ning hao arizona

Spring 2019 Data 375 Introduction to Statistical Computing; Math 129 Calculus II Session 15. In ultrahigh dimensional linear regression where the dimensionality is much larger than the sample size, traditional variance estimation techniques are not applicable. He is a relatively easy grader and wants his students to succeed.

Interaction Screening for Ultra-High Dimensional Data.

Home. This problem has attracted a lot of attention in statistics, and a variety of solutions have been proposed and implemented. Hao, N., & Zhang, H. H. (2014).

My google scholar page. View phone numbers, addresses, public records, background check reports and possible arrest records for Ning Hao. Ning Hao Assistant Professor nhao@math.arizona.edu: High dimensional data; Machine learning; Change point detection. Box 210089 Tucson, AZ 85721-0089 USA Voice: (520) 621-6892 Fax: (520) 621-8322 Contact Us

On sabbatical. in Mathematicsfrom Peking University, and Ph.D. from the Department of Mathematicsat Stony Brook University.

Teaching.

Verified email at math.arizona.edu - Homepage.



Loop is the open research network that increases the discoverability and impact of researchers and their work. Department of Mathematics, The University of Arizona 617 N. Santa Rita Ave. P.O. Sometimes it can be hard to understand him because of his thick Chinese accent, but he is very accessible if one needs help. The authors are partially supported by NSF Grants DMS-1309507 (Hao and Zhang), DMS-1308566 (Feng) and DMS-1554804 (Feng). Department of Mathematics, The University of Arizona 617 N. Santa Rita Ave. P.O.

Current Course. One of the major problems in ultrahigh dimensional regression is the high spurious correlation between the unobserved realized noise and some of the predictors. Our asymptotic results show that the resulting procedure performs as well as the oracle estimator, which knows in advance the mean regression function. We propose a two-stage refitted procedure via a data splitting technique, called refitted cross-validation, to attenuate the influence of irrelevant variables with high spurious correlations. ... F Xiao, X Luo, N Hao, YS Niu, X Xiao, G Cai, CI Amos, H …

Dive into the research topics where Ning - Hao is active. Hao Helen Zhang is Professor, Department of Mathematics, University of Arizona, Tucson, AZ 85721 (Email: hzhang@math.arizona.edu). When p is large, say more than tens of hundreds, the number of interactions is enormous and beyond the capacity of standard machines and software tools for storage and analysis. 1 Interaction Screening for Ultra-High Dimensional Data. Overview; Fingerprint; Network; Research Output (15) Similar Profiles (2) If you made any changes in Pure these will be visible here soon. Previous Courses. Home. Manuscripts. Ning Hao and Yang Feng contribute equally to this work. Numerical examples are used to demonstrate their finite sample performance.Let Y 1, …, Yn be a sequence whose underlying mean is a step function with an unknown number of the steps and unknown change points. Publication.

In particular, we develop a false discovery rate approach to the multiple change-point problem and show a strong sure coverage property for the SaRa.Abstract: Variance estimation is a fundamental problem in statistical modelling. Together they form a unique fingerprint. The naive two-stage estimator and the plug-in one-stage estimators using the lasso and smoothly clipped absolute deviation are also studied and compared. The simulation studies lend further support to our theoretical claims.

Research Output I am an associate professor of Mathematicsat the University of Arizona(UA).

I spent one year as a research associate at the … Ning’s research is partially supported by National Science Foundation and Simons Foundation. Shin, S.J., Wu, Y. and Hao, N. (2019+) A … Computationally, the iFOR procedures are designed to be simple and fast to implement. Teaching. For a data set with n observations and p predictors, the augmented design matrix including all linear and order-2 terms is of size n × (p (2) + 3p)/2. Department of Communications

Ning Hao is a very nice individual. Fingerprint Dive into the research topics where Ning - Hao is active.

Recent advances in variable selection in ultrahigh dimensional linear regression make this problem accessible. Associate Head, STEM Instruction Associate Professor, Applied Mathematics - GIDP Associate Professor, Cognitive Science - GIDP Associate Professor, Mathematics R package SSSS. View Hao Ning’s profile on LinkedIn, the world's largest professional community. Hao, N., Niu, Y.S., Xiao, F. and Zhang, H. (2018) A Super Scalable Algorithm for Short Segment Detection. Ning Hao @ the University of Arizona. Mathematics; Phone (520) 621-1961; Email nhao@email.arizona.edu; 196 Citations; 7 h-Index; 2012 2020. As a result, the realized noises are actually predicted when extra irrelevant variables are selected, leading to a serious underestimate of the level of noise. Whitepages people search is the most trusted directory. Their performances can be improved by the refitted cross-validation method proposed. View all 15 research output High-dimensional statistical learning; Change-point detection; Bioinformatics;Geometry and topology.In ultra-high dimensional data analysis, it is extremely challenging to identify important interaction effects, and a top concern in practice is computational feasibility. Journal of the American Statistical Association , … The detection of the change points, namely the positions where the mean changes, is an important problem in such fields as engineering, economics, climatology and bioscience. In theory, the interaction selection consistency is hard to achieve in high dimensional settings. Research output per year. © 2011 Royal Statistical Society. Interaction effects have heavier tails and more complex covariance structures than main effects in a random design, making theoretical analysis difficult. "We use cookies to help provide and enhance our service and tailor content. Recent external collaboration on country level. 2

Ning Hao. Ning Hao @ the University of Arizona.

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