Category Archives: Short Courses

Four short courses will be offered on August 18, in two sessions (morning and afternoon). Short course registration is now available!

Multiple Test Procedures with Gatekeeping and Graphical Applications

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Instructor: Ajit C. Tamhane (Northwestern University)

AUGUST 18, 8:30—Noon

ROOM: LInC 368

Modern phase III confirmatory clinical trials often involve multidimensional study objectives which require simultaneous testing of multiple hypotheses with logical relationships among them. Examples of such study objectives include investigation of multiple doses or regimens of a new treatment, multiple endpoints, subgroup analyses, non-inferiority and superiority tests, or any combination of these. This short course will provide practical guidance on how to construct multiple testing procedures (MTPs) for such hypotheses while taking into account the logical relationships among them and controlling the appropriate Type I error rate. Continue reading →

Statistical Computing with Julia

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Instructor: Hua Zhou (University of California, Los Angeles)

AUGUST 18, 8:30—Noon

ROOM: LInC 268

Julia is a new open source programming language for technical computing. Its flexible design offers greater speed and power than the R+Python combination without radical change. Are statisticians and data scientists ready for Julia and is Julia ready for them? This short course illustrates the basic language features, numerical linear algebra, statistical functions (JuliaStat), optimization (JuliaOpt), and parallel and distributed computing in Julia using a variety of statistical applications. Continue reading →

Bayesian Computation using PROC MCMC

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Instructor: Fang Chen (SAS Institute Inc)

AUGUST 18, 1:30—5 PM

ROOM: LInC 268

The MCMC procedure is a general purpose Markov chain Monte Carlo simulation tool designed to fit a wide range of Bayesian models, including linear or nonlinear models, multi-level hierarchical models, models with nonstandard likelihood function or prior distributions, and missing data problems. This tutorial starts with an in-depth introduction to PROC MCMC and moves on to demonstrate its use with a series of applications. An optional and brief introduction to Bayesian and MCMC methods is included if the audience prefers an overview on the paradigm. Continue reading →

Structural Breaks, Outliers, MDL, Some Theory and Google Trends

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Instructor: Richard Davis (Columbia University)

AUGUST 18, 1:30—5 PM

ROOM: LInC 368

In this course, we will take another look at modeling time series that exhibit certain types of nonstationarity. Often one encounters time series for which segments look stationary, but the whole ensemble is nonstationary. On top of this, each segment of the data may be further contaminated by an unknown number of innovational and/or additive outliers; a situation that presents interesting modeling challenges. We will seek to find the best fitting model in terms of the minimum description length principle. As this procedure is computationally intense, strategies for accelerating the computations are required. Numerical results from simulation experiments and real data analyses, some of which come from Google trends, show that our proposed procedure enjoys excellent empirical properties. In the case of no outliers, there is an underlying theory that establishes consistency of our method. The theory is based on an interesting application of the functional law of the iterated logarithm.
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