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991.
Relative risk frailty models are used extensively in analyzing clustered and/or recurrent time-to-event data. In this paper,
Laplace’s approximation for integrals is applied to marginal distributions of data arising from parametric relative risk frailty
models. Under regularity conditions, the approximate maximum likelihood estimators (MLE) are consistent with a rate of convergence
that depends on both the number of subjects and number of members per subject. We compare the approximate MLE against alternative
estimators using limited simulation and demonstrate the utility of Laplace’s approximation approach by analyzing U.S. patient
waiting time to deceased kidney transplant data. 相似文献
992.
George Y. C. Wong 《统计学通讯:模拟与计算》2013,42(8):1489-1500
We consider the estimation problem under the Lehmann model with interval-censored data, but focus on the computational issues. There are two methods for computing the semi-parametric maximum likelihood estimator (SMLE) under the Lehmann model (or called Cox model): the Newton-Raphson (NR) method and the profile likelihood (PL) method. We show that they often do not get close to the SMLE. We propose several approach to overcome the computational difficulty and apply our method to a breast cancer research data set. 相似文献
993.
《统计学通讯:理论与方法》2013,42(10):1989-2004
We consider a linear regression with the error term that obeys an autoregressive model of infinite order and estimate parameters of the models. The parameters of the autoregressive model should be estimated based on estimated residuals obtained by means of the method of ordinary least squares, because the errors are unobservable. The consistency of the coefficients, variance and spectral density of the model obeyed by the error term is shown. Further, we estimate the coefficients of the linear regression by means of the method of estimated generalized least squares. We also show the consistency of the estimator. 相似文献
994.
《Journal of Statistical Computation and Simulation》2012,82(2):161-172
We propose a model for count data from two-stage cluster sampling, where observations within each cluster are subjected simultaneously to internal influences and external factors at the cluster level. This model can be seen as a two-stage hierarchical model with local and global predictors. This parameter-driven model causes the counts within a cluster to share a common latent factor and to be correlated. Maximum likelihood (ml) estimation based on an EM algorithm for the model is discussed. Simulation study is carried out to assess the benefit of using ml estimates compared to a standard Poisson regression analysis that ignores the within cluster correlation. 相似文献
995.
《Journal of Statistical Computation and Simulation》2012,82(5):973-985
An algorithm for sampling from non-log-concave multivariate distributions is proposed, which improves the adaptive rejection Metropolis sampling (ARMS) algorithm by incorporating the hit and run sampling. It is not rare that the ARMS is trapped away from some subspace with significant probability in the support of the multivariate distribution. While the ARMS updates samples only in the directions that are parallel to dimensions, our proposed method, the hit and run ARMS (HARARMS), updates samples in arbitrary directions determined by the hit and run algorithm, which makes it almost not possible to be trapped in any isolated subspaces. The HARARMS performs the same as ARMS in a single dimension while more reliable in multidimensional spaces. Its performance is illustrated by a Bayesian free-knot spline regression example. We showed that it overcomes the well-known ‘lethargy’ property and decisively find the global optimal number and locations of the knots of the spline function. 相似文献
996.
梁金喜 《合肥工业大学学报(社会科学版)》2001,15(Z1):118-121
文章运用辩证唯物主义观点,分析了高校后勤社会化改革过程中甲、乙方之间既对立又统一的辩证关系,提出了在后勤改革逐步深化的过程中正确处理好甲、乙方关系的重要性和迫切性,并对进一步坚定后勤社会化改革方向,认真处理好甲、乙方关系等问题进行了探讨. 相似文献
997.
When confronted with multiple covariates and a response variable, analysts sometimes apply a variable‐selection algorithm to the covariate‐response data to identify a subset of covariates potentially associated with the response, and then wish to make inferences about parameters in a model for the marginal association between the selected covariates and the response. If an independent data set were available, the parameters of interest could be estimated by using standard inference methods to fit the postulated marginal model to the independent data set. However, when applied to the same data set used by the variable selector, standard (“naive”) methods can lead to distorted inferences. The authors develop testing and interval estimation methods for parameters reflecting the marginal association between the selected covariates and response variable, based on the same data set used for variable selection. They provide theoretical justification for the proposed methods, present results to guide their implementation, and use simulations to assess and compare their performance to a sample‐splitting approach. The methods are illustrated with data from a recent AIDS study. The Canadian Journal of Statistics 37: 625–644; 2009 © 2009 Statistical Society of Canada 相似文献
998.
999.
Spatially-adaptive Penalties for Spline Fitting 总被引:2,自引:0,他引:2
David Ruppert & Raymond J. Carroll 《Australian & New Zealand Journal of Statistics》2000,42(2):205-223
The paper studies spline fitting with a roughness penalty that adapts to spatial heterogeneity in the regression function. The estimates are p th degree piecewise polynomials with p − 1 continuous derivatives. A large and fixed number of knots is used and smoothing is achieved by putting a quadratic penalty on the jumps of the p th derivative at the knots. To be spatially adaptive, the logarithm of the penalty is itself a linear spline but with relatively few knots and with values at the knots chosen to minimize the generalized cross validation (GCV) criterion. This locally-adaptive spline estimator is compared with other spline estimators in the literature such as cubic smoothing splines and knot-selection techniques for least squares regression. Our estimator can be interpreted as an empirical Bayes estimate for a prior allowing spatial heterogeneity. In cases of spatially heterogeneous regression functions, empirical Bayes confidence intervals using this prior achieve better pointwise coverage probabilities than confidence intervals based on a global-penalty parameter. The method is developed first for univariate models and then extended to additive models. 相似文献
1000.
The problem of estimation of the parameters in a logistic regression model is considered under multicollinearity situation
when it is suspected that the parameter of the logistic regression model may be restricted to a subspace. We study the properties
of the preliminary test based on the minimum ϕ -divergence estimator as well as in the ϕ -divergence test statistic. The minimum ϕ -divergence estimator is a natural extension of the maximum likelihood estimator and the ϕ -divergence test statistics is a family of the test statistics for testing the hypothesis that the regression coefficients
may be restricted to a subspace. 相似文献