Local Empirical Likelihood Diagnosis of Varying Coefficient Density-Ratio Models Based on Case-Control Data

In this paper, a varying-coefficient density-ratio model for case-control studies is developed. We investigate the local empirical likelihood diagnosis of varying coefficient density-ratio model for case-control data. The local empirical log-likelihood ratios for the nonparametric coefficient functions are introduced. First, the estimation equations based on empirical likelihood method are established. Then, a few of diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation studies.


Introduction
Varying coefficient models are often used as extensions of classical linear models (e.g.Shumway [1]).Their appeals are that the modeling bias can be significantly reduced and the "curse of dimensionality" can also be avoided.These models have gained considerable attention due to their various applications in many areas, such as biomedical study, finance, econometrics, and environmental study.The estimation for the coefficient functions has been extensively discussed in the literatures, including the smoothing spline method (see Hastie and Tibshirani [2]), the locally weighted polynomial method (see Hoover et al. [3]), the two-step estimation procedure (see Fan and Zhang [4]), and the basis function approximations (see Huang et al. [5]).
In this paper, we consider the following general two-sample varying-coefficient density-ratio model where {} ψ ⋅ is a nonnegative known function that makes ( ) , f w z to be a density function, which includes the exponential-tilt model as a special case with ( ) ( ) In parametric situation, Thomas [6] and Lustbader et al. [7] considered a general relative risk model, a mixture model, ( ) where δ is a scalar parameter that describes the general shape of the relative risk function.It includes additive relative risk model ( ) and log-linear relative model ( ) Various density-ratio models for some conventional density functions were discussed in Kay and Little [8].It has been shown recently that the density-ratio model provides a good fit to the observed data in some medical applications (Qin and Zhang [9]; Qin et al. [10]; Zhang [11]), genetic quantitative trait loci analysis (Zou et al. [12]), and clinical trials with skewed outcomes (White and Thompson [13]).Liu, Jiang and Zhou [14] considered estimation and inference for the two-sample varying-coefficient density-ratio model (1) by constructing the local empirical likelihood function.The EL approach is appealing for analyzing the varying-coefficient density-ratio model because the two density functions in (1) can be modeled nonparametrically.This nonparametric method of inference has sampling properties similar to the bootstrap.Another advantage of the EL approach is that it takes auxiliary information, such as the density-ratio in (1), into account to improve estimation.
The empirical likelihood method origins from Thomas & Grunkemeier [15].Owen [16] first proposed the definition of empirical likelihood and expounded the system info of empirical likelihood.Zhu and Ibrahim [17] utilized this method for statistical diagnostic.Liugen Xue and Lixing Zhu [18] summarized the application of this method.
Over the last several decades, the diagnosis and influence analysis of linear regression model has been fully developed (R.D. Cook and S. Weisberg [19], Bocheng Wei, Gobin Lu & Jianqing Shi [20]).Regarding the varying coefficient model, especially for the B-spline estimation of parameter, diagnosis and influence analysis have some results (Z.Cai, J. Fan, R. Li [21], J. Fan, W. Zhang [22]).So far the statistical diagnostics of varyingcoefficient density-ratio models with case-control data based on local empirical likelihood method has not yet seen in the literature.This paper attempts to study it.
The remainder of the article is organized as follows.Local empirical likelihood and estimation equation are presented in Section 2. The main results are given in Section 3.An example is given to illustrate our results in Section 4.

Local Empirical Likelihood and Estimation Equation
be a sequence of independent and identically distributed random vectors from the control group, each with density ( ) g x , and  be a sequence of independent and identically distributed random vectors from the case group, each with density ( ) n and 2 n are the number of subjects in the control group and case group, respectively.Let 1 2 n n n = + , and { } { } 1), the empirical likelihood function derived according to Prentice and Pyke [23] is: where ( ) G X is the distribution func- tion corresponding to ( ) g x .However, ( ) can not be used directly to obtain estimates for ( ) α ⋅ and ( ) β ⋅ because ( ) α ⋅ and ( ) β ⋅ are infinite-dimensional parameters.Thus, instead of (2), we consider the loca- lized conditional empirical likelihood below.
Assume that all components of ( ) α ⋅ and ( ) β ⋅ are smooth so that they admit Taylors series expansions, i.e., for each given 0 w and for w around 0 w , X * for fixed 0 w .Then, the local log empirical likelihood (LEL) represents the size of the local neighborhood.The kernel weight is used to give smoother weight to data with w near 0 w .The last constraint is the auxiliary information for the EL estimation.By the method of Lagrange mul- tipliers, similar to that used in Owen (2001), we obtain where λ is determined by the constraint equation 1 Motivated by Zhu and Ibrahim (2008), we regard λ and ξ as independent variables and define , log 1 1 Obviously, the maximum empirical likelihood estimates ξ and λ are the solutions of following equations:

Local Influence Analysis of Model
We consider the local influence method for a case-weight perturbation n R ω ∈ , for which the empirical log-likelihood function . In this case, 0 ω ω = , defined to be an 1 n × vector with all elements equal to 1, represents no perturbation to the empirical likelihood, because Thus, the empirical likelihood displacement is defined as where ( ) We consider two local influence measures based on the normal curvature ( ) as follows.Let .

Numerical Study
We generate   We draw 1000 data sets with sample size In order to checkout the validity of our proposed methodology, we change the value of the first, 125th, 374th, 789th and 999th data.For every case, it is easy to obtain ( ) ( ) It can be seen from the result of i e C that the first, 125th, 374th, 789th and 999th data are strong influence points.Indeed, our results are illustrated.

Discussion
In this paper, we considered the statistical diagnosis for varying-coefficient density-ratio model based on local empirical likelihood.Through simulation study, we illustrate that our proposed method can work fairly well.
ω is the maximum empirical likelihood estimator of ξ based on ( ) where h is a direction in n R .Thus, the normal curvature of the in-

C
with j -th component 1 and 0 otherwise.The 1 v represents the most influential perturbation to the empirical likelihood function, whereas the j -th observation with a large j e can be regarded as influential.As the discuss ofZhu et al. (2008), for varying-coefficient density-ratio model, we can deduce that

Figure 1 .
Figure 1.The influence value of i e C .
− .For ξ and λ , using the sam- ples, we evaluated their maximum empirical likelihood estimators.Consequently, it is easy to calculate the value of11 12