Nonparametric Statistical Inference Under Shape Constraints

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Nonparametric Statistical Inference Under Shape Constraints

 16 - 20 May 2016

ICMS, 15 South College Street Edinburgh
  • Richard Samworth, University of Cambridge
  • Ming Yuan, University of Wisconsin

About:

Nonparametric shape constraints offer statisticians the potential of freedom from restrictive parametric assumptions, while still permitting fully automatic procedures.  In this sense, they combine the best of both the parametric and nonparametric worlds. Inference under shape constraints is a core area of statistical theory and methodology, but the methods often involve challenging optimisation problems and have important applications in many areas, including biology and econometrics.

Speakers

  • Lauren Hannah, Columbia University - Shape Constrained Inference for the Operational Researcher

  • Richard Samworth, University of Cambridge - An Introduction to Nonparametric Inference Under Shape Constraints

  • Guenther Walther, Stanford University - Constructing an Essential Histogram    

  • Mary Meyer, Colorado State University - Estimation and Inference in Regression Models with Constrained Surfaces

  • Henrik Lopuhaa, Delft University of Technology - Estimation of the Baseline Distribution in the Cox Model Under Monotonicity Constraints

  • Guert Jongbloed, Delft University of Technology - Estimating a Decreasing Density at Zero 

  • Ivan Mizera, University of Alberta - Shape Constrained Empirical Bayes Prediction

  • Aditya Guntuboyina, University of California - Adaptation in Log-Concave Density Estimation

  • Qiyang Han, University of Washington - Multivariate Convex Regression

  • Noureddine El Karoui, University of California - Can We Trust the Bootstrap?

  • Yining Chen, London School of Economics - Generalised Additive and Index Models with Shape Constraints

  • Johannes Schmidt-Hieber, University of Leiden - Nonparametric Estimation for a Class of Flat Holder Functions

  • Piet Groeneboom, Delft University of Technology - Current Status Linear Regression

  • Karthik Bharath, University of Nottingham - Random Warping Functions for Curve Alignment

  • Matus Maciak, Charles University - Change-Points in Shape Constrained Regression

  • Bodhisattva Sen, Columbia University - Estimation of a Two-Component Mixture Model with Applications to Multiple Testing

  • Yannick Baraud, Universite Sophia-Antipolis - Rho-Estimation Under Shape Constraints

  • Victor-Emmanuel Brunel, Massachusetts Institute of Technology - Recovering Level Sets of the Tukey Depth

  • Dragi Anevski, Lund University - Shape and Order Restricted Inference for Dependent Data

  • Johannes Royset, Naval Postgraduate School - Fusion of Hard and Soft Information in Density Estimation

  • Andrew Johnson, Texas A&M University - Production Function Estimation Using Shape Constrained Estimators

  • Min Xu, University of Pennsylvania - Faithful Variable Screening for High-Dimensional Convex Regression