arXiv subjects

Paramahansa Pramanik

Publications and source records attributed to Paramahansa Pramanik.

29 records · Page 2Linked to original sources

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↗

arXiv2024

Bayes Factor of Zero Inflated Models under Jeffereys Prior

Microbiome omics data including 16S rRNA reveal intriguing dynamic associations between the human microbiome and various disease states. Drastic changes in microbiota can be associated with factors like diet, hormonal cycles, diseases, and medical interventions. Along with the identification of specific bacteria taxa associated with diseases, recent advancements give evidence that metabolism, genetics, and environmental factors can model these microbial effects. However, the current analytic methods for integrating microbiome data are fully developed to address the main challenges of longitudinal metagenomics data, such as high-dimensionality, intra-sample dependence, and zero-inflation of observed counts. Hence, we propose the Bayes factor approach for model selection based on negative binomial, Poisson, zero-inflated negative binomial, and zero-inflated Poisson models with non-informative Jeffreys prior. We find that both in simulation studies and real data analysis, our Bayes factor remarkably outperform traditional Akaike information criterion and Vuong's test. A new R package BFZINBZIP has been introduced to do simulation study and real data analysis to facilitate Bayesian model selection based on the Bayes factor.

stat.ME↗

arXiv2023

Path Integral Control in Infectious Disease Modeling

COVID-19, a global pandemic of unprecedented scale, has had a profound impact on nations worldwide, resulting in the tragic loss of nearly 1.1 million lives in the United States and a staggering 7 million worldwide. In the absence of effective vaccines, governments across the globe resorted to the implementation of lockdown measures as a vital strategy to mitigate the virus's relentless spread. While these restrictions were widely enforced, crucial sectors like public health and safety remained operational, ensuring the continuity of essential services. The timing and stringency of lockdown measures in various U.S. states were intricately tailored to the severity of the outbreak within their respective regions. Lockdowns effectively curtailed social interactions, thereby significantly reducing virus transmission. However, it is essential to strike a balance, as prolonged lockdowns can sow apprehension among the populace, impeding the resumption of normal social activities due to the persistent fear of contracting COVID-19. These prolonged restrictions have reverberated throughout the business landscape, resulting in reduced consumer and employee participation, ultimately denting long-term profitability sustainability. Businesses that lacked the resilience of adequate inventory faced the dire prospect of permanent closure. Regrettably, the absence of substantial government financial support has made business closure an all too common outcome, with the arduous task of revival to former employment levels.

physics.soc-ph↗

arXiv2022

Stochastic Control of a SIR Model with Non-linear Incidence Rate Through Euclidean Path Integral

This paper utilizes a stochastic Susceptible-Infected-recovered (SIR) model with a non-linear incidence rate to perform a detailed mathematical study of optimal lock-down intensity and vaccination rate under the COVID-19 pandemic environment. We use a Feynman-type path integral control approach to determine a Fokker-Plank type equation of this system. Since we assume the availability of information on the COVID-19 pandemic is complete and perfect, we can show a unique fixed point. A non-linear incidence rate is used because, it can be raised from saturation effects that if the proportion of infected agents is very high so that exposure to the pandemic is inevitable, then the transmission rate responds slower than linearity to the increase in the number of infections. The simulation study shows that with higher diffusion coefficients susceptible and recovery curves keep the downward trends while the infection curve becomes ergodic. Finally, we perform a data analysis using UK data at the beginning of 2021 and compare it with our theoretical results.

math.OC↗

arXiv2022

On Lock-down Control of a Pandemic Model

In this paper a Feynman-type path integral control approach is used for a recursive formulation of a health objective function subject to a fatigue dynamics, a forward-looking stochastic multi-risk susceptible-infective-recovered (SIR) model with risk-group's Bayesian opinion dynamics towards vaccination against COVID-19. My main interest lies in solving a minimization of a policy-maker's social cost which depends on some deterministic weight. I obtain an optimal lock-down intensity from a Wick-rotated Schrodinger-type equation which is analogous to a Hamiltonian-Jacobi-Bellman (HJB) equation. My formulation is based on path integral control and dynamic programming tools facilitates the analysis and permits the application of algorithm to obtain numerical solution for pandemic control model. Feynman path integral is a quantization method which uses the quantum Lagrangian function, while Schrodinger's quantization uses the Hamiltonian function. These two methods are believed to be equivalent but, this equivalence has not fully proved mathematically. As the complexity and memory requirements of grid-based partial differential equation (PDE) solvers increase exponentially as the dimension of the system increases, this method becomes impractical in the case with high dimensions. As an alternative path integral control solves a class a stochastic control problems with a Monte Carlo method for a HJB equation and this approach avoids the need of a global grid of the domain of the HJB equation.

math.OC↗

arXiv2021

Scoring a Goal optimally in a Soccer game under Liouville-like quantum gravity action

