4 citations · 5 across the 15 of their papers we have counts for
11 papers · 1 filter
High-dimensional sliced inverse regression with endogeneity
Linh H. Nghiem, Francis. K. C. Hui, Samuel Muller +1
Sliced inverse regression (SIR) is a popular sufficient dimension reduction method that identifies a few linear transformations of the covariates without losing regression informat…
Logistic Regression Model for Differentially-Private Matrix Masked Data
Linh H Nghiem, Aidong A. Ding, Samuel Wu
A recently proposed scheme utilizing local noise addition and matrix masking enables data collection while protecting individual privacy from all parties, including the central dat…
Random effects model-based sufficient dimension reduction for independent clustered data
Linh H. Nghiem, F. K. C. Hui
Sufficient dimension reduction (SDR) is a popular class of regression methods which aim to find a small number of linear combinations of covariates that capture all the information…
A Linear Errors-in-Variables Model with Unknown Heteroscedastic Measurement Errors
Linh H. Nghiem, Cornelis J. Potgieter
In the classic measurement error framework, covariates are contaminated by independent additive noise. This paper considers parameter estimation in such a linear errors-in-variable…
Likelihood-based surrogate dimension reduction
Linh H. Nghiem, Francis K. C. Hui, Samuel Mueller +1
We consider the problem of surrogate sufficient dimension reduction, that is, estimating the central subspace of a regression model, when the covariates are contaminated by measure…
Sparse Sliced Inverse Regression via Cholesky Matrix Penalization
Linh Nghiem, Francis K. C. Hui, Samuel Mueller +1
We introduce a new sparse sliced inverse regression estimator called Cholesky matrix penalization and its adaptive version for achieving sparsity in estimating the dimensions of th…
Screening methods for linear errors-in-variables models in high dimensions
Linh Nghiem, Francis K. C. Hui, Samuel Mueller +1
Microarray studies, in order to identify genes associated with an outcome of interest, usually produce noisy measurements for a large number of gene expression features from a smal…
Bayesian Regularization of Gaussian Graphical Models with Measurement Error
Michael Byrd, Linh Nghiem, Monnie McGee
We consider a framework for determining and estimating the conditional pairwise relationships of variables when the observed samples are contaminated with measurement error in high…
Estimation in linear errors-in-variables models with unknown error distribution
Linh Nghiem, Michael Byrd, Cornelis Potgieter
Parameter estimation in linear errors-in-variables models typically requires that the measurement error distribution be known (or estimable from replicate data). A generalized meth…
Simulation-Selection-Extrapolation: Estimation in High-Dimensional Errors-in-Variables Models
Linh Nghiem, Cornelis Potgieter
This paper considers errors-in-variables models in a high-dimensional setting where the number of covariates can be much larger than the sample size, and there are only a small num…
Phase Function Density Deconvolution with Heteroscedastic Measurement Error of Unknown Type
Linh Nghiem, Cornelis J. Potgieter
It is important to properly correct for measurement error when estimating density functions associated with biomedical variables. These estimators that adjust for measurement error…