3 citations · 6 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
Penalized Likelihood Methods for Modeling Count Data
Minh Thu Bui, Cornelis J. Potgieter, Akihito Kamata
The paper considers parameter estimation in count data models using penalized likelihood methods. The motivating data consists of multiple independent count variables with a modera…
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…
Density Deconvolution for Generalized Skew-Symmetric Distributions
Cornelis J. Potgieter
This paper develops a density deconvolution estimator that assumes the density of interest is a member of the generalized skew-symmetric (GSS) family of distributions. Estimation o…
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…
Tail Estimation via Sample Splitting: A Two-Sample Framework for Stable-like Distributions
Cornelis J. Potgieter, Jacques van Appel, Sudharshan Samaratunga
Stable distributions provide a flexible framework for modeling heavy-tailed and skewed data, with the stability index quantifying tail heaviness. We propose a new semiparametri…
Nonparametric Cusum Charts for Angular Data with Applications in Health Science and Astrophysics
F. Lombard, Douglas M. Hawkins, Cornelis Potgieter
This paper develops non-parametric rotation invariant CUSUMs suited to the detection of changes in the mean direction as well as changes in the concentration parameter of angular d…