Minimum distance lack-of-fit tests under long memory errors

Minimum Distance Lack-of-Fit Tests under Long Memory Errors

This paper discusses some tests of lack-of-fit of a parametric regression model when errors form a long memory moving average process with the long memory parameter 0 < d < 1/2, and when design is non-random and uniform on [0, 1]. These tests are based on certain minimized distances between a nonparametric regression function estimator and the parametric model being fitted. The paper investigat...

Minimum distance lack-of-fit tests for fixed design

This paper discusses a class of tests of lack-of-fit of a parametric regression model when design is non-random and uniform on [0,1]. These tests are based on certain minimized distances between a nonparametric regression function estimator and the parametric model being fitted. We investigate asymptotic null distributions of the proposed tests, their consistency and asymptotic power against a ...

Goodness-of-fit testing under long memory

In this talk we shall discuss the problem of fitting a distribution function to the marginal distribution of a long memory process. It is observed that unlike in the i.i.d. set up, classical tests based on empirical process are relatively easy to implement. More importantly, we discuss fitting the marginal distribution of the error process in location, scale and linear regression models. An int...

Nonparametric Smoothing and Lack-of-Fit Tests

A solution to get the problem off, have you found it? Really? What kind of solution do you resolve the problem? From what sources? Well, there are so many questions that we utter every day. No matter how you will get the solution, it will mean better. You can take the reference from some books. And the nonparametric smoothing and lack of fit tests is one book that we really recommend you to rea...

Empirical L2-distance lack-of-fit tests for Tobit regression models