arXiv subjects

Paramahansa Pramanik

Publications and source records attributed to Paramahansa Pramanik.

At least 19 recordsLinked to original sources

arXiv2026

Bayesian Signaling and Entry Decisions under Uncertain Market Conditions

We develop a continuous-time entry-deterrence game in which market demand evolves according to the Chan-Karolyi-Longstaff-Sanders (CKLS) stochastic differential equation, allowing mean reversion and state-dependent volatility. An incumbent with privately known strength strategically chooses advertising and promotional expenditures to influence a potential entrant's beliefs, while the entrant faces a costly, irreversible entry decision and optimally waits until market conditions justify participation. Within a dynamic Stackelberg setting, Bayesian learning, asymmetric information, stochastic demand, and strategic controls jointly determine entry and signaling behavior. Using a Feynman-type path-integral control formulation, we characterize a Markovian Nash feedback equilibrium for the firms' expenditure strategies. Our contribution is to integrate CKLS demand uncertainty, private information, irreversible entry, Bayesian belief updating, and path-integral feedback control within a unified continuous-time entry-deterrence framework, while providing a computational alternative to direct Hamilton-Jacobi-Bellman (HJB) approach. We illustrate the framework empirically using 2010-2024 revenue data for Enterprise Products Partners and Targa Resources. The resulting trajectories are qualitatively consistent with the model's predictions, exhibiting persistence, recovery after adverse shocks, and distinct responses associated with different competitive positions, while supporting the model's strategic mechanisms under uncertainty.

econ.TH↗

arXiv2026

Dynamic Physical Hedging amid Jump Losses, Reconstruction-Price Uncertainty, Population Interactions

We study dynamic physical hedging for insurers exposed jointly to catastrophe losses and stochastic reconstruction costs. Surplus evolves as a controlled jump diffusion whose loss amplitude combines marked catastrophe severity, an exogenous mean-reverting cost factor, and endogenous mitigation. We establish well-posedness, moment and stability estimates, and a stopping-time dynamic programming principle, and prove that the value function is the unique viscosity solution of the resulting nonlocal Hamilton-Jacobi-Bellman (HJB) equation Strategic interaction is introduced through a mean field game (MFG) with reduced-form vulnerability costs, yielding a coupled backward-forward HJB-Kolmogorov system. We establish relaxed equilibrium existence, Markovian realization, and uniqueness under appropriate compactness and monotonicity conditions. Numerical experiments show that reconstruction costs and capitalization materially affect optimal hedging and that cross-sectional vulnerability alters equilibrium costs. Tail-family robustness calculations further assess the sensitivity of these conclusions to alternative catastrophe-severity specifications.

q-fin.MF↗

arXiv2026

Stochastic Choice with Distribution-Dependent Preferences

We develop a continuous-time stochastic choice theory with endogenous preference evolution. Unlike dynamic random utility, observed behavior affects future preferences through the conditional distribution of latent preference states, generating endogenous distributional feedback. We show that this feedback has observable behavioral implications and characterize stochastic choice by a behavioral representation consisting of contemporaneous choice and continuation behavior. This representation is identified from stochastic choice, yields a rigidity result linking structural preference dynamics to observable behavior, and characterizes exactly when distribution dependent utility is behaviorally reducible to dynamic random utility. We further prove a behavioral impossibility theorem: stochastic choice arrays exhibiting behavioral distributional feedback admit no dynamic random utility representation. On the probabilistic side, we establish existence and weak uniqueness for the underlying conditional McKean-Vlasov system with conditional law feedback. The structure unifies endogenous information, latent preference dynamics, behavioral identification, and stochastic choice within a single continuous-time model.

econ.TH↗

arXiv2026

Modeling Educational Performance Using School Demographics and Teacher Characteristics

High-dimensional educational datasets often exhibit sparsity, grouped predictors, and locally correlated covariates, limiting the effectiveness of conventional regression methods. We propose an Adaptive Weighted Group Fused LASSO estimator that jointly performs adaptive variable selection, group regularization, and coefficient fusion within a unified penalized regression framework. An efficient ADMM algorithm is developed, and asymptotic properties, including consistency, oracle property, and debiased asymptotic normality, are established. Simulation studies demonstrate superior estimation and prediction performance compared with existing penalized methods. An application to Alabama public school mathematics proficiency data illustrates improved model interpretability, predictive accuracy, and identification of the most influential institutional predictors.

