NEW NONPARAMETRIC STATISTICAL METHODS FOR IMPERFECTLY OBSERVED DATA

Abstract

Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutrition, this project aims to develop novel and highly effective statistical methodology for solving contemporary problems involving new types of imperfectly observed data. The expected outcomes will solve frontier problems, where information can only be accessed through sop..

Related publications (2)

SEMI-PARAMETRIC PREDICTION INTERVALS IN SMALL AREAS WHEN AUXILIARY DATA ARE MEASURED WITH ERROR

Gauri Datta, Aurore Delaigle, Peter Hall, Li Wang

2018-10-01

In recent years, demand for reliable small area statistics has considerably increased, but the size of samples obtained in small a..

Nonparametric Kernel Methods with Errors-in-Variables: Constructing Estimators, Computing them, and Avoiding Common Mistakes

Aurore Delaigle

2014-06-01

Estimating a curve nonparametrically from data measured with error is a difficult problem that has been studied by many authors. C..