problem Understanding the relationship between export shares and GDP across different commodity sectors.
method Analysis of GDP and international trade data using the SITC classification from 1962 to 2000.
result The export shares of machinery and food/crude materials are significantly correlated with GDP, following a power-law relationship.
Optimal attack against autoregressive models by manipulating environment states.
problem Manipulating autoregressive forecasts to track a target trajectory.
method Linear Quadratic Regulator (LQR) for linear models, Model Predictive Control (MPC) for nonlinear models.
result Optimal attack formulations for both white-box and black-box settings.
Investment decision in commodity reserves is modeled with uncertainty and learning.
problem Uncertainty in commodity reserve levels and price uncertainty.
method Continuous-time Markov chain model to value investment option.
result Learning about reserve levels improves investment decision.
The paper examines how share buybacks impact a company's earnings per share.
problem The trade-off between reducing share count and decreasing net earnings due to share buybacks.
method Review of accretive share repurchases, analysis of EPS increase as a function of price paid, and quantification of earnings growth difference.
result Share buybacks can enhance EPS, but the net effect on earnings growth is mixed.
The study examines how climate risk influences sovereign debt default decisions.
problem The relationship between climate risk and sovereign debt default decisions.
method Calibration of a model to analyze the credit spreads of sovereign bonds and the impact of climate vulnerability on bond spreads.
result Climate risk does not significantly influence the decision to default on sovereign debt.
Economic returns of research: the Pareto law and its implicationscond-mat.stat-mech
At what level should government or companies support research? This complex multi-faceted question encompasses such qualitative bonus as satisfying natural human curiosity, the quest for knowledge and the impact on education and culture, but one of its most scrutinized component reduces to the assessment of economic pe…
Georgia needs a new budget code to manage fiscal policies effectively.
problem Weak and incomplete law on Budget System hinders fiscal policy implementation.
method Develop and adopt a new Budget Code with equal force as the Tax Code.
result Effective correlation between state, regional, and local budgets is crucial for social-economic development.
We detect lookahead bias in LLM forecasts using a novel statistical method.
problem Detecting lookahead bias in LLM-generated economic forecasts.
method Developed a statistical procedure using date-only recall queries and estimated Lookahead Propensity (LAP).
result LLM forecasts are contaminated with lookahead bias, as indicated by a positive interaction between LAP and the forecast in accuracy regressions.
Deep neural network predicts diabetic readmission with high accuracy.
problem Predicting 30-day readmission for diabetic patients.
method Categorical embeddings and deep neural network.
result 95.2% accuracy and 97.4% AUROC on diabetic readmission data.
New approach uses satellite imagery to estimate economic growth.
problem Lack of reliable economic data in developing countries.
method Dynamic network and representation learning.
result Accurately predicts spatial gross economic expenditures.
Two new approaches improve decision-making in asset monitoring systems.
problem Sampling bias in risk-based active learning leads to poor performance later.
method Semi-supervised learning and discriminative classification models.
result Discriminative classifiers are more robust to sampling bias.
Enhanced tabular benchmarks for energy-efficient neural architecture search.
problem Energy consumption in deep learning models.
method Introducing EC-NAS, an enhanced tabular benchmark with energy consumption data.
result EC-NAS reveals a balance between energy usage and accuracy in neural architecture search.
SummerTime: Variable-length Time SeriesSummarization with Applications to PhysicalActivity Analysiscs.LG
SummerTime summarizes variable-length time series for machine learning applications.
problem Classical machine learning methods struggle with variable-length time series data.
method Summarizes time series into a fixed-length feature vector using Gaussian Mixture Models (GMM).
result Improves classification and regression performance in physical activity analysis.
Machine learning aids self-healing in cellular networks, tackling data imbalance and cost sensitivity.
problem Challenges in applying machine learning for self-healing in cellular networks.
method Data-driven machine learning techniques addressing data imbalance, insufficiency, and cost sensitivity.
result Feasibility and effectiveness of cost-sensitive fault detection with imbalanced data.
(Early) AI Compute Asset Pricingq-fin.PR
The paper analyzes the pricing of a new compute futures asset.
problem Uncertainty in AI adoption and pricing of compute capital.
method An asset-pricing framework for compute futures, including synthetic futures pricing.
result Preliminary evidence suggests a positive compute risk premium.