stat.ME↗

arXiv2026

Optimal Harvesting under Stochastic Control: HJB Equation and Feynman-Kac Representation

Sustainable resource management requires harvesting strategies that account for environmental variability and ecological uncertainty. This study investigates optimal harvesting of renewable biological resources within a stochastic framework, where population dynamics are influenced by random environmental fluctuations and modeled using stochastic differential equations. Two complementary approaches are employed: the Hamilton-Jacobi-Bellman (HJB) equation and the Feynman-Kac representation. The HJB framework provides a dynamic optimization rule and characterizes the value function through a nonlinear partial differential equation, while the Feynman-Kac approach offers a probabilistic interpretation of expected returns. A comparative analysis demonstrates the theoretical consistency and practical relevance of both methods for designing economically efficient and ecologically sustainable harvesting policies under uncertainty.

math.OC↗

arXiv2026

Obesity and Sociodemographic Factors in Luminal Breast Cancer

Luminal breast cancers represent the most prevalent molecular subtype of breast carcinoma, with Luminal A tumors generally associated with more favorable clinical outcomes than Luminal B tumors. Obesity-related inflammation and prolonged exposure to exogenous steroids have been implicated in the progression of luminal malignancies. This study evaluated 1,928 patients with Luminal A breast cancer and 1,610 patients with Luminal B breast cancer to examine associations among body mass index (BMI), age, ethnic background, menopausal status, and receptor expression, including estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Patients with Luminal B tumors demonstrated a significantly greater mean BMI compared with those with Luminal A tumors. In addition, Luminal B tumors were more frequently observed among patients of African ancestry relative to White and Hispanic populations. Multivariable analyses revealed that elevated BMI and African ancestry were independently associated with increased odds of Luminal B carcinoma, whereas postmenopausal status was associated with lower risk. Mediation analysis further indicated that BMI partially explained the association between ancestry and Luminal B disease. These findings suggest that obesity and population-specific factors may contribute to the development of more aggressive luminal breast cancer phenotypes.

q-bio.QM↗

arXiv2026

The Quantum Structure of Markets: Linking Hamiltonian-Jacobi-Bellman Dynamics to Schrodinger Equation through Feynman Action

We develop a Euclidean path-integral control to characterize optimal firm behavior in an economy governed by Walrasian equilibrium, Pareto efficiency, and non-cooperative Markovian feedback Nash equilibrium. The approach recasts the problem as a Lagrangian stochastic control system with forward-looking dynamics, thereby avoiding the explicit construction of a value function. Instead, optimal policies are obtained from a continuously differentiable Ito process generated through integrating factors, which yields a tractable alternative to conventional solution methods for complex market environments. This construction is useful in settings with nonlinear stochastic differential equations where standard Hamilton-Jacobi-Bellman (HJB) formulations are difficult to implement. Consistent with Feynman-Kac-type representations, the resulting solutions need not be unique. In economies with a large number of firms, the analysis admits a natural comparison with mean-field game formulations. Our main contribution is to derive a noncooperative feedback Nash equilibrium within this path-integral setting and to contrast it with outcomes implied by mean-field interactions. Several examples illustrate the method's applicability and highlight differences relative to solutions based on the Pontryagin maximum principle generated by HJB.

econ.TH↗

arXiv2026

Modeling Age-Adjusted Mortality in the United States

This research explores how total mortality figures relate to age-standardized death rates within the United States, using the complete historical record of national mortality statistics. Through a detailed investigation of both all-cause and cause-specific mortality trends, the study evaluates the impact of demographic standardization on interpreting mortality data across different time periods and geographic regions. Results indicate a robust and persistent association between crude death totals and age-adjusted rates. However, the findings also demonstrate that without adjusting for age, comparisons over time or across locations may misrepresent underlying epidemiological shifts, largely due to evolving population age structures. The study underscores the critical role of age adjustment as a methodological tool for generating accurate, interpretable, and comparable measures of public health outcomes.

q-bio.OT↗

arXiv2026

A Bayesian Discrete Framework for Enhancing Decision-Making Processes in Clinical Trial Designs and Evaluations

