Sergey B.
Private tutor in Auckland, New Zealand
Education
PhD in Statistics from a top US university | winner of several major competitions | degree in finance from a top business school | industry and academic experience in machine learning, stochastic processes, asset pricing and quantitative trading | areas covered: statistics, econometrics, finance, risk management, statistical arbitrage, actuarial mathematics, biostatistics and optimisation.
Experience
I help with assignments, tests, presentations, dissertations, advanced research, big programming projects, article review and general skill improvement. Proficient in all major statistical packages: Matlab, R / RStudio, SAS, SPSS, Stata, EViews, Minitab, JMP, Python. The covered areas can be split into the following blocks. 1) Mainstream Statistics and Biostatistics: multiple linear regression, logistic regression, logit / probit transformation, linearization of nonlinear regression, spline, kernel smoother, nonparametric statistics, cross-validation, model selection, bootstrap, jacknife, factor analysis / principal component analysis, canonical correlation analysis, classic Monte Carlo, Markov Chain Monte Carlo, EM algorithm, Gibbs sampler, Metropolis-Hastings, Bayesian statistics (conjugate prior, hierarchical Bayes, empirical Bayes), multivariate analysis, path analysis, structural equation modeling, missing data imputation, t-test, F-test, chi-square test, Wilcoxon test, Mann-Whitney / Kruskal-Wallis test, power calculation, weighted / generalized least squares, heteroskedasticity, outlier diagnostics, robust estimation / hypotheses testing / confidence intervals, variance transformation, propensity score matching, generalized linear model (GLM), analysis of variance (ANOVA / MANOVA), analysis of covariance (ANCOVA / MANCOVA), GWAS, survival analysis (parametric families, Kaplan - Meier estimator, log-rank test, Cox proportional hazards model, accelerated failure time model, competing risks, recurring events), systems reliability, bioinformatics, models for panel / longitudinal data, spatial statistics, Bonferroni correction, meta-analysis, experimental design, design of surveys, reliability theory, quality control. 2) Probability: discrete probability, continuous probability, large sample theory, martingale, Poisson process, Brownian motion, Levy process, random field, spectral theory, diffusion, semimartingale, stochastic volatility modeling, stochastic calculus, stationary process, renewal theory, Markov chain, regime switching process, Hidden Markov Model (HMM), Gaussian mixture process, fractal. 3) Econometrics: time series, instrumental variable, simultaneous equations, generalized method of moments (GMM), method of maximum likelihood, quasi likelihood, ARMA / ARIMA, ARCH / GARCH / EGARCH, ARDL, augmented Dickey - Fuller, vector autoregression (VAR), vector error correction model (VECM), serial correlation, filtering methods, forecasting, intervention analysis. 4) Finance: option pricing, exotic derivatives pricing, risk-neutral measure, replicating strategy, market risk, Black - Scholes, affine term structure model, interest rate products, foreign exchange, credit derivative, equities, commodities, swap, swaption, extinguishing swap, cancellable swap, callable bond, convertible bond, barrier option, Asian / American option, double knockout, futures contract, credit default swap (CDS), k-to-default swap, mortgage, empirical prepayment models, copula characterization, structural models, reduced form models, correlation smile, loss curve, HJM, Longstaff - Schwartz estimation, Crank - Nicolson scheme, mean-variance portfolio rebalancing, financial economics, statistical arbitrage (statarb), dispersion strategy, butterfly trading, reversion, momentum, cointegration, Sharpe / CalMar ratio optimization, backtesting, technical analysis, portfolio optimization and risk management, hedging, financial engineering / mathematical finance. 5) Data Mining: signal processing, pattern recognition, classification, neural network, projection pursuit, MARS, CART, random forest, support vector machine (SVM), wavelet analysis, ridge regression, lasso, nearest neighbor, discriminant analysis, naive Bayes, cluster analysis. 6) Optimisation: convex optimisation, dynamic programming, reinforcement learning, genetic algorithm, simulated annealing, robust optimisation. 7) Methods for Insurance: survival model, empirical hazard rate, life tables, premium dynamics, varying annuity, collective risk, copula function, decrement model, ruin theory, reinsurance derivative. 8) Statistical software: R, Matlab, SPSS / AMOS, Stata, SAS, JMP, Minitab, EViews, Microsoft Excel, RStudio, Python.
Availability
Any day at any time
Can Meet
Up to 1 hour away for no additional charge
Hobbies
Sessions via S k y p e (two hours minimum per session; hourly rate) or projects submitted through email (fixed cost specified beforehand).
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