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Leandro Pardo - One of the best experts on this subject based on the ideXlab platform.
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Robust Wald-type tests for non-homogeneous observations based on the minimum density power divergence estimator
Metrika, 2018Co-Authors: Ayanendranath Basu, Abhik Ghosh, Nirian Martin, Leandro PardoAbstract:This paper considers the problem of robust Hypothesis testing under non-identically distributed data. We propose Wald-type tests for both simple and composite Hypothesis for independent but non-homogeneous observations based on the robust minimum density power divergence estimator of the common underlying parameter. Asymptotic and theoretical robustness properties of the proposed tests are discussed. Application to the problem of testing for the general Linear Hypothesis in a generalized Linear model with a fixed-design has been considered in detail with specific illustrations for its special cases under the normal and Poisson distributions.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
Electronic Journal of Statistics, 2017Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. A family of robust Wald type tests are considered here, where the minimum density power divergence estimator is used instead of the maximum likelihood estimator. We obtain the asymptotic distribution and also study the robustness properties of these Wald type test statistics. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
arXiv: Statistics Theory, 2016Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. The family of tests considered is based on the minimum density power divergence estimator instead of the maximum likelihood estimator and it is referred to as the Wald-type test statistic in the paper. We obtain the asymptotic distribution and also study the robustness properties of the Wald type test statistic. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
Ayanendranath Basu - One of the best experts on this subject based on the ideXlab platform.
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Robust Wald-type tests for non-homogeneous observations based on the minimum density power divergence estimator
Metrika, 2018Co-Authors: Ayanendranath Basu, Abhik Ghosh, Nirian Martin, Leandro PardoAbstract:This paper considers the problem of robust Hypothesis testing under non-identically distributed data. We propose Wald-type tests for both simple and composite Hypothesis for independent but non-homogeneous observations based on the robust minimum density power divergence estimator of the common underlying parameter. Asymptotic and theoretical robustness properties of the proposed tests are discussed. Application to the problem of testing for the general Linear Hypothesis in a generalized Linear model with a fixed-design has been considered in detail with specific illustrations for its special cases under the normal and Poisson distributions.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
Electronic Journal of Statistics, 2017Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. A family of robust Wald type tests are considered here, where the minimum density power divergence estimator is used instead of the maximum likelihood estimator. We obtain the asymptotic distribution and also study the robustness properties of these Wald type test statistics. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
arXiv: Statistics Theory, 2016Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. The family of tests considered is based on the minimum density power divergence estimator instead of the maximum likelihood estimator and it is referred to as the Wald-type test statistic in the paper. We obtain the asymptotic distribution and also study the robustness properties of the Wald type test statistic. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
Nirian Martin - One of the best experts on this subject based on the ideXlab platform.
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Robust Wald-type tests for non-homogeneous observations based on the minimum density power divergence estimator
Metrika, 2018Co-Authors: Ayanendranath Basu, Abhik Ghosh, Nirian Martin, Leandro PardoAbstract:This paper considers the problem of robust Hypothesis testing under non-identically distributed data. We propose Wald-type tests for both simple and composite Hypothesis for independent but non-homogeneous observations based on the robust minimum density power divergence estimator of the common underlying parameter. Asymptotic and theoretical robustness properties of the proposed tests are discussed. Application to the problem of testing for the general Linear Hypothesis in a generalized Linear model with a fixed-design has been considered in detail with specific illustrations for its special cases under the normal and Poisson distributions.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
Electronic Journal of Statistics, 2017Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. A family of robust Wald type tests are considered here, where the minimum density power divergence estimator is used instead of the maximum likelihood estimator. We obtain the asymptotic distribution and also study the robustness properties of these Wald type test statistics. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
arXiv: Statistics Theory, 2016Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. The family of tests considered is based on the minimum density power divergence estimator instead of the maximum likelihood estimator and it is referred to as the Wald-type test statistic in the paper. We obtain the asymptotic distribution and also study the robustness properties of the Wald type test statistic. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
Abhik Ghosh - One of the best experts on this subject based on the ideXlab platform.
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Robust Wald-type tests for non-homogeneous observations based on the minimum density power divergence estimator
Metrika, 2018Co-Authors: Ayanendranath Basu, Abhik Ghosh, Nirian Martin, Leandro PardoAbstract:This paper considers the problem of robust Hypothesis testing under non-identically distributed data. We propose Wald-type tests for both simple and composite Hypothesis for independent but non-homogeneous observations based on the robust minimum density power divergence estimator of the common underlying parameter. Asymptotic and theoretical robustness properties of the proposed tests are discussed. Application to the problem of testing for the general Linear Hypothesis in a generalized Linear model with a fixed-design has been considered in detail with specific illustrations for its special cases under the normal and Poisson distributions.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
Electronic Journal of Statistics, 2017Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. A family of robust Wald type tests are considered here, where the minimum density power divergence estimator is used instead of the maximum likelihood estimator. We obtain the asymptotic distribution and also study the robustness properties of these Wald type test statistics. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
arXiv: Statistics Theory, 2016Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. The family of tests considered is based on the minimum density power divergence estimator instead of the maximum likelihood estimator and it is referred to as the Wald-type test statistic in the paper. We obtain the asymptotic distribution and also study the robustness properties of the Wald type test statistic. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
Abhijit Mandal - One of the best experts on this subject based on the ideXlab platform.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
Electronic Journal of Statistics, 2017Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. A family of robust Wald type tests are considered here, where the minimum density power divergence estimator is used instead of the maximum likelihood estimator. We obtain the asymptotic distribution and also study the robustness properties of these Wald type test statistics. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
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a wald type test statistic for testing Linear Hypothesis in logistic regression models based on minimum density power divergence estimator
arXiv: Statistics Theory, 2016Co-Authors: Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal, Nirian Martin, Leandro PardoAbstract:In this paper a robust version of the classical Wald test statistics for Linear Hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. The family of tests considered is based on the minimum density power divergence estimator instead of the maximum likelihood estimator and it is referred to as the Wald-type test statistic in the paper. We obtain the asymptotic distribution and also study the robustness properties of the Wald type test statistic. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
