Michael Steele

C.F. Koo Professor at The Wharton School

Schools

  • The Wharton School

Links

Biography

The Wharton School

Education

  • PhD, Stanford University, 1975
  • BA, Cornell University, 1971

Career and Recent Professional Awards

  • President, Institute for Mathematical Statistics, 2010
  • Fellow, Institute for Mathematical Statistics, 1984
  • Fellow, American Statistical Association, 1989
  • Frank Wilcoxon Prize, American Society for Quality Control and the American Statistical Association, 1990

Academic Positions Held

Wharton : 1990present (named C.F. Koo Professor, 1991).

Previous appointments : Princeton University; Carnegie Mellon University; Stanford University; University of British Columbia.

Visiting appointments : University of Chicago, Columbia University

A. Arlotto and J. Michael Steele (2017), A Central Limit Theorem for Costs in Bulinskaya's Inventory Management Problem When Deliveries Face Delays , Methodology and Computing in Applied Probabiliy: Special Issue in Memory of Moshe Shaked, (to appear).

J. Michael Steele and Peichao Peng (Under Review), Analogs of Records: Relative Sequential Selections: Relaxed or Constrained.

J. Michael Steele (2016), The BrussRobertson Inequality: Elaborations, Extensions, and Applications , Mathematica Applicanda (Annales Societatis Mathematicae Polonae Series III), 44 (1), pp. 316.

A. Arlotto and J. Michael Steele (2016), A Central Limit Theorem for Temporally NonHomogenous Markov Chains with Applications to Dynamic Programming , Mathematics of Operations Research, 41 (4), pp. 14481468.

Peichao Peng and J. Michael Steele (2016), Sequential Selection of a Monotone Subsequence from a Random Permutation , Proceedings of the American Mathematics Society, 144 (11), pp. 49734982.

A. Arlotto, Elchanan Mossel, J. Michael Steele (2016), Quickest Online Selection of an Increasing Subsequence of Specified Size , Random Structures and Algorithms, 49, pp. 235252.

A. Arlotto and J. Michael Steele (2016), BeardwoodHaltonHammersly Theorem for Stationary Ergodic Sequences: a Counterexample , Annals of Applied Probability, 26 (4), pp. 21412168.

V. Posdnyakov and J. Michael Steele (2016), Buses, Bullies, and Bijections , Mathematics Magazine, 89 (3), pp. 167176.

V. Pozdnyakov and J. Michael Steele (Under Review), Scan Statistics: Pattern Relations and Martingale Methods.

S. Bhamidi, J. Michael Steele, T. Zaman (2015), Twitter Event Networks and the Superstar Model , Annals of Applied Probability, 25 (5), pp. 24622502.

Past Courses

STAT430 PROBABILITY

Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

STAT433 STOCHASTIC PROCESSES

An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and BlackScholes.

STAT434 FIN & ECON TIME SERIES

This course will introduce students to the time series methods and practices which are most relevant to the analysis of financial and economic data. After an introduction to the statistical programming language R the course develops an autoregressive models, moving average models, and their generalizations. The course then develops models that are closely focused on particular features of financial series such as the challenges of time dependent volatility.

STAT510 PROBABILITY

Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals.

STAT533 STOCHASTIC PROCESSES

An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and BlackScholes.

STAT900 ADVANCED PROBABILITY

The topics covered will change from year to year. Typical topics include the theory of large deviations, percolation theory, particle systems, and probabilistic learning theory.

STAT901 STOCHASTIC PROCESSES II

Martingales, optimal stopping, Wald's lemma, agedependent branching processes, stochastic integration, Ito's lemma.

STAT930 PROBABILITY

Measure theory and foundations of Probability theory. Zeroone Laws. Probability inequalities. Weak and strong laws of large numbers. Central limit theorems and the use of characteristic functions. Rates of convergence. Introduction to Martingales and random walk.

STAT931 STOCHASTIC PROCESSES

Markov chains, Markov processes, and their limit theory. Renewal theory. Martingales and optimal stopping. Stable laws and processes with independent increments. Brownian motion and the theory of weak convergence. Point processes.

STAT941 ADVANCE INFERENCE II

A continuation of STAT 940.

STAT955 STOCH CAL & FIN APPL

Selected topics in the theory of probability and stochastic processes.

STAT956 FIN & ECON TIME SERIES

This graduate course introduces students to the time series methods and practices which are most relevant to the analysis of financial and economic data. The course will address both theoretical and empirical issues. Extensive use will be made of the SPlus Statistical Language, but no previous experience of SPlus will be required. The course begins with a quick review of ARIMA models. Most of the course is devoted to ARCH, GARCH, threshold, switching Markov, state space, and nonlinear models.

STAT991 SEM IN ADV APPL OF STAT

This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

  • Wharton Undergraduate Excellence in Teaching Award, 2010
  • Frank Wilcoxon Prize, American Society for Quality Control and the American Statistical Association, 1990
  • Fellow, American Statistical Association, 1989
  • Fellow, Institute for Mathematical Statistics, 1984

Knowledge @ Wharton

  • Polling the Polling Experts: How Accurate and Useful Are Polls These Days?, Knowledge @ Wharton 11/14/2007
  • To Blog or Not to Blog: Report from the Front, Knowledge @ Wharton 10/18/2006

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