Three Statistical Problems With Imprecisely or Incompletely Observed Data

Imprecisely or incompletely observed data often appear in engineering, epidemiology and economic studies, observations on certain variables may be grouped or measured with errors, which poses challenges to the usual statistical methods. This thesis consists of three studies in this general area. First, we study linear calibration of a crude device to a more accurate one. Second, we use the Markov Chain Monte Carlo (MCMC) method to handle a grouped independent variable in a linear model as motivated by a residential energy study. The third study is concerned with an approximate minimum Hellinger distance estimator (AMHDE) under appropriate grouping of data from a continuous variable. The estimator is shown to be asymptotically normal with good efficiency and robustness.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Lin, Nan

Contributors dc:contributor

• He, Xuming

• Statistics

Language dc:language

eng

Identifier

(MiAaPQ)AAI3101906

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/87396