Developments in Statistical Methods
Development in statistical methods consists of Monte Carlo Simulation, Markov Chains and Bayesian Methods etc. These are modern concepts in statistics and can be complex at times. Our talented pool of Statistics experts, Statistics assignment tutors and Statistics homework tutors provide you the detailed and simplified solution for all your needs related to Development in statistical methods. Our Statistics homework/assignment help section has been designed to guide you through all your homework, assignment, term paper and dissertation problems.
Following is the list of comprehensive topics in which we offer the quality solutions:
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- Elements of statistical learning: global fitting versus local fitting, linear methods for regression, splines, kernel methods and local likelihood
- Robustness of likelihood approaches: distance between working model and “truth”, maximum likelihood under wrong models, quasi-mle, model selection, robust estimation. Empirical likelihood: empirical likelihood of mean
- Model assessment and selection: bias-variance trade-off, effective number of parameters, bic, cross-validation. Further topics: additive models, varying-coefficient linear models, boosting, neural network, support vector machines
- Bayesian methods and Markov chain Monte Carlo (mcmc) basic bayes, Gibbs sampler, and Metropolis-Hastings algorithm.
- Computational Biology And Bioinformatics
- Factor Analysis