University of Pennsylvania (PhD)
In addition to a PhD in Statistics and a master’s level degree in Finance from the University of Pennsylvania, I have years of teaching and tutoring experience. I help with exams, assignments, research, dissertation defense, analytics and so on. I cover all the areas of Statistics and Quantitative Finance, including probability theory, stochastic processes, large sample and small sample theory, Bayesian statistics, Monte Carlo, parametric and nonparametric tests, linear regression, generalized linear models, panel data models, (multivariate) analysis of variance, machine learning, data compression and factor analysis, time series analysis, actuarial methods, pricing financial derivatives, investment strategies and portfolio optimization, portfolio risk analysis and stress-testing.
I work with the following statistical packages: R, Stata, Matlab, SPSS, SAS, Minitab, EViews and Excel. In my opinion, some packages are better suited for flexible programming and some packages are better suited for standard statistical analyses (like principal component analysis or mixed effects models). But I am proficient in all of the listed packages and work in all of them if necessary.
Tutoring is provided on the hourly basis while complete analysis via e-mail is provided for a fixed, pre-specified fee. The effective hourly rate increases with the value of the material, with the rate listed on my profile corresponding to college level material. Please contact me with a syllabus or project description for the exact prices... Typically, I am able to schedule a session on a 1-3 day notice. The turn-around on projects ranges from 1 day to 2 months, depending on their size. In addition to a PhD in Statistics and a master’s level degree in Finance from the University of Pennsylvania, I have years of teaching and tutoring experience. I help with exams, assignments, research, dissertation defense, analytics and so on. I cover all the areas of Statistics and Quantitative Finance, including probability theory, stochastic
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I have worked in SAS since 2010. I have done that only for my clients because, in my humble opinion, this is not the best language for expressing original statistical ideas. It is pretty decent for running standard statistical analyses involving regression, shrinkage methods, generalized linear models, panel data, classification, cluster analysis, data compression, time series analysis, Monte Carlo, neural nets, etc. And that is what I used SAS for. I perform complete analysis for my clients if necessary. I also schedule tutorials, where we go over various SAS functionality slowly and steadily.
I have worked in Stata since 2009, using the package for tutorials and my own needs. I like Stata for its treatment of survey data, extensive robust estimation capabilities and flexibility for building mixed effects models for panel data. I also use Stata regularly for missing data imputation, data management, linear and non-linear regression, survival analysis, simultaneous equations and such. I am happy to help with whatever assignments you are working on, either on the tutorials basis or on the project basis.