Spelling suggestions: "subject:"lack.it tests""
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Comparison study on some classical lack-of-fit tests in regression modelsShrestha, Tej Bahadur January 1900 (has links)
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.
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Cluster-based lack of fit tests for nonlinear regression modelsMunasinghe, Wijith Prasantha January 1900 (has links)
Doctor of Philosophy / Department of Statistics / James W. Neill / Checking the adequacy of a proposed parametric nonlinear regression model is important
in order to obtain useful predictions and reliable parameter inferences. Lack of fit is said to
exist when the regression function does not adequately describe the mean of the response
vector. This dissertation considers asymptotics, implementation and a comparative performance
for the likelihood ratio tests suggested by Neill and Miller (2003). These tests use
constructed alternative models determined by decomposing the lack of fit space according to
clusterings of the observations. Clusterings are selected by a maximum power strategy and a
sequence of statistical experiments is developed in the sense of Le Cam. L2 differentiability
of the parametric array of probability measures associated with the sequence of experiments
is established in this dissertation, leading to local asymptotic normality. Utilizing contiguity,
the limit noncentral chi-square distribution under local parameter alternatives is then
derived. For implementation purposes, standard linear model projection algorithms are
used to approximate the likelihood ratio tests, after using the convexity of a class of fuzzy
clusterings to form a smooth alternative model which is necessarily used to approximate the
corresponding maximum optimal statistical experiment. It is demonstrated empirically that
good power can result by allowing cluster selection to vary according to different points along
the expectation surface of the proposed nonlinear regression model. However, in some cases,
a single maximum clustering suffices, leading to the development of a Bonferroni adjusted
multiple testing procedure. In addition, the maximin clustering based likelihood ratio tests
were observed to possess markedly better simulated power than the generalized likelihood
ratio test with semiparametric alternative model presented by Ciprian and Ruppert (2004).
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Tests d'ajustement reposant sur les méthodes d'ondelettes dans les modèles ARMA avec un terme d'erreur qui est une différence de martingales conditionnellement hétéroscédastiqueLiou, Chu Pheuil 09 1900 (has links)
No description available.
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