In this paper we present a new theoretical model to find out an optimal weight associated with a soccer player under the presence of a stochastic goal dynamics by using Feynman path integral method, where the action of a player is on $\sqrt{8/3}$-Liouville Quantum Gravity surface. Before determine the optimal weight we first establish an Infinitary logic which can deal with infinite variables on the strategy space then, a quantum formula of this logic has been developed and finally, based on this we are able show the existence of a Lefschetz-Hopf fixed point of this game. As in a competitive tournament, all possible standard strategies to score goals are known to the opposition team, a player's action is stochastic in nature and they would have some comparative advantage to score goals. Furthermore, conditions like uncertainties due to rain, dribbling and passing skill of a player, whether the match is a day match or a day-night match, advantages of having home crowd and asymmetric information of action profiles have been considered on the way to determine the optimal weight.

math.OC↗

arXiv2021

Consensus as a Nash Equilibrium of a stochastic differential game

In this paper a consensus has been constructed in a social network which is modeled by a stochastic differential game played by agents of that network. Each agent independently minimizes a cost function which represents their motives. A conditionally expected integral cost function has been considered under an agent's opinion filtration. The dynamic cost functional is minimized subject to a stochastic differential opinion dynamics. As opinion dynamics represents an agent's differences of opinion from the others as well as from their previous opinions, random influences and stubbornness make it more volatile. An agent uses their rate of change of opinion at certain time point as a control input. This turns out to be a non-cooperative stochastic differential game which have a feedback Nash equilibrium. A Feynman-type path integral approach has been used to determine an optimal feedback opinion and control. This is a new approach in this literature. Later in this paper an explicit solution of a feedback Nash equilibrium opinion is determined.

math.ST↗

arXiv2021

Optimal Estimation of Brownian Penalized Regression Coefficients

In this paper we introduce a new methodology to determine an optimal coefficient of penalized functional regression. We assume the dependent, independent variables and the regression coefficients are functions of time and error dynamics follow a stochastic differential equation. First we construct our objective function as a time dependent residual sum of square and then minimize it with respect to regression coefficients subject to different error dynamics such as LASSO, group LASSO, fused LASSO and cubic smoothing spline. Then we use Feynman-type path integral approach to determine a Schrödinger-type equation which have the entire information of the system. Using first order conditions with respect to these coefficients give us a closed form solution of them.

stat.ME↗

arXiv2021

Effects of water currents on fish migration through a Feynman-type path integral approach under $\sqrt{8/3}$ Liouville-like quantum gravity surfaces

A stochastic differential game theoretic model has been proposed to determine optimal behavior of a fish while migrating against water currents both in rivers and oceans. Then, a dynamic objective function is maximized subject to two stochastic dynamics, one represents its location and another its relative velocity against water currents. In relative velocity stochastic dynamics, a Cucker-Smale type stochastic differential equation is introduced under white noise. As the information regarding hydrodynamic environment is incomplete and imperfect, a Feynman type path integral under $\sqrt{8/3}$ Liouville-like quantum gravity surface has been introduced to obtain a Wick-rotated Schrödinger type equation to determine an optimal strategy of a fish during its migration. The advantage of having Feynman type path integral is that, it can be used in more generalized nonlinear stochastic differential equations where constructing a Hamiltonian-Jacobi-Bellman (HJB) equation is impossible. The mathematical analytic results show exact expression of an optimal strategy of a fish under imperfect information and uncertainty.

math.OC↗

arXiv2020

Motivation to Run in One-Day Cricket

In this paper we introduce a new methodology to determine an optimal coefficient for a positive finite measure of batting average, strike rate, and bowling average of a player in order to get an optimal score of a team under dynamic modeling using a path integral method. We also introduce new run dynamics modeled as a stochastic differential equation in order to incorporate the average weather conditions at the cricket ground, the weather condition on the day of the match including sudden deterioration which leads to a partial or complete stop of the game, total attendance, and home field advantage.

math.OC↗

arXiv2019

Semicooperation under curved strategy spacetime

Mutually beneficial cooperation is a common part of economic systems as firms in partial cooperation with others can often make a higher sustainable profit. Though cooperative games were popular in 1950s, recent interest in non-cooperative games is prevalent despite the fact that cooperative bargaining seems to be more useful in economic and political applications. In this paper we assume that the strategy space and time are inseparable with respect to a contract. Under this assumption we show that the strategy spacetime is a dynamic curved Liouville-like 2-brane quantum gravity surface under asymmetric information and that traditional Euclidean geometry fails to give a proper feedback Nash equilibrium. Cooperation occurs when two firms' strategies fall into each other's influence curvature in this strategy spacetime. Small firms in an economy dominated by large firms are subject to the influence of large firms. We determine an optimal feedback semi-cooperation of the small firm in this case using a Liouville-Feynman path integral method.

math.OC↗