Comparison study on some classical lack-of-fit tests in regression models

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Show simple item record Shrestha, Tej Bahadur 2010-06-30T14:35:36Z 2010-06-30T14:35:36Z 2010-06-30T14:35:36Z
dc.description.abstract 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. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject lack-of-fit tests en_US
dc.subject regression models en_US
dc.subject Simulation Studies en_US
dc.title Comparison study on some classical lack-of-fit tests in regression models en_US
dc.type Report en_US Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Weixing Song en_US
dc.subject.umi Statistics (0463) en_US 2010 en_US August en_US

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