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November 2017

STOR Colloquium: Danica Ommen, Iowa State University

November 13, 2017 @ 2:30 pm - 3:30 pm

Title: Different Paradigms of Interpretation for Forensic Value of Evidence Quantification Abstract: Currently, one of the major problems in the forensic science community is the confusion between different statistical paradigms. A quantification of the value of evidence is interpreted differently under each paradigm, and may even be the answer to different questions. It is our opinion that these issues need to be addressed before quantitative forensic analyses are considered a reliable science in the justice system. A related issue is the…

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STOR Colloquium: Todd Kuffner, Washington University in St. Louis

November 15, 2017 @ 2:30 pm - 3:30 pm

Title: Philosophy of Science, Principled Statistical Inference, and Data Science   Abstract: Statistical reasoning and statistical inference have strong historical connections with philosophy of science. In this talk, the new paradigm of data-driven science is examined through comparison with principled statistical approaches. I will review the merits and shortcomings of principled statistical inference. The talk will feature a case study of post-selection inference, recent progress regarding inference for black box algorithms, and a survey of future challenges.   Refreshments will…

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STOR Colloquium: Alex Belloni, Duke

November 20, 2017 @ 2:30 pm - 3:30 pm

Title: Inference with High-Dimensional Controls and Parameters of Interest based on joint work with Victor Chernozhukov, Denis Chetverikov, and Ying Wei Abstract: In this work we propose and analyze procedures to construct confidence regions for p (infinite dimensional) parameters of interest after model selection for general moment condition models where p is potentially larger than the sample size n. This allows us to cover settings with functional response data where each of the p > n parameters of interest is…

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December 2017

STOR Colloquium: Anru Zhang, University of Wisconsin-Madison

December 4, 2017 @ 2:30 pm - 3:30 pm

Singular Value Decomposition for High-dimensional High-order Data   High-dimensional high-order data arise in many modern scientific applications including genomics, brain imaging, and social science. In this talk, we consider the methods, theories and computations for tensor singular value decomposition (tensor SVD), which aims to extract the hidden low-rank structure from high-dimensional high-order data. First, comprehensive results are developed on both the statistical and computational limits for tensor SVD under the general scenario. This problem exhibits three different phases according to…

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January 2018

Mariana Olvera-Cravioto, University of California, Berkeley

January 10, 2018 @ 2:30 pm - 3:30 pm

Efficient simulation for branching recursions   A variety of problems in science and engineering, ranging from population and statistical physics models to the study of queueing systems, computer networks and the internet, lead to the analysis of branching distributional equations. The solutions to these equations are not in general analytically tractable, and hence need to be computed numerically. This talk discusses a simulation algorithm known as “Population Dynamics”, which is designed to produce a pool of identically distributed observations having…

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CANCELLED: Jie Ding, Harvard University

January 17, 2018 @ 2:30 pm - 3:30 pm

Some New Foundational Principles and Fast Algorithms in Data Analytics   Rapid developments in communications, networking, AI robots, 3D printing, genomics, blockchain, novel materials, and powerful computation platforms are rapidly bringing data-generating people, processes and devices together. The interactions between data analytics in multiple regimes (sparse, panel, big data, etc.) and other fields are exciting because the tools that are being invented now may enable new, faster and semi-automated methods of scientific discovery. These, in turn, might further amplify the…

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STOR Colloquium: Mikael Kuusela, SAMSI and UNC-Chapel Hill

January 19, 2018 @ 2:30 pm - 3:30 pm

Locally stationary spatio-temporal interpolation of Argo profiling float data   Argo floats measure sea water temperature and salinity in the upper 2,000 m of the global ocean. The statistical analysis of the resulting spatio-temporal data set is challenging due to its nonstationary structure and large size. I propose mapping these data using locally stationary Gaussian process regression where covariance parameter estimation and spatio-temporal prediction are carried out in a moving-window fashion. This yields computationally tractable nonstationary anomaly fields without the…

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STOR Colloquium: Jason Xu, UCLA

January 22, 2018 @ 2:30 pm - 3:30 pm

Enabling likelihood-based inference for complex and dependent data   The likelihood function is central to many statistical procedures, but poses challenges in classical and modern data settings. Motivated by emergent cell lineage tracking experiments to study blood cell production, we present recent methodology enabling likelihood-based inference for partially observed data arising from continuous-time stochastic processes with countable state space. These computational advances allow principled procedures such as maximum likelihood estimation, posterior inference, and expectation-maximization (EM) algorithms in previously intractable data…

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STOR Colloquium: Shizhe Chen, Columbia University

January 24, 2018 @ 2:30 pm - 3:30 pm

Learning the Connectivity of Large Sets of Neurons   New techniques in neuroscience have opened the door to rich new data sets of neural activities. These data sets shed light on the computational foundation of the brain, i.e., neurons and synapses. However, these data also present unprecedented challenges: novel statistical theory and methods are required to model neural activities, and well-designed experiments are needed to collect informative data. In this talk, we take on the task of learning connectivity among…

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STOR Colloquium: Robin Gong, Harvard University

January 26, 2018 @ 2:30 pm - 3:30 pm

Bayes is sensitive. Is imprecise probability more sensible?   Bayes is prized as principled and coherent, but its quality of inference is sensitive to prior and model misspecifications. Imprecise probability (IP) allows for the flexible expression of partially deficient probabilistic information. In our quest for minimal-assumption inference, is IP a more promising alternative to Bayes?   In this talk, I showcase the power of IP with an application of the Dempster-Shafer theory of belief functions to the prior-free estimation of…

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