MASTERS THESIS TOPICS AND MATERIALS IN STATISTICS
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PhD THESIS TOPICS AND MATERIALS IN STATISTICS
- Bayesian hierarchical modeling for the forensic evaluation of handwritten documents
- Factor models for big data
- Score-based likelihood ratios and sparse Gaussian processes
4.Shape-restricted random forests and semiparametric prediction intervals
5.Small area prediction and big data visualization: Analysis of soil losses from sheet and rill erosion
- Interaction forward selection in ultra-high-dimensional functional linear models
- A framework for statistical and computational reproducibility in large-scale data analysis projects with a focus on automated forensic bullet evidence comparison.
- High-dimensional time series analysis and its application in economic forecasting
- Model estimation, identification and inference for next-generation functional data and spatial data
- Nowcasting GDP using dynamic factor model: A Bayesian approach
- In-silico guided identification of ciliogenesis candidate genes in a non-conventional animal model
- Improving reliability in the wind energy industry via field failure predictions based on life, maintenance, and dynamic data from supervisory control and data acquisition systems
- Statistical methods for ChIP-seq and microbiome studies using next-generation DNA sequencing data
- Statistical causal inference methods and spatio-temporal modeling for animal and human health data
- Incorporating multi-scale structures and physiological processes into the modeling of animal movement
- Assessing and accounting for correlation in RNA-seq data analysis
- Spatially varying coefficient models: Theory and methods
- Bayesian hierarchical modeling for disease outbreaks
- Statistical methods for gene expression studies using next-generation sequencing experiments.
- Self-exciting spatio-temporal statistical models for count data with applications to modeling the spread of violence
- State space models for partially observed biological and agricultural data
- Developments in MCMC diagnostics and sparse Bayesian learning models, Anand Ulhas Dixit
- Choosing cutoff values for correlated continuous diagnostic data to estimate sensitivity and specificity
- Leveraging genetic time series data to improve detection of natural selection
- Modeling crop phenology using remotely sensed data
- Non/Semi-parametric learning from data with complex features
- Multiple hypothesis testing and RNA-seq differential expression analysis accounting for dependence and relevant covariates
- Survey data integration using mass imputation
- Learning algorithms for forensic science applications
- Penalized b-splines and their application with an in depth look at the bivariate tensor product penalized b-spline
- Some Bayesian methods for univariate density estimation
- Visualization methods for genealogical and RNA-sequencing studies: Pertinence, software, and applications, Lindsay Rutter
Random forest robustness, variable importance, and tree aggregation, Andrew Sage
- Approximate Bayesian approaches and semiparametric methods for handling missing data
- Selection and assessment of bivariate Markov random field models
- Statistical methods for microbiome data and antimicrobial resistance analysis
- Stratification for area frame surveys with multiple estimation goals
- Some contributions to k-means clustering problems
- Bayesian analysis of high-dimensional count data
- Local Polynomial Kernel Smoothing with Correlated Errors
- Nonlinear models with measurement error: Application to vitamin D
- Bagged projection methods for supervised classification in big data
- Accounting for structure in education assessment data using hierarchical models
- Forensic tool mark comparisons: Tests for the null hypothesis of different sources
- Statistical methods for bullet matching
- Methods for analysis and uncertainty quantification for processes recorded through sequences of images
- On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling
- Bayesian inference of virus evolutionary models from next-generation sequencing data
- Statistical methods for estimation, testing, and clustering with gene expression data
- Extending removal and distance-removal models for abundance estimation by modeling detections in continuous time
- Applications of Bayesian hierarchical models in gene expression and product reliability
- Mixture model and subgroup analysis in nationwide kidney transplant center evaluation
- Measurement error modeling of physical activity data
- Statistical methods in modeling disease surveillance data with misclassification
- Nonparametric regression models with and without measurement error in the covariates, for univariate and vector responses: a Bayesian approach
- Graphical discovery in stochastic actor-oriented models for social network analysis.
- Exploring dependence in binary Markov random field models
- Kernel deconvolution density estimation.
- Bayesian contributions to the modeling of multivariate macroeconomic data
- Evaluation of Parametric and Nonparametric Statistical Methods in Genomic Prediction
- High-dimensional hierarchical models and massively parallel computing.
- Statistical methods in sports with a focus on win probability and performance evaluation.
- Bayesian models and inferential methods for forecasting disease outbreak severity
- Interfacing R with Web Technologies for Interactive Statistical Graphics and Computing with Data
- Probabilistic methods for quality improvement in high-throughput sequencing data
- Inference based on data from superpositions of identical renewal processes.
- Interactive visualization for missing values, time series, and areal data.
- Small area prediction based on unit level models when the covariate mean is measured with error.
- Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework
- Some methods for handling missing data in surveys
- Local prediction and classification techniques for machine learning and data mining
- Statistical methods in detecting differential expressed genes, analyzing insertion tolerance for genes and group selection for survival data.
- Experimental designs for multiple responses with different models.
- Applications of technology and large data in statistics education and statistical graphics.
- Applications of and extensions to state-space models
- Computer model optimization within hidden constraints
- Bayesian modeling and computation with latent variables.
- Perception in statistical graphics
- An investigation of viral fitness using statistical and computer models of Equine Infectious Anemia Virus infection.
- A local structure graph model for network analysis
- Imputation of missing values using quantile regression
- Modeling, inference and clustering for equivalence classes of 3-D orientations
- Mixed effects modeling with missing data using quantile regression and joint modelling.
- Characterizing diurnal and interannual variability in the atmosphere through physical and stochastic models.
- Contributions to the design and analysis of nondestructive evaluation experiments.
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