Statistical & Financial Consulting by Stanford PhD

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I am a professional offering tutoring services in the fields of applied statistics, theoretical statistics, probability theory, stochastic processes, econometrics, biostatistics, actuarial science and mathematical finance. I hold a PhD in Statistics and a PhD Minor in Finance from Stanford University. During my five years at Stanford I taught more than ten undergraduate, master's level and PhD level statistics and probability courses. After graduation I moved to New York and, for more than a decade, I have been working in the industry focusing on projects in data mining, time series analysis, factor analysis, computational statistics, stochastic optimization, derivatives pricing and systematic trading. Equally importantly, I have tutored students and business professionals in all areas of statistics and quantitative finance for the last eight years. I have consulted researchers in medical, social and economic sciences regarding statistical aspects of their research for ten years.

I help with exams, assignments, presentations, literature review, theoretical research, programming projects and data analysis in any of the major statistical packages (R / R Studio, Stata, SPSS, Matlab, SAS, JMP, Minitab, EViews, Python). As my experience shows, I work with all types of clients, from college students to full professors in other fields. Typically, I meet in Manhattan or tutor via Skype or e-mail if the client is far from New York. In Skype there is an option allowing one to see my desktop. One can see which buttons I click, where my mouse moves, which commands I type, how I perform data analysis, how I derive formulas, and so on. In the past I have tutored clients in New York, Long Island, Boston, Philadelphia, Baltimore, Washington, Pittsburgh, Chicago, Orlando, Miami, Austin, Houston, Dallas, Phoenix, San Diego, Los Angeles, San Jose, San Francisco, Seattle, Vancouver, Montreal, Toronto, London, Cambridge, Edinburgh, Utrecht, Doha, Singapore, Perth, Adelaide, Melbourne, Sydney, Brisbane and so on.

**TYPES OF SERVICE**

**1] Tutoring on the Hourly Basis **

This includes preparation for exams and presentations, work on assignments, coaching for extended business related projects, sessions to improve overall understanding of the material, and so on. The minimum duration of a session is 2 hours.

2] Doing a Project for a Fixed Fee

This includes data analysis, assignments, review of articles and books, theoretical research, development of infrastructure for business and academic needs, answers to specific questions via e-mail, and so on. Please make sure to check out examples of the projects I have done in the past.

3] Dissertation Assistance

This is a mixture of services 1 and 2 spanning months, typically. I help you rephrase research objectives and choose appropriate statistical methodology. I perform the data analysis in the software of your choice: R, Matlab, SPSS, Stata, SAS, JMP, EViews, Minitab or Python. Alternatively, I guide you through performing the analysis yourself and help you interpret the results. I navigate your correspondence with the scientific adviser to make sure that he understands what you have done, you understand what he wants and he gives you only the tasks / ideas that you can handle. I prepare you for each public appearance, including the dissertation defense... You may want to read the dissertation tips.

4] Designing A Course in a Given Area

I prepare study materials, homework problems and data-driven projects for you. You read the materials and do the projects on your own timing. After that, we meet face to face or in Skype. I check the results of your work and answer the list of questions you have prepared. I explain how to approach several selected tasks. After that, I give you a new batch of study materials and homework problems. The cycle repeats itself... This option allows you to improve your knowledge relatively cheaply, as you are not paying for the time when I am not around.

More often than not the tutoring rate is

Below I am

Start with browsing my descriptions of various statistical methods (the first link below). Do not read too much into it and do not spend much time on it. You will not understand 50% of the descriptions but they will start painting the big picture of how much is to be learned and why all of it is necessary. Then do a couple of elementary tutorials (the second group of links below) just to shift your mind into the proper gear and start learning. If you like, you can do some of the more advanced tutorials as well. This will improve your understanding of what's waiting for you ahead. But please be reasonable with your time. For example, diving into any statistical programming language too deeply is pointless until you have learned what all those statistical commands are about. Finally, do it right and start reading the books I am listing below. Do practice problems in most of them. The books are split by blocks and it is best to read the blocks in the following order: Undergraduate Probability → Undergraduate Statistics → Advanced Probability & Stochastic Processes → Advanced Statistics, Biostatistics & Econometrics → Data Mining → one or more applied areas (Actuarial Science, Quantitative Finance, Statistical Genetics, etc) → selected optimization methods suitable for the problems in the chosen applied area(s).

In each block try to read the books in the order presented but use your judgement, of course. Feel free to skip the material you already know. If for a specific book I indicate the chapters to read, read only those chapters. If I am not saying much, I am implying that either the whole book or a big part of it might be useful. If I am saying something like "read the whole book" or "the whole book is important", try reading it all and in the worst case scenario settle for 80%. In fact, going through a key reference twice might be more important than going through something else for the first time.

