Elizabeth Tipton 《Research Synthesis Methods》2013,4(2):169-187

Dependent effect size estimates are a common problem in meta‐analysis. Recently, a robust variance estimation method was introduced that can be used whenever effect sizes in a meta‐analysis are not independent. This problem arises, for example, when effect sizes are nested or when multiple measures are collected on the same individuals. In this paper, we investigate the robustness of this method in small samples when the effect size of interest is the risk difference, log risk ratio, or log odds ratio. This simulation study examines the accuracy of 95% confidence intervals constructed using the robust variance estimator across a large variety of parameter values. We report results for both estimations of the mean effect (intercept) and of a slope. The results indicate that the robust variance estimator performs well even when the number of studies is as small as 10, although coverage is generally less than nominal in the slope estimation case. Throughout, an example based on a meta‐analysis of cognitive behavior therapy is used for motivation. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

    

Thordis L. Thorarinsdottir 《Stat》2013,2(1):150-158

We propose the use of the probability integral transform (PIT) for model validation in point process models. The simple PIT diagnostic tools assess the calibration of the model and can detect inconsistencies in both the intensity and the interaction structure. For the Poisson model, the PIT diagnostics can be calculated explicitly. Generally, the calibration may be assessed empirically based on random draws from the model, and the method applies to processes of any dimension. Copyright © 2013 John Wiley & Sons Ltd  相似文献   

    

Kwun Chuen Gary Chan 《Stat》2013,2(1):143-149

A multiply robust estimator for a missing response problem is recently proposed that is more robust than doubly robust estimators proposed in the literature. Its formulation is based on empirical likelihood, which solves an implicit Lagrangian equation and often encounters computational problems such as multiple roots or nonconvergence. An alternative multiply robust estimator is proposed, which is computed by least squares and can be implemented easily in practice. We show that this multiply robust estimator is locally semiparametric efficient.Copyright © 2013 John Wiley & Sons Ltd  相似文献   

    

Michael E. Griswold  Bruce J. Swihart  Brian S. Caffo  Scott L. Zeger 《Stat》2013,2(1):129-142

Clustered data analysis is characterized by the need to describe both systematic variation in a mean model and cluster‐dependent random variation in an association model. Marginalized multilevel models embrace the robustness and interpretations of a marginal mean model, while retaining the likelihood inference capabilities and flexible dependence structures of a conditional association model. Although there has been increasing recognition of the attractiveness of marginalized multilevel models, there has been a gap in their practical application arising from a lack of readily available estimation procedures. We extend the marginalized multilevel model to allow for nonlinear functions in both the mean and association aspects. We then formulate marginal models through conditional specifications to facilitate estimation with mixed model computational solutions already in place. We illustrate the MMM and approximate MMM approaches on a cerebrovascular deficiency crossover trial using SAS and an epidemiological study on race and visual impairment using R. Datasets, SAS and R code are included as supplemental materials. Copyright © 2013 John Wiley & Sons Ltd  相似文献   

    

Dmitriy Karpman  Marco A.R. Ferreira  Christopher K. Wikle 《Stat》2013,2(1):1-8

We propose a point process model with multiplicative risk for the study of tornado reports in the United States. In particular, we implement a rigorous statistical procedure to evaluate whether tornado report counts are significantly related to topographic variability. The model we propose also includes flexible nonparametric components for spatial and seasonality effects. We apply the proposed model and methodology to the analysis of tornado report data from 1953 to 2010 in the United States. Our analysis shows that in addition to the spatial and seasonal effects, the topographic variability is an important component of tornado risk. Copyright © 2013 John Wiley & Sons Ltd  相似文献   

    

Matthias Kohl 《Statistics》2013,47(4):473-488

Bednarski and Müller [Optimal bounded influence regression and scale M-estimators in the context of experimental design, Statistics 35 (2001), pp. 349–369] introduced a class of bounded influence M estimates for the simultaneous estimation of regression and scale in the linear model with normal errors by solving the corresponding normal location and scale problem at each design point. This limits the proposal to regressor distributions with finite support. Based on their approach, we propose a slightly extended class of M estimates that is not restricted to finite support and is numerically easier to handle. Moreover, we employ the even more general class of asymptotically linear (AL) estimators which, in addition, is not restricted to normal errors. The superiority of AL estimates is demonstrated by numerical comparisons of the maximum asymptotic mean-squared error over infinitesimal contamination neighbourhoods.  相似文献   

    

Jiang Du  Zhongzhan Zhang  Tianfa Xie 《Statistics》2013,47(6):1417-1432

M-estimation is a widely used technique for robust statistical inference. In this paper, we study model selection and model averaging for M-estimation to simultaneously improve the coverage probability of confidence intervals of the parameters of interest and reduce the impact of heavy-tailed errors or outliers in the response. Under general conditions, we develop robust versions of the focused information criterion and a frequentist model average estimator for M-estimation, and we examine their theoretical properties. In addition, we carry out extensive simulation studies as well as two real examples to assess the performance of our new procedure, and find that the proposed method produces satisfactory results.  相似文献   

    

Edouard Fournier  Stéphane Grihon  Thierry Klein 《Statistics》2013,47(5):1168-1186

In the big data era, it is often needed to resolve the problem of parsimonious data representation. In this paper, the data under study are curves and the sparse representation is based on a semiparametric model. Indeed, we propose an original registration model for noisy curves. The model is built transforming an unknown function by plane similarities. We develop a statistical method that allows to estimate the parameters characterizing the plane similarities. The properties of the statistical procedure are studied. We show the convergence and the asymptotic normality of the estimators. Numerical simulations and a real-life aeronautic example illustrate and demonstrate the strength of our methodology.  相似文献   

    

João Lita da Silva 《Statistics》2013,47(3):657-667

The strong consistency of the least-squares estimates in regression models is obtained when the errors are i.i.d. with absolute moment of order r, 0<r? 2. The assumptions presented for the random error sequence will permit us to obtain improvements of the conditions on the regressors in order to obtain the strong consistency of the least-squares estimates in linear and nonlinear regression models.  相似文献   

    

S. Y. Hwang  I. V. Basawa  M. S. Choi  S. D. Lee 《Statistics》2013,47(3):487-507

Well-known estimation methods such as conditional least squares, quasilikelihood and maximum likelihood (ML) can be unified via a single framework of martingale estimating functions (MEFs). Asymptotic distributions of estimates for ergodic processes use constant norm (e.g. square root of the sample size) for asymptotic normality. For certain non-ergodic-type applications, however, such as explosive autoregression and super-critical branching processes, one needs a random norm in order to get normal limit distributions. In this paper, we are concerned with non-ergodic processes and investigate limit distributions for a broad class of MEFs. Asymptotic optimality (within a certain class of non-ergodic MEFs) of the ML estimate is deduced via establishing a convolution theorem using a random norm. Applications to non-ergodic autoregressive processes, generalized autoregressive conditional heteroscedastic-type processes, and super-critical branching processes are discussed. Asymptotic optimality in terms of the maximum random limiting power regarding large sample tests is briefly discussed.  相似文献