Workshop Description

The Workshop on Higher-Order Asymptotics and Post-Selection Inference (WHOA-PSI) seeks to showcase the exciting new ideas coming out of the post-selection inference framework and investigate how tools from higher-order asymptotics can both elucidate important properties of post-selection inference procedures, as well as suggest new directions which may ultimately yield more accurate small-sample performance. (More details on potential topics below).

This conference supports the Non-Discrimination Statement of the Association for Women in Mathematics (AWM).

We are grateful to have the financial sponsorship of the NSF through DMS-1623028, the Dean of the Faculty of Arts & Sciences, the Department of Mathematics, and the Division of Biostatistics, all at Washington University in St. Louis.

Click here for a final program.

Click here for the discussion board. Most of the slides are also available there. I tried an experiment with this workshop: since speakers and audience members rarely have sufficient interaction, I created a discussion board where audience members could give comments and ask questions in real-time (also with LaTeX support), and the speaker could reply later on, even weeks later after having time to reflect!

Eventually (not yet) everything will be archived on a permanent website available here.

Organizing Committee

- Todd Kuffner (lead organizer, Washington University in St. Louis, Assistant Professor, Dept. of Mathematics)
- John Kolassa (Rutgers University, Professor, Dept. of Statistics and Biostatistics)
- Nancy Reid (University of Toronto, Professor, Dept. of Statistical Sciences)
- Ryan Tibshirani (Carnegie Mellon University, Assistant Professor, Dept. of Statistics)
- Alastair
Young (Imperial College London, Professor, Dept. of Mathematics)

Final List of Speakers (a full list of registered participants is at the bottom of this page)

- Genevera Allen
(Rice University, Assistant Professor, Dept. of Statistics)

- Jelena
Bradic (UC San Diego, Assistant Professor, Dept. of Mathematics)

- Lawrence Brown (University of Pennsylvania, Professor, Dept. of Statistics)
- Andreas Buja (University of Pennsylvania, Professor, Dept. of Statistics)
- Florentina
Bunea (Cornell University, Dept. of Statistical Science)

- Hongyuan Cao (University of Missouri Columbia, Assistant Professor, Dept. of Statistics)
- Jianqing Fan (Princeton University, Professor, Dept. of Operations Research and Financial Engineering)
- Don Fraser
(University of Toronto, Professor, Dept. of Statistical Sciences)

- Dalia Ghanem (UC Davis, Assistant Professor, Dept. of Agricultural and Resource Economics)
- Soumendra Lahiri
(NC State University, Professor, Dept. of Statistics)

- Xihong Lin
(Harvard University, Professor, Dept. of Biostatistics)

- Shujie Ma (UC Riverside, Assistant Professor, Dept. of Statistics)
- Ryan Martin
(NC State University, Associate Professor,
Dept. of Statistics)

- Peter McCullagh (University of Chicago, Professor, Dept. of Statistics)
- Xiao-Li
Meng (tentative, Harvard University, Professor, Dept. of Statistics)

- Annie Qu
(University of Illinois Urbana-Champaign, Professor, Dept. of
Statistics)

- John Robinson
(University of Sydney, Professor, School of Mathematics and Statistics)

- Richard Samworth
(University of Cambridge, Professor, Dept. of Pure Mathematics and
Mathematical Statistics)

- Nicola Sartori
(University of Padova, Italy, Associate Professor, Dept. of Statistical
Sciences)

- Yuekai Sun (UC Berkeley, Neyman Visiting Assistant Professor, Dept. of Statistics)
- Xiaoying Tian (Stanford University, current Ph.D. student)
- Rob Tibshirani
(Stanford University, Professor, Dept. of Statistics)

- Daniel Yekutieli
(Tel Aviv University, Israel, Professor, Dept. of Statistics and
Operations Research)

- Anru Zhang (University of Wisconsin Madison, Assistant Professor, Dept. of Statistics)

Potential Topics include (but are certainly not limited to):

Principles and general views of post-selection inference, for example

- Benjamini
(2010). `Simultaneous and selective inference: current successes and
future challenges', Biometrical
Journal 52, 708-721.

- Taylor & Tibshirani (2015), `Statistical learning and selective inference', Proceedings of the National Academy Sciences 112, 7629-7634.

- Hyun, G'Sell & Tibshirani (2016), `Exact post-selection inference for changepoint detection and other generalized lasso problems', arXiv: 1606.03552
- Taylor &
Tibshirani (2016), `Post-selection inference for L1-penalized
likelihood models', arXiv: 1602.07358

- Fithian, Taylor,
Tibshirani & Tibshirani (2015+), `Selective sequential model
selection', arXiv: 1512.02565

- Tibshirani, Taylor,
Lockhart, Tibshirani (2015+), `Exact post-selection inference for
sequential regression procedures', J.
Amer. Statist. Assoc., to appear.

- Lockhart, Taylor, Tibshirani & Tibshirani (2014), `A significance test for the lasso', Annals of Statistics 42, 413-468.
- Tibshirani, Rinaldo, Tibshirani & Wasserman (2015), `Uniform asymptotic inference and the bootstrap after model selection', arXiv: 1506.06266
- Tian & Taylor (2015), `Asymptotics of selective inference', arXiv: 1501.03588
- Lee, Sun, Sun & Taylor (2015), `Exact post-selection inference with the lasso', to appear in the Annals of Statistics.