This study examines the application of Bayesian approach in the context of clinical trials, emphasizing their increasing importance in contemporary biomedical research. While conventional frequentist approach provides a foundational basis for analysis, it often lacks the flexibility to integrate prior knowledge, which can constrain its effectiveness in adaptive settings. In contrast, Bayesian methods enable continual refinement of statistical inferences through the assimilation of accumulating evidence, thereby supporting more informed decision-making and improving the reliability of trial findings. This paper also considers persistent challenges in clinical investigations, including replication difficulties and the misinterpretation of statistical results, suggesting that Bayesian strategies may offer a path toward enhanced analytical robustness. Moreover, discrete probability models, specifically the Binomial, Poisson, and Negative Binomial distributions are explored for their suitability in modeling clinical endpoints, particularly in trials involving binary responses or data with overdispersion. The discussion further incorporates Bayesian networks and Bayesian estimation techniques, with a comparative evaluation against maximum likelihood estimation to elucidate differences in inferential behavior and practical implementation.

stat.ME↗

arXiv2025

MetaScoreLens: Evaluating User Feedback Across Digital Entertainment Systems

The popularity of electronic games has grown steadily in recent years, attracting a broad audience across age groups. With this growth comes a large volume of related data, prompting efforts like the PlayMyData to compile and share structured datasets for academic use. This study utilizes such a dataset to compare user review ratings across four current-generation gaming systems: Nintendo, Xbox, PlayStation, and PC. Statistical methods, including analysis of variance (ANOVA), were applied to identify differences in average scores among these platforms. The findings indicate that PC titles tend to receive the most favorable user feedback, followed by Xbox and PlayStation, while Nintendo games showed the lowest average ratings. These patterns suggest that the platform on which a game is released may influence how players evaluate their experience. Such results may be valuable to developers and industry stakeholders in making informed decisions about future investments and development priorities.

cs.HC↗

arXiv2025

Genomic Influence of a Key Transcription Factor in Male Glandular Malignancy

Prostate cancer (PCa) remains a significant global health concern among men, particularly due to the lethality of its more aggressive variants. Despite therapeutic advancements that have enhanced survival for many patients, high grade PCa continues to contribute substantially to cancer related mortality. Emerging evidence points to the MYB proto-oncogene as a critical factor in promoting tumor progression, therapeutic resistance, and disease relapse. Notably, differential expression patterns have been observed, with markedly elevated MYB levels in tumor tissues from Black men relative to their White counterparts potentially offering insight into documented racial disparities in clinical outcomes. This study investigates the association between MYB expression and key oncogenic features, including androgen receptor (AR) signaling, disease progression, and the risk of biochemical recurrence. Employing a multimodal approach that integrates histopathological examination, quantitative digital imaging, and analyses of public transcriptomic datasets, our findings suggest that MYB overexpression is strongly linked to adverse prognosis. These results underscore MYB's potential as a prognostic biomarker and as a candidate for the development of individualized therapeutic strategies.

q-bio.QM↗

arXiv2025

Dissecting Multi-Level Pricing Schemes in the Context of eCW Client Engagement

This paper presents a usage-based pricing framework for the Intelligent Medical Objects ProblemIT Portal utilized by eClinicalWorks (eCW) clients. The approach begins by determining a stable monthly unit price per request, estimated as the median from semi-parametric Bayesian cubic smoothing spline analyses covering the period November 2015 to December 2016. Clients are subsequently segmented into eight volume-based tiers, with total charges computed by multiplying the derived median unit price by each client's total request count. Examination of the dataset reveals that 806 accounts with a single registered user and 470 accounts with two registered users both exhibit disproportionately high request volumes. The proposed model incorporates adjustments to account for these anomalies.

stat.AP↗

arXiv2025

Optimal Feedback Control in Social Networks in a McKean-Vlasov-Friedkin-Johnsen System

This paper presents a comprehensive analytical formulation for deriving a closed-form optimal strategy for agents operating within a social network, modeled through a McKean-Vlasov stochastic differential equation (SDE). Each agent aims to minimize a personal dynamic cost functional that accounts for deviations from the collective opinions of others, their own past beliefs, and is influenced by randomness and inherent opinion rigidity, often described as stubbornness. To tackle this, we develop a novel methodology rooted in a Feynman-type path integral framework, incorporating a specially designed integrating factor to obtain explicit feedback control laws. This approach provides a tractable and insightful solution to the control problem in a setting shaped by both memory and noise. As part of our analysis, we adopt a modified form of the Friedkin-Johnsen opinion dynamics model to more accurately capture the influence of prior beliefs and social interactions, enabling the explicit derivation of the optimal strategy. Comparative simulations further illustrate the effectiveness and adaptability of our method across different network structures, highlighting its potential relevance to understanding opinion evolution and influence strategies in complex social systems.

