Browsing by Author "Sujit Ghosh, Member"

Now showing 1 - 20 of 43

Acoustics Detection Systems for Wearables in Healthcare
(2024-08-12) Chen, Yuhan; Edgar Lobaton, Chair; Sujit Ghosh, Member; Tianfu Wu, Member; Alper Bozkurt, Member
Advanced Methods in Bayesian Variable Selection and Causal Inference.
(2021-07-27) Cui, Can; Brian Reich, Chair; Sujit Ghosh, Member; Ana-Maria Staicu, Member; Joshua Gray, Member; Shu Yang, Member
Advances in Bayesian Inference for Species Divergence Times.
(2014-08-18) Lee, Hui-Jie; Jeffrey Thorne, Chair; Eric Stone, Member; Sujit Ghosh, Member; Brian Wiegmann, Member
Advances in Nonparametric Bayesian Methods for Clustering and Classification.
(2014-01-27) Kao, Yimin; Brian Reich, Chair; Sujit Ghosh, Member; Yichao Wu, Member; Howard Bondell, Member; Grady Miller, Graduate School Representative
Advances in Semiparametric Quantile Regression.
(2022-07-22) Xu, Steven Guanxing; Brian Reich, Co-Chair; Shu Yang, Co-Chair; Ana-Maria Staicu, Member; Sujit Ghosh, Member; Jacqueline Hughes-Oliver, Member; Chengying Xu, Graduate School Representative
Agricultural Commodity Prices.
(2021-11-30) Thompson, Robert S; Barry Goodwin, Co-Chair; Nicholas Piggott, Co-Chair; Heidi Schweizer, Member; Sujit Ghosh, Member
Bayesian Analysis of Dynamic Times Series and High-dimensional Models with Their Applications.
(2018-07-10) Ning, Bo; Subhashis Ghoshal, Co-Chair; Peter Bloomfield, Co-Chair; Kazimierz Borkowski, Graduate School Representative; Sujit Ghosh, Member; David Dickey, Member; Eric Chi, Member
Bayesian Classification and Change Point Detection for Functional Data.
(2018-08-09) Li, Xiuqi; Subhashis Ghoshal, Chair; Negash Medhin, Member; Sujit Ghosh, Member; Arnab Maity, Member
Bayesian Inference of Stochastic Volatility Models and Applications in Risk Management.
(2011-11-28) Liu, Ye; Peter Bloomfield, Chair; Sujit Ghosh, Member; David Dickey, Member; Min Kang, Member
Bayesian Inference on Multivariate Median and Quantiles.
(2020-06-09) Bhattacharya, Indrabati; Subhashis Ghoshal, Chair; Sujit Ghosh, Member; Ryan Martin, Member; Steven Hunter, Graduate School Representative; Minh Tang, Member
Bayesian Inference Under Shape Constraints.
(2019-05-13) Chakraborty, Moumita; Subhashis Ghoshal, Chair; Soumendra Lahiri, Member; Sujit Ghosh, Member; Ryan Martin, Member; Zheng Li, Graduate School Representative
Bayesian Methodologies for the Spatial Spread of Disease.
(2022-08-05) Trostle, John Parker; Gustavo Machado, Co-Chair; Brian Reich, Co-Chair; Sujit Ghosh, Member; Kevin Gross, Member; Andrew Papanicolaou, Graduate School Representative
Bivariate Contours for Censored Data.
(2011-11-03) Mathias, Jamila; John Monahan, Co-Chair; Huixia Wang, Co-Chair; Howard Bondell, Member; Sujit Ghosh, Member; Jerry Davis, Graduate School Representative
Calibration of Numerical Model Output using Nonparametric Spatial Density Functions.
(2012-03-19) Zhou, Jingwen; Montserrat Fuentes, Chair; Brian Reich, Member; Sujit Ghosh, Member; Frederick Bingham, External; Jerry Davis, Member
Climate, Streamflow and Nutrient Variability over the Southeast United States.
