Tests for Independence in Nonparametric Regression
نویسندگان
چکیده
منابع مشابه
Tests for Independence in Nonparametric Regression
Consider the nonparametric regression model Y = m(X) + ε, where the function m is smooth, but unknown. We construct tests for the independence of ε and X, based on n independent copies of (X, Y ). The testing procedures are based on differences of neighboring Y ’s. We establish asymptotic results for the proposed tests statistics, investigate their finite sample properties through a simulation ...
متن کاملTests for independence in nonparametric regression ( supplement )
Proof of (2.17) From (2.10) we have with high probability for large n and uniformly in x and y √ n(F n (x, y) − ˆ F X (x) ˆ G(y)) ≤ α n x, y + log 2 n n − G(y)α n (x, ∞) − ˆ F X (x) α n ∞, y − log 2 n n + 2C log 2 n √ n , √ n(F n (x, y) − ˆ F X (x) ˆ G(y)) ≥ α n x, y − log 2 n n − G(y)α n (x, ∞) − ˆ F X (x) α n ∞, y + log 2 n n − 2C log 2 n √ n. Set V n,0 = √ n(F n − ˆ F X ˆ G). From (2.12) and...
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Three simple and explicit procedures for testing the independence of two multi-dimensional random variables are described. Two of the associated test statistics (L1, log-likelihood) are defined when the empirical distribution of the variables is restricted to finite partitions. A third test statistic is defined as a kernel-based independence measure. Two kinds of tests are provided. Distributio...
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A non parametric test of the mutual independence between many numerical random vectors is proposed. This test is based on a characterization of mutual independence defined from probabilities of half-spaces in a combinatorial formula of Möbius. As such, it is a natural generalization of tests of independence between univariate random variables using the empirical distribution function. If the nu...
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ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2006
ISSN: 1556-5068
DOI: 10.2139/ssrn.930597