- Berk, Brown, Buja, Zhang & Zhao (2013), `Valid post-selection inference', Annals of Statistics 41, 802-837.
- Benjamini
(2010), `Discovering the false discovery rate', J. Roy. Statist. Soc. Ser. B 72,
405-416.

- Benjamini & Yekutieli (2005), `False discovery rate-adjusted multiple confidence intervals for selected parameters', J. Amer. Statist. Assoc. 100, 71-93.
- G'Sell, Wager, Chouldechova & Tibshirani (2015+), `Sequential selection procedures and false discovery rate control', J. Roy. Statist. Soc. Ser. B, to appear.
- Barber
& Candes (2015), `Controlling the false discovery rate via
knockoffs', Annals of Statistics
43, 2055-2085.

- Panigrahi, Taylor
& Weinstein (2016). `Bayesian post-selection inference in the
linear model', arXiv: 1605.08824

- Yekutieli
(2012). `Adjusted Bayesian inference for selected parameters', J. Roy. Statist. Soc. Ser. B,
74(3), 515-541.

- Bradic (2016). `Randomized maximum-contrast selection: subagging for large-scale regression', Elec. J. Statist. 10(1), 121-170.
- Li
& Bradic (2015). `Boosting in the presence of outliers: adaptive
classification with non-convex loss functions', arXiv: 1510.01064.

- Efron (2014), `Estimation and accuracy after model selection', J. Amer. Statist. Assoc. 109, 991-1007.
- Buhlmann
& Yu (2002), `Analyzing bagging', Annals
of Statistics 30, 927-961.

- Fan, Shao & Zhou (2015), `Are discoveries spurious? Distributions of Maximum Spurious Correlations and their applications', arXiv: 1502.04237
- Cai & Guo (2015), `Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity', arXiv: 1506.05539
- Ning & Liu (2015),
`A general theory of hypothesis tests and confidence regions for sparse
high dimensional models', arXiv: 1412.8765

- Ning, Zhao & Liu
(2015), `A likelihood ratio framework for high dimensional
semiparametric regression', arXiv: 1412.2295

- Shah & Samworth (2013), `Variable selection with error control: another look at stability selection', J. Roy. Statist. Soc. B 75, 55-80.
- Meinshausen & Buhlmann (2010), `Stability selection', J. Roy. Statist. Soc. Ser. B 72, 417-473.
- van de Geer, Buhlmann, Ritov & Dezeure (2014), `On asymptotically optimal confidence regions and tests for high-dimensional models', Annals of Statistics 42, 1166-1202.
- Javanmard &
Montanari (2015+), `Hypothesis testing in high-dimensional regression
under the Gaussian random design model: asymptotic theory', IEEE Trans. Inform. Theory, to
appear.

- Liu & Yu (2013), `Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression', Electronic J. Statist. 7, 3124-3169.
- Zhang & Zhang (2014), `Confidence intervals for low-dimensional parameters in high-dimensional linear models', J. Roy. Statist. Soc. Ser. B 76, 217-242.
- Belloni, Chernozhukov
& Hansen, `Inference methods for high-dimensional sparse
econometric models', Advances in
Economics & Econometrics, Econometric Society World Congress
2010.

- Shi & Qu (2016). `Weak signal identification and inference in penalized model selection', Annals of Statistics, to appear.
- Jeng
(2016). `Detecting weak signals in high dimensions', J. Multivariate Statist. 147,
234-246.

The aspects of the above topics and other post-selection inference procedures which will be emphasized in the workshop are those related to higher-order asymptotics, including both analytic- and resampling-based tools and refinements, some of which are described in:

- Small (2010), Expansions and Asymptotics for Statistics, Chapman & Hall.
- Young
(2009), `Routes to higher-order accuracy in parametric inference', Austral. N.Z. J. Statist. 51,
115-126.

- Brazzale & Davison (2008), `Accurate parametric inference for small samples', Statistical Science 23, 465-484.
- Brazzale, Davison & Reid (2007), Applied Asymptotics: Case Studies in Small-Sample Statistics, Cambridge University Press.
- Butler (2007), Saddlepoint Approximations with Applications, Cambridge University Press.
- Bedard, Fraser &
Wong (2007), `Higher accuracy for Bayesian and frequentist inference:
large sample theory for small sample likelihood', Statistical Science 22, 301-321.

- Yi
& Fraser (2007), `Higher order asymptotics: an intrinsic difference
between univariate and multivariate models', J. Statist. Research 41, 1-20.

- Kolassa (2006), Series
Approximation Methods in Statistics 3rd edition, Springer.

- Young & Smith (2005), Essentials of Statistical Inference, Cambridge University Press.
- Reid
(2003), `Asymptotics and the theory of inference', Annals of Statistics 31, 1695-1731.

- Severini (2000), Likelihood Methods in Statistics, Oxford University Press.
- Pace & Salvan (1997), Principles
of Statistical Inference from a Neo-Fisherian Perspective, World
Scientific.