(2011-06-23) Oh, Jeseung; Sankarasubramanian Arumugam, Chair; Sanmugavadivel Ranjithan, Member; Detlef Knappe, Member; Sujit Ghosh, Member
Comparing Bayesian, Maximum Likelihood and Classical Estimates for the Jolly-Seber Model
(2001-05-30) Brown, George Gordon Jr.; John Monahan, Co-Chair; Ken Pollock, Co-Chair; Roger Berger, Member; Sujit Ghosh, Member
In 1965 Jolly and Seber proposed a model to analyze data for open population capture-recapture studies. Despite frequent use of the Jolly-Seber model, likelihood-based inference is complicated by the presence of a number of unobservable variables that cannot be easily integrated from the likelihood. In order to avoid integration, various statistical methods have been employed to obtain meaningful parameter estimates. Conditional maximum likelihood, suggested by both Jolly and Seber, has become the standard method. Two new parameter estimation methods, applied to the Jolly-Seber Model D, are presented in this thesis. The first new method attempts to obtain maximum likelihood estimates after integrating all of the unobservable variables from the Jolly-Seber Model D likelihood. Most of the unobservable variables can be analytically integrated from the likelihood. However, the variables dealing with the abundance of uncaptured individuals must be numerically integrated. A FORTRAN program was constructed to perform the numerical integration and search for MLEs using a combination of fixed quadrature and Newton's method. Since numerical integration tends to be very time consuming, MLEs could only be obtained from capture-recapture studies with a small number of sampling periods. In order to test the validity of the MLE, a simulation experiment was conducted that obtained MLEs from simulated data for a wide variety of parameter values. Variance estimates for these MLEs were obtained using the Chapman-Robbins lower bound. These variances estimates were used to construct 90% confidence intervals with approximately correct coverage. However, in cases with few recaptures the MLEs performed poorly. In general, the MLEs tended to perform well on a wide variety of the simulated data sets and appears to be a valid tool for estimating population characteristics for open populations. The second new method employs the Gibbs sampler on an unintegrated and an integrated version of the Jolly-Seber Model D likelihood. For both version full conditional distributions are easily obtained for all parameters of interest. However, sampling from these distributions is non-trivial. Two FORTRAN programs were developed to run the Gibbs sampler for the unintegrated and the integrated likelihoods respectively. Means, medians, modes and variances were constructed from the resulting empirical posterior distributions and used for inference. Spectral density was used to construct a variance estimate for the posterior mean. Equal-tailed posterior density regions were directly calculated from the posteriors distributions. A simulation experiment was conducted to test the validity of density regions. These density regions also have approximately the proper coverage provided that the capture probability is not too small. Convergence to a stationary distribution is explored for both version of the likelihood. Often, convergence was difficult to detect, therefore a test of convergence was constructed by comparing two independent chains from both version of the Gibbs sampler. Finally, an experiment was constructed to compare these two new methods and the traditional conditional maximum likelihood estimates using data simulated from a capture-recapture experiment with 4 sampling periods. This experiment showed that there is little difference between the conditional maximum likelihood estimates and the 'true' maximum likelihood estimates when the population size is large. A second simulation experiment was conducted to determine which of the 3 estimation methods provided the 'best' estimators. This experiment was largely inconclusive as no single method routinely outperformed the others.
Context Models for Physiological Response Under Signal Quality.
(2020-05-08) Gonzalez, Laura Lucia; Edgar Lobaton, Chair; Wesley Snyder, Member; Cranos Williams, Member; Anya McGuirk, External; Sujit Ghosh, Member
Enabling Machine Learning Tasks in Wearable Cyber-Physical Systems through Uncertainty Quantification and Signal Processing.
(2021-11-19) da Silva, Rafael Luiz; Edgar Lobaton, Chair; Tianfu Wu, Member; Cranos Williams, Member; Sujit Ghosh, Member
Essays on the Effect of GATT/WTO and Financial Crisis on International Trade
(2015-06-19) Cho, Moon Hee; Xiaoyong Zheng, Co-Chair; Ivan Kandilov, Co-Chair; Sujit Ghosh, Member; Kathryn Boys, Member
Estimation of Regression Coefficients in the Competing Risks Model with Missing Cause of Failure
(2002-03-13) Lu, Kaifeng; Anastasios A. Tsiatis, Chair; Marie Davidian, Member; Sujit Ghosh, Member; John F. Monahan, Member
In many clinical studies, researchers are interested in theeffects of a set of prognostic factors on the hazard of death from a specific disease even though patients may die from other competing causes. Often the time to relapse is right-censored for some individuals due to incomplete follow-up. In some circumstances, it may also be the case that patients are known to die but the cause of death is unavailable. When cause of failure is missing, excluding the missing observations from the analysis or treating them as censored may yield biased estimates and erroneous inferences. Under the assumption that cause of failure is missing at random, we propose three approaches to estimate the regression coefficients. The imputation approach isstraightforward to implement and allows for the inclusion ofauxiliary covariates, which are not of inherent interest formodeling the cause-specific hazard of interest but may be related to the missing data mechanism. The partial likelihood approach we propose is semiparametric efficient and allows for more general relationships between the two cause-specific hazards and more general missingness mechanism than the partial likelihood approach used by others. The inverse probability weighting approach isdoubly robust and highly efficient and also allows for theincorporation of auxiliary covariates. Using martingale theory and semiparametric theory for missing data problems, the asymptotic properties of these estimators are developed and the semiparametric efficiency of relevant estimators is proved. Simulation studies are carried out to assess the performance of these estimators in finite samples. The approaches are also illustrated using the data from a clinical trial in elderly women with stage II breast cancer. The inverse probability weighted doubly robust semiparametric estimator is recommended for itssimplicity, flexibility, robustness and high efficiency.