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.


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 $80 per hour. However, for selected areas requiring "high-tech" skills the rate is higher. This includes machine learning, hidden Markov models, spatial statistics, advanced SAS procedures, structural equation modeling and such. The best course of action is to contact me with a syllabus or a project description and I will provide you with detailed pricing... Also, some of you may choose to minimize the costs by engaging in long periods of independent reading and problem solving before working with me on more advanced topics. This is completely fine. In fact, to learn a big area well you should approach the task as a marathon and not a sprint. And this is only possible if the process does not put much strain on your emotions and finances. So please do as much as you can on your own and contact me when ready. Typically, it is beneficial to have at least one introductory tutoring session to assess your background, "defrost" the dormant knowledge and design the study plan. But this is not required. Without knowing you I can suggest only a generic study plan but it does work for many students (with small modifications at times).

Below I am sketching an independent study program designed to give a student the knowledge of probability, statistics and one of the applied areas. The program assumes that the student comes with fundamental training in mathematics (calculus, geometry, matrix algebra) and scrappy knowledge of selected parts of probability and statistics. Again... the plan is designed without knowing you. So please take it with a grain of salt and contact me if at any point in the future you feel the need for a customized solution.

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.




Statistics: Undergraduate Level

Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed). New York: W. W. Norton & Company. - Intuitive and relatively slow to make it easy on an undergrad. If at times you feel bored because you have already understood the material, feel free to skip some of the examples and illustrations.

Connoly, S. (2015). College Statistics Made Easy. Algebra Publishing Higher Education. - The first half of the book will be familiar after Freedman et al. and probability books. So you can skip it. But read the second half, which continues the discussion of linear regression and offers college level introduction into ANOVA, generalized linear models and nonparametrics.

Statistics, Biostatistics & Econometrics: Master's Level & Higher

Lehmann, E. L., & Casella, G. (1998). Theory of Point Estimation (2nd ed). New York: Springer. - Focus on chapters 1, 2 and 4, which are an absolute must.

Lehmann, E. L., & Romano, J. P. (2006). Testing Statistical Hypotheses (corrected 2nd printing of the 3rd ed). New York: Springer. - Focus on chapters 3 - 5 and 11 - 13. The two Lehmann et al. books require complete and coherent training in probability theory and are best utilized if you read proofs and do practice problems.

Greene, W. H. (2011). Econometric Analysis (7th ed). Upper Saddle River, NJ: Prentice Hall. - Let the contents not confuse you into thinking that after reading Lehmann et al. you can skip some of the chapters. The book is largely complementary and highlights the issues of heteroskedasticity, serial correlation, large-sample properties of estimators and other problems important for econometricians.

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. - During the first run read chapters 1 - 6 only.

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.

Data Mining

Hastie, T., Tibshirani, R., & Friedman, J. H. (2008). The Elements of Statistical Learning: Data mining, Inference, and Prediction. New York: Springer. - Beautiful, intuitive, easy to read. Many areas are covered. The introductory chapters are as important as more fashionable material closer to the end of the book.

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

Vapnik, V. N. (1998). Statistical Learning Theory. Wiley-Interscience. - Advanced but covers topics downplayed in other references.

Probability Theory and Combinatorics: Undergraduate Level

Ross, S. M. (2012). A First Course in Probability (9th ed). Pearson Education Limited. - 1st choice.


Hoel, P. G., Port, S. C, & Stone, C. J. (1972). Introduction to Probability Theory. Houghtion Mifflin, Boston. - 2nd choice.

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

Probability Theory & Stochastic Processes: Master's Level & Higher

Williams, D. (1991). Probability with Martingales. Cambridge University Press. - Part A is an introduction to measure theory and general probability theory. The whole book is a must.

Lawler, G. F. (1995). Introduction to Stochastic Processes. New York: Chapman and Hall/CRC. - There are many good overviews of stochastic processes but this one is concise, containing all the key results nonetheless.

Oksendal, B. K. (2002). Stochastic Differential Equations: An Introduction with Applications (5th ed). Springer-Verlag Berlin Heidelberg. - A clean and rigorous introduction to stochastic calculus but read it only after you have picked up measure theory in some other references.

Information Theory

Cover, T. & Thomas, J. (2006). Elements of Information Theory (2nd ed). Wiley, Hoboken, New Jersey. - The biggest minds often write the most readable books. This certainly is the case for Tom Cover. Too sad he is gone. He was a warm person, genuinely curious about life.

Systems Reliability Theory

Rausand, M. & Høyland, A. (2004). System Reliability Theory: Models, Statistical Methods, and Applications. Wiley-Interscience, Hoboken, New Jersey. - This reference complements survival analysis, actuarial science and credit risk books because much attention is spent on the non-linear mechanism in which the reliability of the whole system depends on the reliability of separate parts.

Actuarial Science

Bowers, N. L., Gerber, H. U., Hickman, J. C., Jones, D. A., & Nesbitt, C. J. (1997). Actuarial Mathematics (2nd ed). Society of Actuaries. - A big reference, which goes from the simple to the complex.

Shang, H. (2006). Actuarial Science: Theory and Methodology. World Scientific Publishing, Singapore. - This one is more focused on modern methods of actuarial mathematics.

Quantitative Finance

Hull, J. (2011). Options, Futures, and Other Derivatives (8th ed). Pearson / Prentice Hall. - A friendly introduction for MBAs and anybody with imperfect quantitative training. Includes many institutional details.

Duffie, D. (2001). Dynamic Asset Pricing Theory (3rd ed). Princeton University Press. - Chapters 3, 4 and 9 are peripheral to asset pricing and can be skipped on the first read. Otherwise, the book is a coherent sequence of logical steps, leading a reader with pure stochastic processes training to understanding why derivatives are priced the way they are. Not for MBAs.

Brigo, D., & Mercurio, F. (2006). Interest Rate Models - Theory and Practice (2nd ed). Springer-Verlag Berlin Heidelberg. - Continuation of Duffie with emphasis on the change of numeraire and term structure models.

Lipton, A. (2001). Mathematical Methods for Foreign Exchange: A Financial Engineer's Approach. World Scientific. - Continuation of Duffie with emphasis on exotic options.

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. - Factor models for trading with long horizon.

Taleb, N. (1997). Dynamic Hedging: Managing Vanilla and Exotic Options. Wiley Finance, New York. - Standard reference for traders and risk managers.

Statistical Genetics

Ewens, W. J. (2004). Mathematical Population Genetics: Theoretical Introduction (2nd ed). New York: Springer. - Stochastic processes applied to modeling dynamics of gene pool.

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

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


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. - Standard reference on stochastic optimization.

Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press. - The book is longer than it could be, but if you read the book you will see the power that reinforcement learning techniques can give you. The estimation algorithm may not converge for days or it may not converge at all, but if it does very complex, multi-dimensional problems are solved and optimal strategies are determined.

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

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

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