Goodness-of-fit tests for the error distribution in nonparametric regression
نویسندگان
چکیده
منابع مشابه
Goodness-of-fit tests for the error distribution in nonparametric regression
Suppose the random vector (X,Y ) satisfies the regression model Y = m(X) + σ(X)ε, where m(·) = E(Y |·), σ2(·) = Var(Y |·) and ε is independent of X. The covariate X is d-dimensional (d ≥ 1), the response Y is one-dimensional, and m and σ are unknown but smooth functions. In this paper we study goodness-of-fit tests for the parametric form of the error distribution under this model, without assu...
متن کاملGoodness-of-fit tests for marginal distribution of long memory error fields in spatial nonparametric regression models
This paper presents a test for fitting the marginal error density of a stationary long memory error random field in spatial nonparametric regression models, where the one dimensional covariate process is also assumed to be a long memory random field, independent of the error random field. The proposed test is based on the integrated square distance between an error density estimator obtained fr...
متن کاملBayesian Nonparametric Goodness of Fit Tests
We survey in some detail the rather small literature on Bayes nonparametric Testing. We mostly concentrate on Bayesian testing of goodness of fit to a parametric null with nonparametric alternatives. We also survey briefly some related unpublished material. We discuss both methodology and posterior consistency.
متن کاملThe Comparison Between Goodness of Fit Tests for Copula
Copula functions as a model can show the relationship between variables. Appropriate copula function for a specific application is a function that shows the dependency between data in a best way. Goodness of fit tests theoretically are the best way in selection of copula function. Different ways of goodness of fit for copula exist. In this paper we will examine the goodness of fit test...
متن کاملNonparametric Goodness-of-Fit Tests for Discrete Null Distributions
Methodology extending nonparametric goodness-of-fit tests to discrete null distributions has existed for several decades. However, modern statistical software has generally failed to provide this methodology to users. We offer a revision of R’s ks.test() function and a new cvm.test() function that fill this need in the R language for two of the most popular nonparametric goodness-of-fit tests. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2010
ISSN: 0167-9473
DOI: 10.1016/j.csda.2010.02.010