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11.
M. P. Wand 《Australian & New Zealand Journal of Statistics》2009,51(1):9-41
Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology. 相似文献
12.
For capture–recapture models when covariates are subject to measurement errors and missing data, a set of estimating equations is constructed to estimate population size and relevant parameters. These estimating equations can be solved by an algorithm similar to the EM algorithm. The proposed method is also applicable to the situation when covariates with no measurement errors have missing data. Simulation studies are used to assess the performance of the proposed estimator. The estimator is also applied to a capture–recapture experiment on the bird species Prinia flaviventris in Hong Kong. The Canadian Journal of Statistics 37: 645–658; 2009 © 2009 Statistical Society of Canada 相似文献
13.
近几十年以来,国际上在对\"风险的处理和效益的优化\"这两个现代金融学的中心议题的分析和处理过程中,金融时间序列的计量学模型及其相应的分析越来越起到非常重要的作用.对于线性时间序列模型如AR(p),MA(q),ARMA(p,q)等,已经为我们所熟知.具体到模型的参数估计在数据没有缺失时,也有很多经典的办法,如最小二乘法、极大似然法等.但是当数据在中间有缺失时,上述方法将无能为力.本文将详细讨论在数据有缺失时的ARMA(1,1)模型,即Zt=αZt-1+εt-βεt-1的参数的估计方法. 相似文献
14.
Sanjoy K. Sinha 《Revue canadienne de statistique》2008,36(2):277-299
The EM algorithm is often used for finding the maximum likelihood estimates in generalized linear models with incomplete data. In this article, the author presents a robust method in the framework of the maximum likelihood estimation for fitting generalized linear models when nonignorable covariates are missing. His robust approach is useful for downweighting any influential observations when estimating the model parameters. To avoid computational problems involving irreducibly high‐dimensional integrals, he adopts a Metropolis‐Hastings algorithm based on a Markov chain sampling method. He carries out simulations to investigate the behaviour of the robust estimates in the presence of outliers and missing covariates; furthermore, he compares these estimates to the classical maximum likelihood estimates. Finally, he illustrates his approach using data on the occurrence of delirium in patients operated on for abdominal aortic aneurysm. 相似文献
15.
Ola Elerian Siddhartha Chib Neil Shephard 《Econometrica : journal of the Econometric Society》2001,69(4):959-993
This paper is concerned with the Bayesian estimation of nonlinear stochastic differential equations when observations are discretely sampled. The estimation framework relies on the introduction of latent auxiliary data to complete the missing diffusion between each pair of measurements. Tuned Markov chain Monte Carlo (MCMC) methods based on the Metropolis‐Hastings algorithm, in conjunction with the Euler‐Maruyama discretization scheme, are used to sample the posterior distribution of the latent data and the model parameters. Techniques for computing the likelihood function, the marginal likelihood, and diagnostic measures (all based on the MCMC output) are developed. Examples using simulated and real data are presented and discussed in detail. 相似文献
16.
Mortaza Jamshidian Ke‐Hai Yuan 《Wiley Interdisciplinary Reviews: Computational Statistics》2014,6(1):56-73
This paper reviews various methods of identifying missing data mechanisms. The three well‐known mechanisms of missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) are considered. A number of tests deem rejection of homogeneity of means and/or covariances (HMC) among observed data patterns as a means to reject MCAR. Utility of these tests as well as their shortcomings are discussed. In particular, examples of MAR and MNAR data with homogeneous means and covariances between their observed data patterns are provided for which tests of HMC fail to reject MCAR. More generally, tests of homogeneity of parameter estimates between various subsets of data are reviewed and their utility as tests of MCAR and MAR (in special cases) is pointed out. Since many tests of MCAR assume multinormality, methods to assess this assumption in the context of incomplete data are reviewed. Tests of homogeneity of distributions among observed data patterns for MCAR are also considered. A new nonparametric test of this type is proposed on the basis of pairwise comparison of marginal distributions. Finally, methods of examining missing data mechanism based on sensitivity analysis including methods that model missing data mechanism based on logistic, probit, and latent variable regression models, as well as methods that do not require modeling of missing data mechanism are reviewed. The paper concludes with some practical comments about the validity and utility of tests of missing data mechanism. WIREs Comput Stat 2014, 6:56–73. doi: 10.1002/wics.1287 This article is categorized under:
- Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
- Applications of Computational Statistics > Psychometrics
17.
Optimal estimation of a regression function, when either the response or the predictor may be missed at random, is considered. Missing at random (MAR) means that the conditional probability of missing, given response and predictor, does not depend on a variable whose values may be missed. Mean integrated squared error (MISE) is the used statistical criteria, and a nonparametric approach implies that no assumption about shape of the regression function is made. It is shown that optimal estimation depends on which variable, the response or the predictor, is missed. For a setting with missed responses, optimal estimation is based only on complete cases of observations and incomplete ones can be ignored. For a setting with missed predictors, optimal estimation is based on all cases, both complete and incomplete, and the procedure includes estimation of the conditional probability of missing the predictor given the response. Proposed estimators are completely data‐driven, do not involve imputation of missing values, and adapt to missing mechanism and smoothness of an estimated regression function. Theoretical results are complemented by the analysis of a credit score survey data. WIREs Comput Stat 2014, 6:265–275. doi: 10.1002/wics.1303 This article is categorized under:
- Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
18.
Outliers are commonly observed in psychosocial research, generally resulting in biased estimates when comparing group differences using popular mean-based models such as the analysis of variance model. Rank-based methods such as the popular Mann–Whitney–Wilcoxon (MWW) rank sum test are more effective to address such outliers. However, available methods for inference are limited to cross-sectional data and cannot be applied to longitudinal studies under missing data. In this paper, we propose a generalized MWW test for comparing multiple groups with covariates within a longitudinal data setting, by utilizing the functional response models. Inference is based on a class of U-statistics-based weighted generalized estimating equations, providing consistent and asymptotically normal estimates not only under complete but missing data as well. The proposed approach is illustrated with both real and simulated study data. 相似文献
19.
Panos G. Georgopoulos Christopher J. Brinkerhoff Sastry Isukapalli Michael Dellarco Philip J. Landrigan Paul J. Lioy 《Risk analysis》2014,34(7):1299-1316
A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data. 相似文献
20.
AbstractWe suggest shrinkage based technique for estimating covariance matrix in the high-dimensional normal model with missing data. Our approach is based on the monotone missing scheme assumption, meaning that missing values patterns occur completely at random. Our asymptotic framework allows the dimensionality p grow to infinity together with the sample size, N, and extends the methodology of Ledoit and Wolf (2004) to the case of two-step monotone missing data. Two new shrinkage-type estimators are derived and their dominance properties over the Ledoit and Wolf (2004) estimator are shown under the expected quadratic loss. We perform a simulation study and conclude that the proposed estimators are successful for a range of missing data scenarios. 相似文献