PhD THESIS TOPICS AND MATERIALS IN STATISTICS

 PhD THESIS TOPICS AND MATERIALS IN STATISTICS

 

 

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PhD THESIS TOPICS AND MATERIALS IN STATISTICS

 

1. Bayesian hierarchical modeling for the forensic evaluation of handwritten documents

 

2. Factor models for big data

 

3. 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

 

6. Interaction forward selection in ultra-high-dimensional functional linear models

 

7. A framework for statistical and computational reproducibility in large-scale data analysis projects with a focus on automated forensic bullet evidence comparison.

 

8. High-dimensional time series analysis and its application in economic forecasting

 

9. Model estimation, identification and inference for next-generation functional data and spatial data

 

10. Nowcasting GDP using dynamic factor model: A Bayesian approach

 

11. In-silico guided identification of ciliogenesis candidate genes in a non-conventional animal model

 

12. 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

 

13. Statistical methods for ChIP-seq and microbiome studies using next-generation DNA sequencing data

 

14. Statistical causal inference methods and spatio-temporal modeling for animal and human health data

 

15. Incorporating multi-scale structures and physiological processes into the modeling of animal movement

 

16. Assessing and accounting for correlation in RNA-seq data analysis

 

17. Spatially varying coefficient models: Theory and methods

 

18. Bayesian hierarchical modeling for disease outbreaks

 

19. Statistical methods for gene expression studies using next-generation sequencing experiments.

 

20. Self-exciting spatio-temporal statistical models for count data with applications to modeling the spread of violence

 

21. State space models for partially observed biological and agricultural data

 

22. Developments in MCMC diagnostics and sparse Bayesian learning models, Anand Ulhas Dixit

 

 

23. Choosing cutoff values for correlated continuous diagnostic data to estimate sensitivity and specificity

 

24. Leveraging genetic time series data to improve detection of natural selection

 

25. Modeling crop phenology using remotely sensed data

 

26. Non/Semi-parametric learning from data with complex features

 

27. Multiple hypothesis testing and RNA-seq differential expression analysis accounting for dependence and relevant covariates

 

28. Survey data integration using mass imputation

 

29. Learning algorithms for forensic science applications

 

30. Penalized b-splines and their application with an in depth look at the bivariate tensor product penalized b-spline

 

31. Some Bayesian methods for univariate density estimation

 

32. Visualization methods for genealogical and RNA-sequencing studies: Pertinence, software, and applications, Lindsay Rutter

 

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Random forest robustness, variable importance, and tree aggregation, Andrew Sage

 

33. Approximate Bayesian approaches and semiparametric methods for handling missing data

 

34. Selection and assessment of bivariate Markov random field models

 

35. Statistical methods for microbiome data and antimicrobial resistance analysis

 

36. Stratification for area frame surveys with multiple estimation goals

 

37. Some contributions to k-means clustering problems

 

38. Bayesian analysis of high-dimensional count data

 

39. Local Polynomial Kernel Smoothing with Correlated Errors

 

40. Nonlinear models with measurement error: Application to vitamin D

 

41. Bagged projection methods for supervised classification in big data

 

42. Accounting for structure in education assessment data using hierarchical models

 

43. Forensic tool mark comparisons: Tests for the null hypothesis of different sources

 

44. Statistical methods for bullet matching

 

45. Methods for analysis and uncertainty quantification for processes recorded through sequences of images
46. On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling

 

47. Bayesian inference of virus evolutionary models from next-generation sequencing data

 

48. Statistical methods for estimation, testing, and clustering with gene expression data

 

49. Extending removal and distance-removal models for abundance estimation by modeling detections in continuous time

 

50. Applications of Bayesian hierarchical models in gene expression and product reliability

 

51. Mixture model and subgroup analysis in nationwide kidney transplant center evaluation

 

52. Measurement error modeling of physical activity data

 

53. Statistical methods in modeling disease surveillance data with misclassification

 

54. Nonparametric regression models with and without measurement error in the covariates, for univariate and vector responses: a Bayesian approach

 

55. Graphical discovery in stochastic actor-oriented models for social network analysis.

 

 

56. Exploring dependence in binary Markov random field models

 

57. Kernel deconvolution density estimation.

 

58. Bayesian contributions to the modeling of multivariate macroeconomic data

 

59. Evaluation of Parametric and Nonparametric Statistical Methods in Genomic Prediction

 

60. High-dimensional hierarchical models and massively parallel computing.

 

61. Statistical methods in sports with a focus on win probability and performance evaluation.

 

62. Bayesian models and inferential methods for forecasting disease outbreak severity

 

63. Interfacing R with Web Technologies for Interactive Statistical Graphics and Computing with Data

 

64. Probabilistic methods for quality improvement in high-throughput sequencing data

 

65. Inference based on data from superpositions of identical renewal processes.

 

66. Interactive visualization for missing values, time series, and areal data.

 

67. Small area prediction based on unit level models when the covariate mean is measured with error.

 

68. Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework

 

69. Some methods for handling missing data in surveys

 

70. Local prediction and classification techniques for machine learning and data mining

 

71. Statistical methods in detecting differential expressed genes, analyzing insertion tolerance for genes and group selection for survival data.

 

72. Experimental designs for multiple responses with different models.

 

73. Applications of technology and large data in statistics education and statistical graphics.

 

74. Applications of and extensions to state-space models

 

75. Computer model optimization within hidden constraints

 

76. Bayesian modeling and computation with latent variables.

 

77. Perception in statistical graphics

 

78. An investigation of viral fitness using statistical and computer models of Equine Infectious Anemia Virus infection.

 

79. A local structure graph model for network analysis

 

80. Imputation of missing values using quantile regression

 

81. Modeling, inference and clustering for equivalence classes of 3-D orientations

 

82. Mixed effects modeling with missing data using quantile regression and joint modelling.

 

 

83. Characterizing diurnal and interannual variability in the atmosphere through physical and stochastic models.

 

84. Contributions to the design and analysis of nondestructive evaluation experiments.

 

 

 

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