Bing-yi Jing

Publication List Details

Period

1992 - 2007

Number

32

Co-Authors

Saddlepoint Approximations to the Trimmed Mean (2007)

R. Helmers, G. Qin, W. Zhou, Roelof Helmers, Bing-yi Jing, ...

CWI is a founding member of ERCIM, the European Research Consortium for Informatics and Mathematics. CWI's research has a theme-oriented structure and is grouped into four clusters. Listed below...

Saddlepoint approximation for Student's t-statistic with no moment conditions (2005)

Jing, Bing-Yi, Shao, Qi-Man, Zhou, Wang

A saddlepoint approximation of the Student's t-statistic was derived by Daniels and Young [Biometrika 78 (1991) 169-179] under the very stringent exponential moment condition that requires that the...

Saddlepoint approximation for Student’s t-statistic with no moment conditions (2004)

Jing, Bing-Yi, Shao, Qi-Man, Zhou, Wang

A saddlepoint approximation of the Student’s t-statistic was derived by Daniels and Young [Biometrika 78 (1991) 169–179] under the very stringent exponential moment condition that requires that...

Saddlepoint approximations to the trimmed mean (2004)

Helmers, Roelof, Jing, Bing-Yi, Qin, Gengsheng, Zhou, Wang

Saddlepoint approximations for the trimmed mean and the studentized trimmed mean are established. Some numerical evidence on the quality of our saddlepoint approximations is also included.

Self-normalized Cramér-type large deviations for independent random variables (2003)

Jing, Bing-Yi, Shao, Qi-Man, Wang, Qiying

Let $X_1, X_2, \ldots $ be independent random variables with zero means and finite variances. It is well known that a finite exponential moment assumption is necessary for a Cramér-type large...

Self-normalized Cramér-type large deviations for independent random variables (2003)

Wang, Qiying, Jing, Bing-Yi, Shao, Qi-Man

Let X1, X2, ... be independent random variables with zero means and finite variances. It is well known that a finite exponential moment assumption is necessary for a Cramér-type large deviation...

Edgeworth expansion for U-statistics under minimal conditions (2003)

Jing, Bing-Yi, Wang, Qiying

Berry-Esseen bounds for U-statistics under the optimal moment conditions were derived by Koroljuk and Borovskich and Friedrich. Under the same optimal moment assumptions with an additional nonlattice...

Saddlepoint Approximations to the Trimmed Mean (2002)

R. Helmers, G. Qin, W. Zhou, Roelof Helmers, Bing-yi Jing, ...

CWI is a founding member of ERCIM, the European Research Consortium for Informatics and Mathematics. CWI's research has a theme-oriented structure and is grouped into four clusters. Listed below...

Strong consistency of estimators for heteroscedastic partly linear regression model under dependent samples (2002)

Han-Ying Liang, Bing-Yi Jing

In this paper we are concerned with the heteroscedastic regression model yi=xiβ+g(ti)+σiei, 1≤i≤n under correlated errors ei, where it is assumed that σi2=f(ui), the design points (xi,ti,ui)...

The Berry-Esséen bound for Studentized statistics (2000)

Jing, Bing-Yi, Wang, Qiying, Zhao, Lincheng

We derive Berry–Esséen bounds for a class of Studentized statistics. The results are applied to Studentized $U$-statistics, Studentized $L$-statistics and Studentized functions of the sample mean...

The Berry-Esséen bound for studentized statistics (2000)

Jing, Bing-Yi, Wang, Qiying, Zhao, Lincheng

We derive Berry-Esséen bound for a class of studentized statistics. The results are applied to Studentized U-statistics, Studentized L-statistics and Studentized functions of the sample mean to...

An Exponential Nonuniform Berry-Esseen Bound for Self-Normalized Sums (1999)

Jing, Bing-Yi, Wang, Qiying

In this paper we shall derive exponential nonuniformBerry–Esseen bounds in the central limit theorem for self-normalized sums.We show that the size of the error can be reduced considerably by...

