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学者姓名：谢田法
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Abstract ：
Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses, and there is uncertainty on which scoring function is used to model the responses. In this paper, we address the scoring function uncertainty of continuous response ranking problems by proposing a ranking model averaging (RMA) method. With a set of candidate models varied by scoring functions, RMA assigns weights for each model determined by a K-fold crossvalidation criterion based on pairwise loss. We provide two main theoretical properties for RMA. First, we prove that the averaging ranking predictions of RMA are asymptotically optimal in achieving the lowest possible ranking risk. Second, we provide a bound on the difference between the empirical RMA weights and theoretical optimal ones, and we show that RMA weights are consistent. Simulation results validate RMA superiority over competing methods in reducing ranking risk. Moreover, when applied to empirical examples-order priority, wine quality, and piston slap noise-RMA shows its effectiveness in building accurate ranking systems.
Keyword ：
model averaging model averaging crossvalidation crossvalidation pairwise loss pairwise loss ranking problem ranking problem asymptotic optimality asymptotic optimality
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| GB/T 7714 | Feng, Ziheng , He, Baihua , Xie, Tianfa et al. Ranking Model Averaging: Ranking Based on Model Averaging [J]. | INFORMS JOURNAL ON COMPUTING , 2024 . |
| MLA | Feng, Ziheng et al. "Ranking Model Averaging: Ranking Based on Model Averaging" . | INFORMS JOURNAL ON COMPUTING (2024) . |
| APA | Feng, Ziheng , He, Baihua , Xie, Tianfa , Zhang, Xinyu , Zong, Xianpeng . Ranking Model Averaging: Ranking Based on Model Averaging . | INFORMS JOURNAL ON COMPUTING , 2024 . |
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Abstract ：
In studies involving infectious diseases or two-step treatment research, the 2x2 contingency table with a structural zero serves as a common framework for data collection. In biomedical studies and related fields, inferring the risk differences through confidence intervals is of significant importance. However, the reliability of approximate intervals based on asymptotic normality is questionable, particularly in small samples. This paper aims to address this limitation by proposing exact intervals for the risk difference, enhancing both reliability and precision. Initially, a novel interval is introduced using the restricted most probable method, which is then optimized via the h-function method to create an optimal exact interval. A comparative analysis is conducted, contrasting this proposed interval with others derived from methods such as the score method, inferential model method, and modified inferential model method. Numerical studies demonstrate the superiority of the proposed interval in terms of both infimum coverage probability and total interval length. Additionally, two illustrative examples are provided to demonstrate the practical application of this interval in real-world scenarios.
Keyword ：
h-function method h-function method Infimum coverage probability Infimum coverage probability Restricted most probable statistic Restricted most probable statistic Total interval length Total interval length
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| GB/T 7714 | Cao, Xingyun , Wang, Weizhen , Xie, Tianfa . An optimal exact interval for risk difference in 2x2 contingency tables with structural zeros [J]. | STATISTICAL METHODS AND APPLICATIONS , 2024 . |
| MLA | Cao, Xingyun et al. "An optimal exact interval for risk difference in 2x2 contingency tables with structural zeros" . | STATISTICAL METHODS AND APPLICATIONS (2024) . |
| APA | Cao, Xingyun , Wang, Weizhen , Xie, Tianfa . An optimal exact interval for risk difference in 2x2 contingency tables with structural zeros . | STATISTICAL METHODS AND APPLICATIONS , 2024 . |
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Abstract ：
This paper studies the least squares model averaging methods for two non-nested linear models. It is proved that the Mallows model averaging weight of the true model is root-n consistent. Then the authors develop a penalized Mallows criterion which ensures that the weight of the true model equals 1 with probability tending to 1 and thus the averaging estimator is asymptotically normal. If neither candidate model is true, the penalized Mallows averaging estimator is asymptotically optimal. Simulation results show the selection consistency of the penalized Mallows method and the superiority of the model averaging approach compared with the model selection estimation.
