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- Yanyuan Ma, Alan Edelman
- 1997

We show that if an n n Jordan block is perturbed by an O() upper k-Hessenberg matrix (k subdiagonals including the main diagonal), then generically the eigenvalues split into p rings of size k and one of size r (if r 6 = 0), where n = pk + r. This generalizes the familiar result (k = n; p = 1; r = 0) that generically the eigenvalues split into a ring of… (More)

Treating matrices as points in n 2 dimensional space, we apply geometry to study and explain algorithms for the numerical determination of the Jordan structure of a matrix. Traditional notions such as sensitivity of subspaces are replaced with angles between tangent spaces of manifolds in n 2 dimensional space. We show that the subspace sensitivity is… (More)

- H T Banks, Sarah Grove, Shuhua Hu, Yanyuan Ma
- 2005

A hierarchical Bayesian approach is developed to estimate parameters at both the individual and the population level in a HIV model, with the implementation carried out by Markov Chain Monte Carlo (MCMC) techniques. Sample numerical simulations and statistical results are provided to demonstrate the feasibility of this approach.

- James Ze Wang, Gio Wiederhold, Hector Garcia-Molina, Stephen T C Wong, Yuan Wang, Jia Li +39 others

I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, a s a dissertation for the degree of Doctor of Philosophy. I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, a s a dissertation for the degree of Doctor of Philosophy. I certify that I… (More)

- Yanyuan Ma, Liping Zhu
- 2016

We provide a novel and completely different approach to dimension-reduction problems from the existing literature. We cast the dimension-reduction problem in a semiparametric estimation framework and derive estimating equations. Viewing this problem from the new angle allows us to derive a rich class of estimators, and obtain the classical dimension… (More)

Microarrays are one of the most widely used high-throughput technologies. One of the main problems in the area is that conventional estimates of the variances required in the t-statistic and other statistics are unreliable due to the small number of replications. Various methods have been proposed in the literature to overcome this lack of degrees of… (More)

- Stéphane Guerrier, Nabil Mili, Roberto Molinari, Samuel Orso, Marco Avella-Medina, Yanyuan Ma
- Frontiers in genetics
- 2016

Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. To do so, many of the recently proposed classification methods require some form of dimension-reduction of the problem which finally provide a… (More)

- Tianle Chen, Yanyuan Ma, Yuanjia Wang
- Statistics in medicine
- 2015

We propose a simple approach predicting the cumulative risk of disease accommodating predictors with time-varying effects and outcomes subject to censoring. We use a nonparametric function for the coefficient of the time-varying effect and handle censoring through self-consistency equations that redistribute the probability mass of censored outcomes to the… (More)

- Yanyuan Ma, Yuanjia Wang
- Statistics in medicine
- 2014

Huntington's disease (HD) is a neurodegenerative disorder with a dominant genetic mode of inheritance caused by an expansion of CAG repeats on chromosome 4. Typically, a longer sequence of CAG repeat length is associated with increased risk of experiencing earlier onset of HD. Previous studies of the association between HD onset age and CAG length have… (More)

We take a semiparametric approach in fitting a linear transformation model to a right censored data when predictive variables are subject to measurement errors. We construct consistent estimating equations when repeated measurements of a surrogate of the unobserved true predictor are available. The proposed approach applies under minimal assumptions on the… (More)