Empirical likelihood for a partially linear model with covariate data missing at random

Yang, Yiping; Xue, Liugen; Cheng, Weihu

doi:10.1016/j.jspi.2009.05.046

Cited by 26 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study tries to find the relationship between virologic and immunologic responses in AIDS clinical trials. The data set has been studied by Wu (2001, 2002) and Yang, Xue and Cheng (2009). Generally speaking, the virologic response RNA (measured by viral load) and immunologic response (measured by CD cell counts) have a negative correlation during the clinical trials.…”

Section: Figures 3 Is About Herementioning

confidence: 99%

Testing Heteroscedasticity for Regression Models Based on Projections

Tan¹,

Jiang²,

Guo³

et al. 2021

STAT SINICA

View full text Add to dashboard Cite

In this paper we propose a new test of heteroscedasticity for parametric regression models and partial linear regression models in high dimensional settings. When the dimension of covariates is large, existing tests of heteroscedasticity perform badly due to the "curse of dimensionality". To attack this problem, we construct a test of heteroscedasticity by using a projection-based empirical process. We study the asymptotic properties of the test statistic under the null hypothesis and alternative hypotheses. It is shown that the test can detect local alternatives departure from the null hypothesis at the fastest possible rate in hypothesis testing. As the limiting null distribution of the test statistic is not distribution free, we propose a residual-based bootstrap. The validity of the bootstrap approximations is investigated. We present some simulation results to show the finite sample performances of the test. Two real data analyses are conducted for illustration.

show abstract

Section: Figures 3 Is About Herementioning

confidence: 99%

Testing Heteroscedasticity for Regression Models Based on Projections

Tan¹,

Jiang²,

Guo³

et al. 2021

STAT SINICA

View full text Add to dashboard Cite

show abstract

“…Most of material on this section comes from Qin et al (2009). Related works can be found in ), Xue (2009a, Yang et al (2009).…”

Section: Empirical Likelihood In Missing Data Problemsmentioning

confidence: 99%

Empirical likelihood in some nonparametric and semiparametric models

Xue

Zhu

2012

Statistics and Its Interface

Self Cite

View full text Add to dashboard Cite

In this very selective overview, we summarise the recent developments by our own and other, on the empirical likelihood in some nonparametric and semiparametric regression models. The models include the partially linear model, the single-index model, the partially linear singleindex model, the varying coefficient model, and so on. The focus of this overview is to expatiate the adjustment and "bias correction" methodologies when Wilks' phenomenon does not hold. The adjustment or bias correction can make the limiting distributions tractable such that they can be directly used to construct the confidence regions of parameters of interest without the assistance of Monte Carlo approximation.

show abstract

“…Xue and Xue [18] defined the empirical likelihood for partial linear model with missing response at random and made statistical inference for the regression coefficients, the baseline function and the response mean. In the presence of missing covariates, Yang et al [19] further investigated the empirical likelihood ratios for the regression coefficients and the baseline function of the partial linear regression model.…”

Section: Introductionmentioning

confidence: 99%

Empirical likelihood for single index model with missing covariates at random

et al. 2014

Self Cite

View full text Add to dashboard Cite

In this paper, we investigate the empirical-likelihood-based inference for the construction of confidence intervals and regions of the parameters of interest in single index models with missing covariates at random. An augmented inverse probability weighted-type empirical likelihood ratio for the parameters of interest is defined such that this ratio is asymptotically standard chi-squared. Our approach is to directly calibrate the empirical log-likelihood ratio, and does not need multiplication by an adjustment factor for the original ratio. Our bias-corrected empirical likelihood is self-scale invariant and no plug-in estimator for the limiting variance is needed. Some simulation studies are carried out to assess the performance of our proposed method.

show abstract

Empirical likelihood for a partially linear model with covariate data missing at random

Cited by 26 publications

References 19 publications

Testing Heteroscedasticity for Regression Models Based on Projections

Testing Heteroscedasticity for Regression Models Based on Projections

Empirical likelihood in some nonparametric and semiparametric models

Empirical likelihood for single index model with missing covariates at random

Contact Info

Product

Resources

About