MASTERS
STATISTICS PROJECT TOPICS AND MATERIALS
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MASTERS
STATISTICS PROJECT TOPICS AND MATERIALS
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
PDF
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.
HOW TO RECEIVE PROJECT MATERIAL(S)
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amount (#5,000) into our bank Account below, send the following information to
08068231953 or 08168759420
(1) Your
project topics
(2) Email
Address
(3)
Payment Name
(4) Teller
Number
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CHUKWUDI
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Bank: GTBank.
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CHUKWUDI
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Bank: FIRST BANK
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