Afshin Motavali

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Assignment 12-June-2022
Assignment 11-June-2022
Analysis of Salaries of Professors using Regression Analysis
The Salaries data were collected as part of the on-going effort of the college's administration to monitor salary differences between male and female faculty members. In this report, I analyzed the data to see what important factors affect the professors' salaries in the United States. All the readers can reach me in case they have any kind of questions about the analysis.
Bayesian empirical likelihood for ridge and lasso regressions
Ridge and lasso regression models, which are also known as regularization methods, are widely used methods in machine learning and inverse problems that introduce additional information to solve ill-posed problems and/or perform feature selection.The ridge and lasso estimates for linear regression parameters can be interpreted as Bayesian posterior estimates when the regression parameters have Normal and independent Laplace (i.e., double-exponential) priors, respectively. A significant challenge in regularization problems is that these approaches assume that data are normally distributed, which makes them not robust to model misspecification. A Bayesian approach for ridge and lasso models based on empirical likelihood is proposed. This method is semiparametric because it combines a nonparametric model and a parametric model. Hence, problems with model misspecification are avoided. Under the Bayesian empirical likelihood approach, the resulting posterior distribution lacks a closed form and has nonconvex support, which makes the implementation of traditional Markov chain Monte Carlo (MCMC) methods such as Gibbs sampling and Metropolis–Hastings very challenging. To solve the nonconvex optimization and nonconvergence problems, the tailored Metropolis–Hastings approach is implemented. The asymptotic Bayesian credible intervals are derived.
Assignment April 13
Clustering faithful data using EM algorithm
In this document, you will learn how to use normal mixture distribution and the EM algorithm in order to cluster a real data set. Here you can do it by some simple R codes.
Complicated Star in R
One of the best ways to learn digital art in R is to focus on geometrical shapes that could be visualized in R
Top Movies in visualisation in R
Visualization of top 10 IMDB movies using color-palettes inspired by some birds living in Australia.
هندسه در R
Irritated Geometry in R
The Dance of the Planets