Constrained Statistical Inference in Regression

<p>Regression analysis constitutes a large portion of the statistical repertoire in applications. In case where such analysis is used for exploratory purposes with no previous knowledge of the structure one would not wish to impose any constraints on the problem. But in many applications we are interested in a simple parametric model to describe the structure of a system with some prior knowledge of the structure. An important example of this occurs when the experimenter has the strong belief that the regression function changes monotonically in some or all of the predictor variables in a region of interest. The analyses needed for statistical inference under such constraints are nonstandard. The specific aim of this study is to introduce a technique which can be used for statistical inferences of a multivariate simple regression with some non-standard constraints.</p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Campus Only Dissertation

Discipline thesis:degree_discipline

Mathematics

Year dc:date.available

2014

Author dc:creator

• Peiris, Thelge Buddika

Contributors dc:contributor

• Bhattacharya, Bhaskar
• Olive, David
• Ban, Dubravka

• chi-bar square distribution
• dual cone
• hyperplane
• in-equalities
• least favorable disribution
• nonnegative least squre

Repository record dc:identifier

https://opensiuc.lib.siu.edu/dissertations/934

OAI identifier oai:identifier

oai:opensiuc.lib.siu.edu:dissertations-1937