arXiv subjects

Chris Tsokos

Publications and source records attributed to Chris Tsokos.

5 recordsLinked to original sources

arXiv2025

A Real Data-Driven, Robust Survival Analysis on Patients who Underwent Deep Brain Stimulation for Parkinson's Disease by Utilizing Parametric, Non-Parametric, and Semi-Parametric Approaches

Parkinson's Disease (PD) is a devastating neurodegenerative disorder that affects millions of people around the globe. Many researchers are continuously working to understand PD and develop treatments to improve the condition of PD patients, which affects their day-to-day lives. Since the last decades, the treatment, Deep Brain Stimulation (DBS) has given promising results for motor symptoms by improving the quality of daily living of PD patients. In the methodology of the present study, we have utilized sophisticated statistical approaches such as Nonparametric, Semi-parametric, and robust Parametric survival analysis to extract useful and important information about the long-term survival outcomes of the patients who underwent DBS for PD. Finally, we were able to conclude that the probabilistic behavior of the survival time of female patients is statistically different from that of male patients. Furthermore, we have identified that the probabilistic behavior of the survival times of Female patients is characterized by the 3-parameter Lognormal distribution, while that of Male patients is characterized by the 3-parameter Weibull distribution. More importantly, we have found that the Female patients have higher survival compared to the Male patients after conducting a robust parametric survival analysis. Using the semi-parametric COX-PH, we found that the initial implant of the right side leads to a high frequency of events occurring for the female patients with a bad prognostic factor, while for the male patients, a low events occurs with a good prognostic factor. Furthermore, we have found an interaction term between the number of revisions and the initial size of the implant, which increases the frequency of events occurring for the Male patients with a bad prognostic factor.

stat.AP↗

arXiv2018

Bayesian Modeling of Nonlinear Poisson Regression with Artificial Neural Networks

Being in the era of big data, modeling and prediction of count data have become significantly important in many fields including health, finance, social, etc. Although linear Poisson regression has been widely used to model count and rate data, it might not be always suitable as it cannot capture some inherent variability within complex data. In this study, we introduce a probabilistically driven nonlinear Poisson regression model with Bayesian artificial neural networks (ANN) to model count or rate data. This new nonlinear Poisson regression model developed with Bayesian ANN provides higher prediction accuracies over traditional Poisson or negative binomial regression models as revealed in our simulation and real data studies.

stat.AP↗

arXiv2013

Contributors of carbon dioxide in the atmosphere in Europe: the surface response analysis

This paper is a continuation of the statistical modeling of the nonlinear relationship between atmospheric CO2 and attributable variables that can account for emissions, based on data from EU countries, in order to compare the relevant findings to those obtained in the case of US data, in [1, 2]. The current study was initiated in [3], leading to the optimal second-order model, based on three linear terms and five second-order terms. We conclude this study in the present work, by finding the canonical decomposition of the nonlinear model, and by computing the specific two-dimensional confidence regions that it leads to. We then use the model in order to quantify the net effect of various risk factors, and compare to the results obtained in the US case.

stat.AP↗

arXiv2013

Surface response analysis and determination of confidence regions for atmospheric CO2: a global warming study for U.S.A. data

Starting from the atmospheric CO2 measurements taken in Hawaii between 1959 and 2008, a quadratic model with interactions was fitted, using 5 attributable variables. Surface response analysis returned the eigenvalues and eigenvectors at the critical point, which turns out to be of mixed type, with two positive eigenvalues, one null, and the rest negative. From these data, it is derived that the confidence regions in two variables are of various types (elliptic, hyperbolic, and degenerate). Based on these results we indicate how to determine two-dimensional confidence regions for statistically-significant variables which are relevant contributors to the atmospheric CO2 emissions.

stat.CO↗

arXiv2013

Contributors of carbon dioxide in the atmosphere in Europe

Carbon dioxide, along with atmospheric temperature are interacting to cause what we have defined as global warming. In the present study we develop a statistical model using real data to identify the attributable variables (risk factors) that cause the CO2 emissions in the atmosphere in Europe. Some scientists believe that there are more than nineteen attributable variables that cause the CO2 in our atmosphere. However, our study has identified only three individual risk factors and five interactions among the attributable variables that cause almost all the CO2 emissions in the atmosphere in Europe. We rank the risk factors and interactions according to the amount of CO2 they generate. In addition, we compare the present findings of the European data with a similar study for the Continental United States [1, 2]. For example, in the US, liquid fuels ranks number one, while in Europe is gas fuels. In fact, liquid fuels in Europe is the least contributable variable of CO2 in the atmosphere, and gas fuels ranks seventh.

stat.CO↗