Keyword ：
consistency consistency least squares model averaging least squares model averaging Asymptotic optimality Asymptotic optimality non-nested models non-nested models
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| GB/T 7714 | Gao, Yan , Xie, Tianfa , Zou, Guohua . Least Squares Model Averaging for Two Non-Nested Linear Models [J]. | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2023 , 36 (1) : 412-432 . |
| MLA | Gao, Yan et al. "Least Squares Model Averaging for Two Non-Nested Linear Models" . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 36 . 1 (2023) : 412-432 . |
| APA | Gao, Yan , Xie, Tianfa , Zou, Guohua . Least Squares Model Averaging for Two Non-Nested Linear Models . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2023 , 36 (1) , 412-432 . |
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Abstract ：
This paper investigates the hypothesis test of the parametric component in partial functional linear regression models. Based on a rank score function, the authors develop a rank test using functional principal component analysis, and establish the asymptotic properties of the resulting test under null and local alternative hypotheses. A simulation study shows that the proposed test procedure has good size and power with finite sample sizes. The authors also present an illustration through fitting the Berkeley Growth Data and testing the effect of gender on the height of kids.
Keyword ：
Asymptotic normality Asymptotic normality functional principal component analysis functional principal component analysis Karhunen-loeve expansion Karhunen-loeve expansion local alternative hypothesis local alternative hypothesis rank regression rank regression
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| GB/T 7714 | Xie, Tianfa , Cao, Ruiyuan , Yu, Ping . Rank-Based Test for Partial Functional Linear Regression Models [J]. | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2020 , 33 (5) : 1571-1584 . |
| MLA | Xie, Tianfa et al. "Rank-Based Test for Partial Functional Linear Regression Models" . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 33 . 5 (2020) : 1571-1584 . |
| APA | Xie, Tianfa , Cao, Ruiyuan , Yu, Ping . Rank-Based Test for Partial Functional Linear Regression Models . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2020 , 33 (5) , 1571-1584 . |
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Abstract ：
In this paper, we consider rank estimation for partial functional linear regression models based on functional principal component analysis. The proposed rank-based method is robust to outliers in the errors and highly efficient under a wide range of error distributions. The asymptotic properties of the resulting estimators are established under some regularity conditions. A simulation study conducted to investigate the finite sample performance of the proposed estimators shows that the proposed rank method performs well. Furthermore, the proposed methodology is illustrated by means of an analysis of the Berkeley growth data.
Keyword ：
Convergence rate Convergence rate Functional principal component analysis Functional principal component analysis Asymptotic normality Asymptotic normality Rank estimation Rank estimation Karhunen-Loeve expansion Karhunen-Loeve expansion
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| GB/T 7714 | Cao, Ruiyuan , Xie, Tianfa , Yu, Ping . Rank method for partial functional linear regression models [J]. | JOURNAL OF THE KOREAN STATISTICAL SOCIETY , 2020 , 50 (2) : 354-379 . |
| MLA | Cao, Ruiyuan et al. "Rank method for partial functional linear regression models" . | JOURNAL OF THE KOREAN STATISTICAL SOCIETY 50 . 2 (2020) : 354-379 . |
| APA | Cao, Ruiyuan , Xie, Tianfa , Yu, Ping . Rank method for partial functional linear regression models . | JOURNAL OF THE KOREAN STATISTICAL SOCIETY , 2020 , 50 (2) , 354-379 . |
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Abstract ：
In this paper, we investigate the adequate check of the parametric Tobit model. A Cramer-Von Mises type test statistic is constructed, and its asymptotic properties under the null and alternative hypotheses are rigorously studied. The method is effective for the adequacy check of parametric regression models with a scalar or multivariate covariate. Furthermore, it avoids the nonparametric smoothing of the regression function and the choice of the smoothing parameter. Simulation studies are conducted to compare the performance of the proposed test procedure and the existing methods in the literature. A real data set of income is analyzed by applying the proposed method.
Keyword ：
Model diagnostics Model diagnostics Estimated empirical process Estimated empirical process Parametric Tobit model Parametric Tobit model Bootstrap method Bootstrap method
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| GB/T 7714 | Sun, Zhihua , Guo, Yuanyuan , Xie, Tianfa et al. Model diagnostics of parametric Tobit model based on cumulative residuals [J]. | JOURNAL OF THE KOREAN STATISTICAL SOCIETY , 2020 , 50 (1) : 198-213 . |
| MLA | Sun, Zhihua et al. "Model diagnostics of parametric Tobit model based on cumulative residuals" . | JOURNAL OF THE KOREAN STATISTICAL SOCIETY 50 . 1 (2020) : 198-213 . |
| APA | Sun, Zhihua , Guo, Yuanyuan , Xie, Tianfa , Wang, Miaomiao . Model diagnostics of parametric Tobit model based on cumulative residuals . | JOURNAL OF THE KOREAN STATISTICAL SOCIETY , 2020 , 50 (1) , 198-213 . |
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Abstract ：
In this paper, we develop statistical inference procedures for functional quadratic quantile regression model in which the response is a scalar and the predictor is a random function defined on a compact set of R. The functional coefficients are estimated by functional principal components. The asymptotic properties of the resulting estimators are established under mild conditions. In order to test the significance of the nonlinear term in the model, we propose a rank score test procedure. The asymptotic properties of the proposed test statistic are established. The proposed method provides a highly efficient and robust alternative to the least squares method, and can be conveniently implemented using existing R software package. Finally, we examine the performance of the proposed method for finite sample sizes by Monte Carlo simulation studies and illustrate it with a real data example.