- Discrete Probability Tutorial, Khan Academy
- Elementary Statistics Tutorial, Star Trek
- R, Matlab and SAS Tutorials, Tutorials Point
- Introductory Tutorial on Stata, UCLA
- Bayesian Statistics Tutorial Including Stata Commands, Stata Corporation
- EViews Tutorial, IHS Global
- Introductory Tutorial on Minitab
- Python Tutorial, Python Software Foundation
- Machine Learning Tutorial, R2D3.us
- Course on Deep Learning, Stanford University

Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed). New York: W. W. Norton & Company. -

Connoly, S. (2015). College Statistics Made Easy. Algebra Publishing Higher Education. -

Lehmann, E. L., & Casella, G. (1998). Theory of Point Estimation (2nd ed). New York: Springer. -

Lehmann, E. L., & Romano, J. P. (2006). Testing Statistical Hypotheses (corrected 2nd printing of the 3rd ed). New York: Springer. -

Greene, W. H. (2011). Econometric Analysis (7th ed). Upper Saddle River, NJ: Prentice Hall. -

Brockwell, P. J., & Davis, R. A. (1991). Time Series: Theory and Methods (2nd ed). New York: Springer.

Agresti, A. (2002). Categorical Data Analysis. New York: Wiley-Interscience. -

Weerahandi, S. (2004). Generalized Inference in Repeated Measures: Exact Methods in MANOVA and Mixed Models. Wiley-Interscience, Hoboken, New Jersey.

Gibbons, J. D., & Chakraborti, S. (2003). Nonparametric Statistical Inference (4th ed). New York: Marcel Dekker.

Lee, E. T., & Wang, J. W. (2003). Statistical Methods for Survival Data Analysis (3rd ed). Wiley-Interscience, Hoboken, New Jersey.

Robert, C. P., & Casella, G. (2004). Monte Carlo Statistical Methods (2nd ed.). New York: Springer.

Hastie, T., Tibshirani, R., & Friedman, J. H. (2008). The Elements of Statistical Learning: Data mining, Inference, and Prediction. New York: Springer. -

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer.

Vapnik, V. N. (1998). Statistical Learning Theory. Wiley-Interscience. -

Ross, S. M. (2012). A First Course in Probability (9th ed). Pearson Education Limited. -

Hoel, P. G., Port, S. C, & Stone, C. J. (1972). Introduction to Probability Theory. Houghtion Mifflin, Boston. -

Eckhardt, W. (2013). Paradoxes in Probability Theory. Springer Dordrecht Heidelberg New York London.

Williams, D. (1991). Probability with Martingales. Cambridge University Press. -

Lawler, G. F. (1995). Introduction to Stochastic Processes. New York: Chapman and Hall/CRC. -

Oksendal, B. K. (2002). Stochastic Differential Equations: An Introduction with Applications (5th ed). Springer-Verlag Berlin Heidelberg. -

Cover, T. & Thomas, J. (2006). Elements of Information Theory (2nd ed). Wiley, Hoboken, New Jersey. -

Rausand, M. & Høyland, A. (2004). System Reliability Theory: Models, Statistical Methods, and Applications. Wiley-Interscience, Hoboken, New Jersey. -

Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. J. (1997). Actuarial Mathematics (2nd ed). Society of Actuaries. -

Shang, H. (2006). Actuarial Science: Theory and Methodology. World Scientific Publishing, Singapore. -

Hull, J. (2011). Options, Futures, and Other Derivatives (8th ed). Pearson / Prentice Hall. -

Duffie, D. (2001). Dynamic Asset Pricing Theory (3rd ed). Princeton University Press. -

Brigo, D., & Mercurio, F. (2006). Interest Rate Models - Theory and Practice (2nd ed). Springer-Verlag Berlin Heidelberg. -

Lipton, A. (2001). Mathematical Methods for Foreign Exchange: A Financial Engineer's Approach. World Scientific. -

Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1996). The Econometrics of Financial Markets (2nd ed). Princeton University Press.

Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk (2nd ed). McGraw-Hill. -

Taleb, N. (1997). Dynamic Hedging: Managing Vanilla and Exotic Options. Wiley Finance, New York. -

Ewens, W. J. (2004). Mathematical Population Genetics: Theoretical Introduction (2nd ed). New York: Springer. -

Deonier, R. C., Tavaré, S. & Waterman, M. (2005). Computational Genome Analysis: An Introduction. Springer Science, New York. -

Gondro, C., van der Werf, J. & Hayes, B. (2013). Genome-Wide Association Studies and Genomic Prediction. Springer New York Heidelberg Dordrecht London. -

Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.

Spall, J. C. (2003). Introduction to Stochastic Search and Optimization. Wiley-Interscience, Hoboken, New Jersey. -

Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press. -

Prékopa, A. (2010). Stochastic Programming (Mathematics and Its Applications). Kluwer Academic Publishers. -

Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. Princeton University Press. -

Kouvelis, P., & Yu, G. (2010). Robust Discrete Optimization and Its Applications (Nonconvex Optimization and Its Applications). Kluwer Academic Publishers. -

- Detailed profile: experience, case studies and payment options
- Directory of financial topics