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Dive into the research topics where Qihua Wang is active.

Publication


Featured researches published by Qihua Wang.


Journal of the American Statistical Association | 2003

Semiparametric Regression Analysis With Missing Response at Random

Qihua Wang; Oliver Linton; Wolfgang Karl Härdle

We develop inference tools in a semiparametric partially linear regression model with missing response data. A class of estimators is defined that includes as special cases a semiparametric regression imputation estimator, a marginal average estimator, and a (marginal) propensity score weighted estimator. We show that any of our class of estimators is asymptotically normal. The three special estimators have the same asymptotic variance. They achieve the semiparametric efficiency bound in the homoscedastic Gaussian case. We show that the jackknife method can be used to consistently estimate the asymptotic variance. Our model and estimators are defined with a view to avoid the curse of dimensionality, which severely limits the applicability of existing methods. The empirical likelihood method is developed. It is shown that when missing responses are imputed using the semiparametric regression method the empirical log-likelihood is asymptotically a scaled chi-squared variable. An adjusted empirical log-likelihood ratio, which is asymptotically standard chisquared, is obtained. Also, a bootstrap empirical log-likelihood ratio is derived and its distribution is used to approximate that of the imputed empirical log-likelihood ratio. A simulation study is conducted to compare the adjusted and bootstrap empirical likelihood with the normal approximation-based method in terms of coverage accuracies and average lengths of confidence intervals. Based on biases and standard errors, a comparison is also made by simulation between the proposed estimators and the related estimators.


Scandinavian Journal of Statistics | 2002

Empirical Likelihood-based Inference in Linear Models with Missing Data

Qihua Wang; J. N. K. Rao

The missing response problem in linear regression is studied. An adjusted empirical likelihood approach to inference on the mean of the response variable is developed. A non‐parametric version of Wilkss theorem for the adjusted empirical likelihood is proved, and the corresponding empirical likelihood confidence interval for the mean is constructed. With auxiliary information, an empirical likelihood‐based estimator with asymptotic normality is defined and an adjusted empirical log‐likelihood function with asymptotic χ2 is derived. A simulation study is conducted to compare the adjusted empirical likelihood methods and the normal approximation methods in terms of coverage accuracies and average lengths of the confidence intervals. Based on biases and standard errors, a comparison is also made between the empirical likelihood‐based estimator and related estimators by simulation. Our simulation indicates that the adjusted empirical likelihood methods perform competitively and the use of auxiliary information provides improved inferences.


Canadian Journal of Statistics-revue Canadienne De Statistique | 2001

Empirical likelihood for linear regression models under imputation for missing responses

Qihua Wang; J. N. K. Rao

The authors study the empirical likelihood method for linear regression models. They show that when missing responses are imputed using least squares predictors, the empirical log-likelihood ratio is asymptotically a weighted sum of chi-square variables with unknown weights. They obtain an adjusted empirical log-likelihood ratio which is asymptotically standard chi-square and hence can be used to construct confidence regions. They also obtain a bootstrap empirical log-likelihood ratio and use its distribution to approximate that of the empirical log-likelihood ratio. A simulation study indicates that the proposed methods are comparable in terms of coverage probabilities and average lengths of confidence intervals, and perform better than a normal approximation based method.


Annals of the Institute of Statistical Mathematics | 2001

Empirical Likelihood for a Class of Functionals of Survival Distribution with Censored Data

Qihua Wang; Bing-Yi Jing

The empirical likelihood was introduced by Owen, although its idea originated from survival analysis in the context of estimating the survival probabilities given by Thomas and Grunkemeier. In this paper, we investigate how to apply the empirical likelihood method to a class of functionals of survival function in the presence of censoring. We define an adjusted empirical likelihood and show that it follows a chi-square distribution. Some simulation studies are presented to compare the empirical likelihood method with the Studentized-t method. These results indicate that the empirical likelihood method works better than or equally to the Studentized-t method, depending on the situations.


Journal of Multivariate Analysis | 2009

Model checking for partially linear models with missing responses at random

Zhihua Sun; Qihua Wang; Pengjie Dai

In this paper, we investigate the model checking problem for a partial linear model while some responses are missing at random. By imputation and marginal inverse probability weighted methods, two completed data sets are constructed. Based on the two completed data sets, we build two empirical process-based tests for examining the adequacy of partial linearity of the model. The asymptotic distributions of the test statistics under the null hypothesis and local alternative hypotheses are obtained respectively. A re-sampling approach is applied to obtain the approximation to the null distributions of the test statistics. Simulation results show that the proposed tests work well and both proposed methods have better finite sample properties compared with the complete case (CC) analysis which discards all the subjects with missing data.


Science China-mathematics | 1997

Asymptotic properties for the semiparametric regression model with randomly censored data

Qihua Wang; Zhongguo Zheng

AbstractSuppose that the patients’ survival times.Y, are random variables following the semiparametric regression modelY = Xβ +g(T) + ε, where (X,T) is a radom vector taking values inR×[0,1],βis an unknown parameter,g (*) is an unknown smooth regression function andE is the random error with zero mean and variance σ2. It is assumed that (X,T) is independent of E. The estimators


Journal of Multivariate Analysis | 2011

Statistical inference in partially-varying-coefficient single-index model

Qihua Wang; Liugen Xue


Journal of Multivariate Analysis | 2012

Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data

Peng Lai; Qihua Wang; Heng Lian

\hat \beta _n


Bernoulli | 2012

Empirical likelihood for single-index varying-coefficient models

Liugen Xue; Qihua Wang


Journal of the American Statistical Association | 2011

Fusion-Refinement Procedure for Dimension Reduction With Missing Response at Random

Xiaobo Ding; Qihua Wang

andgn(*) of P andg(*) are defined, respectively, when the observations are randomly censored on the right and the censoring distribution is unknown. Moreover, it is shown that

Collaboration


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Peng Lai

Nanjing University of Information Science and Technology

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Bing-Yi Jing

Hong Kong University of Science and Technology

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Tao Zhang

University of Science and Technology

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Wolfgang Karl Härdle

Humboldt University of Berlin

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Xiaolin Chen

China University of Petroleum

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Xuan Wang

Chinese Academy of Sciences

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Yi Liu

Chinese Academy of Sciences

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Zhihua Sun

Chinese Academy of Sciences

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Lili Yao

Chinese Academy of Sciences

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