Keyword ：
Quantile regression Quantile regression Functional data Functional data Functional quadratic regression Functional quadratic regression Rank score test Rank score test
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| GB/T 7714 | Shi, Gongming , Xie, Tianfa , Zhang, Zhongzhan . Statistical inference for the functional quadratic quantile regression model [J]. | METRIKA , 2020 , 83 (8) : 937-960 . |
| MLA | Shi, Gongming et al. "Statistical inference for the functional quadratic quantile regression model" . | METRIKA 83 . 8 (2020) : 937-960 . |
| APA | Shi, Gongming , Xie, Tianfa , Zhang, Zhongzhan . Statistical inference for the functional quadratic quantile regression model . | METRIKA , 2020 , 83 (8) , 937-960 . |
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Abstract ：
We consider the statistical inference for right-censored data when censoring indicators are missing but nonignorable, and propose an adjusted imputation product-limit estimator. The proposed estimator is shown to be consistent and converges to a Gaussian process. Furthermore, we develop an empirical process-based testing method to check the MAR (missing at random) mechanism, and establish asymptotic properties for the proposed test statistic. To determine the critical value of the test, a consistent model-based bootstrap method is suggested. We conduct simulation studies to evaluate the numerical performance of the proposed method and compare it with existing methods. We also analyze a real data set from a breast cancer study for an illustration.
Keyword ：
survival function survival function quasi-likelihood quasi-likelihood MAR mechanism testing MAR mechanism testing nonignorable missing censoring indicators nonignorable missing censoring indicators
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| GB/T 7714 | Sun ZhiHua , Xie TianFa , Liang Hua . Statistical inference for right-censored data with nonignorable missing censoring indicators [J]. | SCIENCE CHINA-MATHEMATICS , 2013 , 56 (6) : 1263-1278 . |
| MLA | Sun ZhiHua et al. "Statistical inference for right-censored data with nonignorable missing censoring indicators" . | SCIENCE CHINA-MATHEMATICS 56 . 6 (2013) : 1263-1278 . |
| APA | Sun ZhiHua , Xie TianFa , Liang Hua . Statistical inference for right-censored data with nonignorable missing censoring indicators . | SCIENCE CHINA-MATHEMATICS , 2013 , 56 (6) , 1263-1278 . |
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Abstract ：
As a competitive depth, (L) over tilde (2)-depth is modified from L-2-depth. Its induced median is called (L) over tilde (2)-median. Basic properties of the median and its sample version are provided. Especially, the strong consistency of sample median is gained under weaker condition. Robustness of the median and its sample version is discussed. Besides ease of computation, it is shown that (L) over tilde (2)-median has both good large-sample and robust properties. Simulation studies are also given to compare the breakdown point of (L) over tilde (2)-median with that of other depth-induced medians.
Keyword ：
consistency consistency data depth data depth Breakdown point Breakdown point multivariate median multivariate median influence function influence function
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| GB/T 7714 | Xie, Tianfa , Li, Guoying . LARGE SAMPLE AND ROBUST PROPERTIES OF (L)over-tilde(2)-MEDIAN [J]. | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2010 , 23 (6) : 1133-1142 . |
| MLA | Xie, Tianfa et al. "LARGE SAMPLE AND ROBUST PROPERTIES OF (L)over-tilde(2)-MEDIAN" . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 23 . 6 (2010) : 1133-1142 . |
| APA | Xie, Tianfa , Li, Guoying . LARGE SAMPLE AND ROBUST PROPERTIES OF (L)over-tilde(2)-MEDIAN . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2010 , 23 (6) , 1133-1142 . |
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