math.OC↗

arXiv2025

Predictive Significance of CD276/B7-H3 Expression in Baseline Biopsies of Advanced Prostate Carcinoma

At the time of diagnosis, prostate cancer can appear deceptively mild or already display signs of widespread disease. Predicting long-term outcomes is often uncertain. This research focused on measuring CD276/B7-H3, an immune checkpoint protein linked to tumor development, in diagnostic tissue samples from 248 men. Participants included both those with cancer confined to the prostate and those with confirmed metastases. Analysis showed that patients with metastatic disease were more likely to exhibit increased B7-H3 levels. Strong expression of this marker was associated with shorter survival times and was observed alongside higher PSA concentrations and greater tumor aggressiveness based on Gleason grading. These trends remained consistent even when other prognostic factors were taken into account. The results suggest that assessing B7-H3 during the initial biopsy could help clinicians identify high-risk patients earlier. This marker may also represent a new target for treatment strategies in advanced prostate cancer.

q-bio.QM↗

arXiv2025

Exploring the Interplay of Adiposity, Ethnicity, and Hormone Receptor Profiles in Breast Cancer Subtypes

This study explores how obesity and race jointly influence the development and prognosis of Luminal subtypes of breast cancer, with a focus on distinguishing Luminal A from the more aggressive Luminal B tumors. Drawing on large-scale epidemiological data and employing statistical approaches such as logistic regression and mediation analysis, the research examines biological factors like estrogen metabolism, adipokines, and chronic inflammation alongside social determinants including healthcare access, socioeconomic status, and cultural attitudes toward body weight. The findings reveal that both obesity and racial background are significant predictors of risk for Luminal B breast cancers. The study highlights the need for a dual approach that combines medical treatment with targeted social interventions aimed at reducing disparities. These insights can improve individualized risk assessments, guide tailored screening programs, and support policies that address the heightened cancer burden experienced by marginalized communities.

q-bio.QM↗

arXiv2025

Impact of random monetary shock: a Keynesian case

This study investigates the optimal strategy for a firm operating in a dynamic Keynesian market setting. The firm's objective function is optimized using the percent deviations from the symmetric equilibrium of both its own price and the aggregate consumer price index (CPI) as state variables, with the strategy in response to random monetary shocks acting as the control variable. Building on the Calvo framework, we adopt a mean field approach to derive an analytic expression for the firm's optimal strategy. Our theoretical results show that greater volatility leads to a decrease in the optimal strategy. To asses the practical relevance of our model, we apply it to four leading consumer goods firms. Empirical analysis suggests that the observed decline in strategies under uncertainty is significantly steeper than what the model predicts, underscoring the substantial influence of market volatility.

econ.TH↗

arXiv2025

An optimal level of Stubbornness to win a soccer match

This study conceptualizes stubbornness as an optimal feedback Nash equilibrium within a dynamic setting. To assess a soccer player's performance, we analyze a payoff function that incorporates key factors such as injury risk, assist rate, passing accuracy, and dribbling ability. The evolution of goal-related dynamics is represented through a backward parabolic partial stochastic differential equation (BPPSDE), chosen for its theoretical connection to the Feynman-Kac formula, which links stochastic differential equations (SDEs) to partial differential equations (PDEs). This relationship allows stochastic problems to be reformulated as PDEs, facilitating both analytical and numerical solutions for complex systems. We construct a stochastic Lagrangian and utilize a path integral control framework to derive an optimal measure of stubbornness. Furthermore, we introduce a modified Ornstein-Uhlenbeck BPPSDE to obtain an explicit solution for a player's optimal level of stubbornness.

math.OC↗

arXiv2025

On factors influencing consumer preference in pipeline stages: an experiment

This paper presents a case study on the eClinical data of Intelligent Medical Objects, which currently employs eight pipeline stages. Historically, the pipeline stage progresses inversely with the number of customers. Our objective is to identify the key factors that significantly affect consumer presences at the more advanced stages of the pipeline. Logistic regression is utilized for this analysis. This technique estimates the probability of an event occurring, enabling researchers to evaluate how various factors influence specific outcomes. Widely applied across disciplines such as medicine, finance, and social sciences, logistic regression is particularly useful for classification tasks and identifying the importance of predictors, thus supporting data-driven decision-making. In this study, logistic regression is used to model the likelihood of reaching the eighth pipeline stage as the dependent variable, revealing that only a few independent variables significantly contribute to explaining this outcome.

stat.AP↗