- Jensen (1995), Saddlepoint Approximations, Oxford University Press.
- Ghosh (1994), Higher Order
Asymptotics, Institute of Mathematical Statistics.

- Barndorff-Nielsen & Cox (1994), Inference and Asymptotics, Chapman & Hall.
- Hall (1992), The Bootstrap and Edgeworth Expansion, Springer.
- Field & Ronchetti (1990), Small Sample Asymptotics, Institute of Mathematical Statistics.
- McCullagh
(1987), Tensor Methods in Statistics,
Chapman & Hall.

- Chapter 3 of Fithian
(2015), Topics in Adaptive Inference,
Ph.D. thesis, Stanford University.

- Chapter 6 of Hastie, Tibshirani & Wainwright (2015), Statistical Learning with Sparsity: The Lasso and Generalizations, Chapman & Hall.
- Chapters 10-11 of Buhlmann &
van de Geer (2011), Statistics for
High-Dimensional Data, Springer.

List of Registered Participants (may be missing a few)

Faculty and post-docs from other institutions (35 US, 9 international)

Faculty from Washington University in St. Louis (9)

Graduate students from other institutions (8)

Debraj Das |
North
Carolina State University Dept. of Statistics |
Ph.D. student (Soumendra Lahiri) |

Sangwon Hyun |
Carnegie
Mellon University Dept. of Statistics |
Ph.D. student (Max G'Sell and
Ryan Tibshirani) |

Arun Kumar Kuchibhotla | University
of Pennsylvania Dept. of Statistics |
Ph.D. student |

Jelena Markovic | Stanford
University Dept. of Statistics |
Ph.D. student (Jonathan Taylor) |

Snigdha Panigrahi | Stanford
University Dept. of Statistics |
Ph.D. student (Jonathan Taylor) |

Xiwei Tang | University
of Illinois Urbana-Champaign Dept. of Statistics |
Ph.D. student (Annie Qu) |

Suzanne Thornton |
Rutgers University Dept. of Statistics and Biostatistics |
Ph.D. student (Min-ge Xie) |

Xiaoying Tian |
Stanford
University Dept. of Statistics |
Ph.D. student (Jonathan Taylor) |

Graduate students from Washington University in St. Louis (16)

Luis Garcia German |
Dept. of Mathematics |
Ph.D. student (Jose Figueroa-Lopez) |

Guanshengrui Hao |
Dept. of Mathematics |
Ph.D. student (Nan Lin) |

Jiayi Fu |
Dept. of Mathematics |
Ph.D. student |

Junnan He |
Dept. of Economics |
Ph.D. student (Werner Ploberger) |

Chang Liu |
Dept. of Mathematics |
Ph.D. student |

Hongyi Liu |
Dept. of Economics |
Ph.D. student (Werner Ploberger) |

Cezareo Rodriguez |
Dept. of Mathematics |
Ph.D. student |

Qi Wang |
Dept. of Mathematics |
Ph.D. student (Jose
Figueroa-Lopez and Todd Kuffner) |

Tian Wang |
Dept. of Mathematics |
Ph.D. student (Jimin Ding) |

Wei Wang |
Dept. of Mathematics |
Ph.D. student (Nan Lin) |

Liqun Yu |
Dept. of Mathematics |
Ph.D. student (Nan Lin) |

Zoe Yu |
Dept. of Mathematics |
Ph.D. student |

Michael Zdinak |
Dept. of Economics |
Ph.D. student (Werner Ploberger) |

Li Zhang |
Dept. of Economics |
Ph.D. student (Werner Ploberger) |

Qiyiwen Zhang |
Dept. of Mathematics |
Ph.D. student |

Xinwei Zhang |
Dept. of Mathematics |
A.M. student (Todd Kuffner) |

Local Information

For those arriving early or thinking about staying longer, St. Louis is a lovely place to visit. Besides the iconic Gateway Arch and the nearby Old Courthouse which houses exhibits on the Dred Scott case, St. Louis has a stunning botanical garden, a high density of good restaurants (BBQ is a specialty), and is close to many rivers (Missouri, Mississippi and Meremac) which are great for float trips. There are many nearby parks and nature reserves which are excellent for hiking, as well as a wolf sanctuary. Mark Twain's boyhood home lies an hour north of the city. Anheuser-Busch is headquartered in St. Louis and offers tours of the brewery (requires advance booking due to popularity). The workshop takes place one week before the second scheduled US Presidential Election debate, which will be held on campus. For those unfamiliar with the institution, Washington University in St. Louis is a leading national research university, ranked 23rd in the world in the 2016 Academic Ranking of World Universities. Our statistics presence is concentrated in the Dept. of Mathematics. You are encouraged to look around this beautiful campus on the western edge of St. Louis, which faces Forest Park, the site of the 1904 World's Fair and home to the Saint Louis Zoo and Saint Louis Art Museum (both free admission, walking distance from campus). For baseball fans, the Pittsburgh Pirates are in town the weekend of the workshop, playing the St. Louis Cardinals at Busch Stadium.