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A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects

In this paper,we present a test of independence between the response variable, which can be discrete or continuous, and a continuous covariate after adjusting for heteroscedastic treatment effects. The method involves first augmenting each pair of the data for all treatments with a fixed number of n...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Wang, Haiyan, Tolos, Siti Marponga, Wang, Suojin
التنسيق: مقال
اللغة:English
منشور في: Statistical Society of Canada 2010
الموضوعات:
Q Science (General)
QA Mathematics
الوصول للمادة أونلاين:http://irep.iium.edu.my/5420/1/Test.of.independence.Wang.Tolos.Wang.cjs.pdf
http://irep.iium.edu.my/5420/
http://onlinelibrary.wiley.com/doi/10.1002/cjs.10068/abstract
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spelling my.iium.irep.54202011-12-07T08:43:24Z http://irep.iium.edu.my/5420/ A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects Wang, Haiyan Tolos, Siti Marponga Wang, Suojin Q Science (General) QA Mathematics In this paper,we present a test of independence between the response variable, which can be discrete or continuous, and a continuous covariate after adjusting for heteroscedastic treatment effects. The method involves first augmenting each pair of the data for all treatments with a fixed number of nearest neighbours as pseudo-replicates. Then a test statistic is constructed by taking the difference of two quadratic forms. The statistic is equivalent to the average lagged correlations between the response and nearest neighbour local estimates of the conditional mean of response given the covariate for each treatment group. This approach effectively eliminates the need to estimate the nonlinear regression function. The asymptotic distribution of the proposed test statistic is obtained under the null and local alternatives. Although using a fixed number of nearest neighbours pose significant difficulty in the inference compared to that allowing the number of nearest neighbours to go to infinity, the parametric standardizing rate for our test statistics is obtained. Numerical studies show that the new test procedure has robust power to detect nonlinear dependency in the presence of outliers that might result from highly skewed distributions. Statistical Society of Canada 2010-09 Article REM application/pdf en http://irep.iium.edu.my/5420/1/Test.of.independence.Wang.Tolos.Wang.cjs.pdf Wang, Haiyan and Tolos, Siti Marponga and Wang, Suojin (2010) A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects. The Canadian Journal of Statistics, 38 (3). pp. 408-433. ISSN 0319-5724 (P), 1708-945X (O) http://onlinelibrary.wiley.com/doi/10.1002/cjs.10068/abstract 10.1002/cjs.10068
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Wang, Haiyan
Tolos, Siti Marponga
Wang, Suojin
A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects
description In this paper,we present a test of independence between the response variable, which can be discrete or continuous, and a continuous covariate after adjusting for heteroscedastic treatment effects. The method involves first augmenting each pair of the data for all treatments with a fixed number of nearest neighbours as pseudo-replicates. Then a test statistic is constructed by taking the difference of two quadratic forms. The statistic is equivalent to the average lagged correlations between the response and nearest neighbour local estimates of the conditional mean of response given the covariate for each treatment group. This approach effectively eliminates the need to estimate the nonlinear regression function. The asymptotic distribution of the proposed test statistic is obtained under the null and local alternatives. Although using a fixed number of nearest neighbours pose significant difficulty in the inference compared to that allowing the number of nearest neighbours to go to infinity, the parametric standardizing rate for our test statistics is obtained. Numerical studies show that the new test procedure has robust power to detect nonlinear dependency in the presence of outliers that might result from highly skewed distributions.
format Article
author Wang, Haiyan
Tolos, Siti Marponga
Wang, Suojin
author_facet Wang, Haiyan
Tolos, Siti Marponga
Wang, Suojin
author_sort Wang, Haiyan
title A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects
title_short A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects
title_full A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects
title_fullStr A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects
title_full_unstemmed A distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects
title_sort distribution free test to detect general dependence between a response variable and a covariate in the presence of heteroscedastic treatment effects
publisher Statistical Society of Canada
publishDate 2010
url http://irep.iium.edu.my/5420/1/Test.of.independence.Wang.Tolos.Wang.cjs.pdf
http://irep.iium.edu.my/5420/
http://onlinelibrary.wiley.com/doi/10.1002/cjs.10068/abstract
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