Master of Science / Department of Statistics / Weixing Song / The relationship between a random variable and a random vector is often investigated
through the regression modeling. Because of its relative simplicity and ease of interpretation,
a particular parametric form is often assumed for the regression function. If the pre-specified
function form truly reflects the truth, then the resulting estimators and inference procedures
would be reliable and efficient. But if the regression function does not represent the true
relationship between the response and the predictors, then the inference results might be
very misleading. Therefore, lack-of-fit test should be an indispensable part in regression
modeling. This report compares the finite sample performance of several classical lack-of-fit
tests in regression models via simulation studies. It has three chapters. The conception
of the lack-of-fit test, together with its basic setup, is briefly introduced in Chapter 1;
then several classical lack-of-fit test procedures are discussed in Chapter 2; finally, thorough simulation studies are conducted in Chapter 3 to assess the finite sample performance of each procedure introduced in Chapter 2. Some conclusions are also summarized in Chapter
3. A list of MATLAB codes that are used for the simulation studies is given in the appendix.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/4247 |
| Date | January 1900 |
| Creators | Shrestha, Tej Bahadur |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Report |
Page generated in 0.0019 seconds