An exponential nonuniform Berry-Esseen bound for self-normalized sums (1999)

Wang, Qiying, Jing, Bing-Yi

In this paper we shall derive exponential nonuniform Berry-Esseen bounds in the central limit theorem for self-normalized sums. We show that the size of the error can be reduced considerably by...

Exponential empirical likelihood is not Bartlett correctable (1996)

Jing, Bing-Yi, Wood, Andrew T. A.

In a recent paper, DiCiccio, Hall and Romano established that Owen's empirical likelihood is Bartlett correctable. This is an intriguing and perhaps surprising result and is the only nonparametric...

Exponential empirical likelihood is not Bartlett correctable (1996)

Jing, Bing-Yi, Wood, Andrew T. A.

In a recent paper, DiCiccio, Hall and Romano established that Owen's empirical likelihood is Bartlett correctable. This is an intriguing and perhaps surprising result and is the only nonparametric...

On blocking rules for the bootstrap with dependent data (1995)

HALL, PETER, HOROWITZ, JOEL L., JING, BING-YI

We address the issue of optimal block choice in applications of the block bootstrap to dependent data. It is shown that optimal block size depends significantly on context, being equal to n1/3,...

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

Qi-Hua Wang, Bing-Yi Jing

Emprical likelihood, censoring, Kaplan-Meier estimate, survival probability, mean lifetime, Studentized-t ,

Empirical likelihood for partial linear models

Qi-Hua Wang, Bing-Yi Jing

Empirical likelihood, partial linear model, Wilks' theorem,

Edgeworth expansion in censored linear regression model

Gensheng Qin, Bing-Yi Jing

Censored data, regression, martingale, asymptoticU-statistic, Edgeworth expansion,

Adjusted empirical likelihood method for quantiles

Wang Zhou, Bing-Yi Jing

Confidence interval, empirical likelihood, quantile, Edgeworth expansion,

Censored Partial Linear Models and Empirical Likelihood

Qin, Gengsheng, Jing, Bing-Yi

Consider the partial linear model Yi=X[tau]i[beta]+g(Ti)+[var epsilon]i, i=1, ..., n, where [beta] is a p-1 unknown parameter vector, g is an unknown function, Xi's are p-1 observable...

Empirical likelihood for non-degenerate U-statistics

Jing, Bing-Yi, Yuan, Junqing, Zhou, Wang

Standard empirical likelihood for U-statistics is too computationally expensive. To overcome this computational difficulty, we reformulate the non-degenerate U-statistics as a sample mean of some...

Some results about the NBUC class of life distributions

Li, Xiaohu, Li, Zehui, Jing, Bing-Yi

NBUC class life distributions are dealt with in this paper. Firstly, a lower bound of the reliability function based upon mean and variance (both assumed finite) is presented. It is shown that the...

Empirical likelihood for partial linear models with fixed designs

Wang, Qi-Hua, Jing, Bing-Yi

The empirical likelihood method of Owen [Owen, A., 1988. Empirical likelihood ratio confidence intervals for single functional. Biometrika 75, 237-249], is extended to partial linear models with...

Global tilting method

Jing, Bing-Yi

The nonparametric tilting method for constructing confidence intervals was first introduced by Efron (1982). It usually involves tilting the empirical distribution first, and then calculating the...

Two-sample empirical likelihood method

Jing, Bing-Yi

The empirical likelihood method is applied to the two-sample problem and is shown to be Bartlett correctable.

Asymptotic properties for estimates of nonparametric regression models based on negatively associated sequences

Liang, Han-Ying, Jing, Bing-Yi

Consider the nonparametric regression model Yni=g(xni)+[epsilon]ni for i=1,...,n, where g is unknown, xni are fixed design points, and [epsilon]ni are negatively associated random errors....

Weighted bootstrap for U-statistics

Wang, Qiying, Jing, Bing-Yi

In this paper we investigate the weighted bootstrap for U-statistics and its properties. Under very general choices of random weights and certain regularity conditions, we show that the weighted...