We propose the maximin efficiency robust test（MERT） for multiple nuisance parameters based on theories about the maximin efficiency robust test for only one nuisance parameter and investigate some theoretical proper...We propose the maximin efficiency robust test（MERT） for multiple nuisance parameters based on theories about the maximin efficiency robust test for only one nuisance parameter and investigate some theoretical properties about this robust test.We explore some theoretical properties about the power of the MERT for multiple nuisance parameters in a specified scenario intuitively further more.We also propose a meaningful example from statistical genetic field to which the MERT for multiple nuisance parameters can be well applied.Extensive simulation studies are conducted to testify the robustness of the MERT for multiple nuisance parameters.展开更多
In this article a new approach for checking the adequacy of GARCH-type models in time series was proposed. The resulted tests involve weight functions, which provide them with the flexibility in choosing scores to enh...In this article a new approach for checking the adequacy of GARCH-type models in time series was proposed. The resulted tests involve weight functions, which provide them with the flexibility in choosing scores to enhance power performance. The choice of weight functions and the power properties of the tests are studied. For a large number of alternatives, asymptotically distribution-free maximin test is constructed. The tests are asymptotically chi-squared under the null hypothesis and easy to implement. Simulation results indicate that the tests perform well.展开更多
This paper is devoted to the goodness-of-fit test for the general autoregressive models in time series. By averaging for the weighted residuals, we construct a score type test which is asymptotically standard chi-squa...This paper is devoted to the goodness-of-fit test for the general autoregressive models in time series. By averaging for the weighted residuals, we construct a score type test which is asymptotically standard chi-squared under the null and has some desirable power properties under the alternatives. Specifically, the test is sensitive to alternatives and can detect the alternatives approaching, along a direction, the null at a rate that is arbitrarily close to n-1/2. Furthermore, when the alternatives are not directional, we construct asymptotically distribution-free maximin tests for a large class of alternatives. The performance of the tests is evaluated through simulation studies.展开更多
The paper proposes and studies some diagnostic tools for checking the goodness-of-fit of general parametric vector autoregressive models in time series. The resulted tests are asymptotically chi-squared under the null...The paper proposes and studies some diagnostic tools for checking the goodness-of-fit of general parametric vector autoregressive models in time series. The resulted tests are asymptotically chi-squared under the null hypothesis and can detect the alternatives converging to the null at a parametric rate. The tests involve weight functions,which provides us with the flexibility to choose scores for enhancing power performance,especially under directional alternatives. When the alternatives are not directional,we construct asymptotically distribution-free maximin tests for a large class of alternatives. A possibility to construct score-based omnibus tests is discussed when the alternative is saturated. The power performance is also investigated. In addition,when the sample size is small,a nonparametric Monte Carlo test approach for dependent data is proposed to improve the performance of the tests. The algorithm is easy to implement. Simulation studies and real applications are carried out for illustration.展开更多
We suggest the score type tests for goodness-of-fit of conditional heteroscedasticity models in both univariate and multivariate time series.The tests can detect the alternatives converging to the null at a parametric...We suggest the score type tests for goodness-of-fit of conditional heteroscedasticity models in both univariate and multivariate time series.The tests can detect the alternatives converging to the null at a parametric rate.Weight functions are involved in the construction of the tests,which provides us with the flexibility to choose scores,especially under directional alternatives,for enhancing power performance.Furthermore,when the alternatives are not directional,we construct asymptotically distribution-free maximin tests for a large class of alternatives.A possibility to construct score-based omnibus tests is discussed when the alternative is saturated.The power performance is also investigated.A simulation study is carried out and a real data is analyzed.展开更多
基金supported by the Natural Science Foundation of China(11401240,11471135)the self-determined research funds of CCNU from the colleges’basic research of MOE(CCNU15A05038,CCNU15ZD011)
文摘We propose the maximin efficiency robust test（MERT） for multiple nuisance parameters based on theories about the maximin efficiency robust test for only one nuisance parameter and investigate some theoretical properties about this robust test.We explore some theoretical properties about the power of the MERT for multiple nuisance parameters in a specified scenario intuitively further more.We also propose a meaningful example from statistical genetic field to which the MERT for multiple nuisance parameters can be well applied.Extensive simulation studies are conducted to testify the robustness of the MERT for multiple nuisance parameters.
基金supported by a grant from the Research Grants Council of Hong Kong.Jianhong Wu was also supported by a grant from Humanities & Social Sciences in Chinese University (07JJD790154)the Youth Talent Foundation of Zhejiang GongShang University (Q09-12)
文摘In this article a new approach for checking the adequacy of GARCH-type models in time series was proposed. The resulted tests involve weight functions, which provide them with the flexibility in choosing scores to enhance power performance. The choice of weight functions and the power properties of the tests are studied. For a large number of alternatives, asymptotically distribution-free maximin test is constructed. The tests are asymptotically chi-squared under the null hypothesis and easy to implement. Simulation results indicate that the tests perform well.
基金grant from the Research Grants Council of Hong Kong
文摘This paper is devoted to the goodness-of-fit test for the general autoregressive models in time series. By averaging for the weighted residuals, we construct a score type test which is asymptotically standard chi-squared under the null and has some desirable power properties under the alternatives. Specifically, the test is sensitive to alternatives and can detect the alternatives approaching, along a direction, the null at a rate that is arbitrarily close to n-1/2. Furthermore, when the alternatives are not directional, we construct asymptotically distribution-free maximin tests for a large class of alternatives. The performance of the tests is evaluated through simulation studies.
基金supported by Research Grants Council of Hong Kong (Grant No. HKBU2-030/07P)Wu Jianhong was also supported by a grant from Humanities and Social Sciences in Chinese University(Grant No.07JJD790154)+1 种基金Science Fund for Young Scholars of Zhejiang Gongshang University (Grant No. Q09-12)Zhejiang Provincial Natural Science Foundation of China (Grant No.Y6090172)
文摘The paper proposes and studies some diagnostic tools for checking the goodness-of-fit of general parametric vector autoregressive models in time series. The resulted tests are asymptotically chi-squared under the null hypothesis and can detect the alternatives converging to the null at a parametric rate. The tests involve weight functions,which provides us with the flexibility to choose scores for enhancing power performance,especially under directional alternatives. When the alternatives are not directional,we construct asymptotically distribution-free maximin tests for a large class of alternatives. A possibility to construct score-based omnibus tests is discussed when the alternative is saturated. The power performance is also investigated. In addition,when the sample size is small,a nonparametric Monte Carlo test approach for dependent data is proposed to improve the performance of the tests. The algorithm is easy to implement. Simulation studies and real applications are carried out for illustration.
基金supported by the Research Grants Council of Hong Kong (Grant No.HKBU2030/07P)Wu Jianhong was also supported by National Natural Science Foundation of China (Grant No.11001238)+2 种基金Humanities and Social Sciences in Chinese University (Grant No.07JJD790154)Zhejiang Provincial Natural Science Foundation of China (Grant No.Y6090172)Science Fund for Young Scholars of Zhejiang Gongshang University,China (Grant No.Q09-12)
文摘We suggest the score type tests for goodness-of-fit of conditional heteroscedasticity models in both univariate and multivariate time series.The tests can detect the alternatives converging to the null at a parametric rate.Weight functions are involved in the construction of the tests,which provides us with the flexibility to choose scores,especially under directional alternatives,for enhancing power performance.Furthermore,when the alternatives are not directional,we construct asymptotically distribution-free maximin tests for a large class of alternatives.A possibility to construct score-based omnibus tests is discussed when the alternative is saturated.The power performance is also investigated.A simulation study is carried out and a real data is analyzed.
