Varying Dispersion Diagnostics for Inverse Gaussian Regression Models

Homogeneity of dispersion parameters is a standard assumption in inverse Gaussian regression analysis. However, this assumption is not necessarily appropriate. This paper is devoted to the test for varying dispersion in general inverse Gaussian linear regression models. Based on the modified profile likelihood (Cox & Reid, 1987), the adjusted score test for varying dispersion is developed and illustrated with Consumer- Product Sales data (Whitmore, 1986) and Gas vapour data (Weisberg, 1985). The effectiveness of orthogonality transformation and the properties of a score statistic and its adjustment are investigated through Monte Carlo simulations.     

环上矩阵的加权Moore-Penrose逆

研究相对于M和N的加权Moore-Penrose逆,得到带有对合反自同构的有单位元的结合环R上的一类可分解矩阵的加权Moore-Penrose逆存在的充分必要条件.当M和N为单位矩阵时,相对于M和N的加权Moore-Penrose逆就是大家熟悉的Moore-Penrose逆.从某种意义上说,相对于M和N的加权Moore-Penrose逆也是相对于M和N的广义Moore- Penrose逆的推广.结论一方面可特殊化到Moore-Penrose逆的情形,另一方面可得到有关A相对于M和N的广义Moore- Penrose逆的结论.     

五对角矩阵的分解及其逆元素的快速算法

提出了五对角矩阵的一种分解方法,其运算量比建立在Gaussian消元法基础上的LU方法运算量少,拓广了相应文献的结果,给出了n阶五对角矩阵的扭曲分解式,得到了五对角矩阵逆矩阵元素的快速算法,结果推广到块五对角矩阵。     

Differentiating noisy radiocommunications signals: wavelet estimation of a derivative in the presence of heteroscedastic noise

Summary.  Radio scientists require estimates of the rate of change in rain-induced signals. Unfortunately, these signals are observed in the presence of atmospheric noise, which has a variance that is dependent on temperature, pressure and other climatic variables. We develop a systematic approach to the problem, using wavelet differentiation combined with coefficient-dependent thresholding, and illustrate the considerable benefits that this provides over more conventional techniques.     

Penalized Weighted Least Squares to Small Area Estimation

In this paper, a penalized weighted least squares approach is proposed for small area estimation under the unit level model. The new method not only unifies the traditional empirical best linear unbiased prediction that does not take sampling design into account and the pseudo‐empirical best linear unbiased prediction that incorporates sampling weights but also has the desirable robustness property to model misspecification compared with existing methods. The empirical small area estimator is given, and the corresponding second‐order approximation to mean squared error estimator is derived. Numerical comparisons based on synthetic and real data sets show superior performance of the proposed method to currently available estimators in the literature.     

Empirical Likelihood in Causal Inference

This paper discusses the estimation of average treatment effects in observational causal inferences. By employing a working propensity score and two working regression models for treatment and control groups, Robins et al. (1994 Robins , J. M. , Rotnitzky , A. , Zhao , L. P. ( 1994 ). Estimation of regression coefficients when some regressors are not always observed . Journal of the American Statistical Association 89 : 846 – 866 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], 1995 Robins , J. M. , Rotnitzky , A. , Zhao , L. P. ( 1995 ). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data . Journal of the American Statistical Association 90 : 106 – 121 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) introduced the augmented inverse probability weighting (AIPW) method for estimation of average treatment effects, which extends the inverse probability weighting (IPW) method of Horvitz and Thompson (1952 Horvitz , D. G. , Thompson , D. J. ( 1952 ). A generalization of sampling without replacement from a finite universe . Journal of the American Statistical Association 47 : 663 – 685 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]); the AIPW estimators are locally efficient and doubly robust. In this paper, we study a hybrid of the empirical likelihood method and the method of moments by employing three estimating functions, which can generate estimators for average treatment effects that are locally efficient and doubly robust. The proposed estimators of average treatment effects are efficient for the given choice of three estimating functions when the working propensity score is correctly specified, and thus are more efficient than the AIPW estimators. In addition, we consider a regression method for estimation of the average treatment effects when working regression models for both the treatment and control groups are correctly specified; the asymptotic variance of the resulting estimator is no greater than the semiparametric variance bound characterized by the theory of Robins et al. (1994 Robins , J. M. , Rotnitzky , A. , Zhao , L. P. ( 1994 ). Estimation of regression coefficients when some regressors are not always observed . Journal of the American Statistical Association 89 : 846 – 866 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], 1995 Robins , J. M. , Rotnitzky , A. , Zhao , L. P. ( 1995 ). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data . Journal of the American Statistical Association 90 : 106 – 121 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Finally, we present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification.     

Stratified proportional subdistribution hazards model with covariate-adjusted censoring weight for case-cohort studies

The case-cohort study design is widely used to reduce cost when collecting expensive covariates in large cohort studies with survival or competing risks outcomes. A case-cohort study dataset consists of two parts: (a) a random sample and (b) all cases or failures from a specific cause of interest. Clinicians often assess covariate effects on competing risks outcomes. The proportional subdistribution hazards model directly evaluates the effect of a covariate on the cumulative incidence function under the non-covariate-dependent censoring assumption for the full cohort study. However, the non-covariate-dependent censoring assumption is often violated in many biomedical studies. In this article, we propose a proportional subdistribution hazards model for case-cohort studies with stratified data with covariate-adjusted censoring weight. We further propose an efficient estimator when extra information from the other causes is available under case-cohort studies. The proposed estimators are shown to be consistent and asymptotically normal. Simulation studies show (a) the proposed estimator is unbiased when the censoring distribution depends on covariates and (b) the proposed efficient estimator gains estimation efficiency when using extra information from the other causes. We analyze a bone marrow transplant dataset and a coronary heart disease dataset using the proposed method.     

Tests for exponentiality

The idea of measuring the departure of data bu a plot of obeserved observations against their expectation has been expeetations has been exploited in this paper to develop tests for exponentiality the tests are for parameter two parameter exponential distribution with complete sample and one parameter exponential distribution with complete sample and one large sample distributions of the test statistics critical points have been computed for different levels of significance and applications of these have been computed for differents levels of significance and applications of these tests have been discussed in case of three data sets.     

Erratum

For a random variable obeying the inverse Gaussian distribu-tion and its reciprocal, the uniformly minimum variance unbiased (UMVU) estimators of each mode are obtained. The UMVU estimators

of the left and right limits of a certain interval which contains an inverse Gaussian variate with an arbitrary given probability are also proposed.     

Errors of misclassification associated with the inverse gaussion distribution

Errors of misclassification and their probabilities are studied for classification problems associated with univariate inverse Gaussian distributions. The effects of applying the linear discriminant function (LDF), based on normality, to inverse Gaussian populations are assessed by comparing probabilities (optimum and conditional) based on the LDF with those based on the likelihood ratio rule (LR) for the inverse Gaussian, Both theoretical and empirical results are presented