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基于系数符号预测的模型选择方法AI 解读

Summary We consider a Bayesian model selection strategy based on predicting the signs of the coefficients in a regression model, i.e. we consider identification of coefficients in a full or encompassing model for which we can confidently predict whether they are positive or negative. This is useful when our main purpose in doing model selection is interpretation, since the sign of a coefficient is often of primary importance for this task. In the case of a linear model with…

广义线性混合模型中的稳健估计AI 解读

Summary Generalized linear mixed models (GLMMs) are widely used to analyse non-normal response data with extra-variation, but non-robust estimators are still routinely used. We propose robust methods for maximum quasi-likelihood and residual maximum quasi-likelihood estimation to limit the influence of outlying observations in GLMMs. The estimation procedure parallels the development of robust estimation methods in linear mixed models, but with adjustments in the dependent…

Summary A common scenario in finite population inference is that it is possible to find a working superpopulation model which explains the main features of the population but which may not capture all the fine details. In addition, there are often outliers in the population which do not follow the assumed superpopulation model. In situations like these, it is still advantageous to make use of the working model to estimate finite population quantities, provided that we do it…

脆弱性模型估计的边际似然方法AI 解读

A marginal likelihood approach is proposed for estimating the parameters in a frailty model using clustered survival data. To overcome the analytic intractability of the marginal likelihood function, we propose a Monte Carlo approximation using the technique of importance sampling. Implementation is by means of simulations from the uniform distribution. The suggested method can cope with censoring and unequal cluster sizes and can be applied to any frailty distribution with…

含随机效应广义线性模型的渐近无偏估计AI 解读

SUMMARY Obtaining estimates that are nearly unbiased has proven to be difficult when random effects are incorporated into a generalized linear model. In this paper, we propose a general method of adjusting any conveniently defined initial estimates to result in estimates which are asymptotically unbiased and consistent. The method is motivated by iterative bias correction and can be applied in principle to any parametric model. A simulation-based approach of implementing the…

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利用辅助信息估计有限总体分布函数的核方法AI 解读

This paper considers the use of auxiliary information to improve the estimation of the distribution function of a finite population. A method is proposed which combines the known distribution of the auxiliary variable with a kernel estimate of the conditional distribution of the survey variable given the value of the auxiliary variable. The resulting estimator compares favourably with existing estimators in terms of efficiency, conditional behaviour and robustness. The…

结合逻辑回归与比例风险回归的混合模型AI 解读

A model is proposed for the analysis of censored data which combines a logistic formulation for the probability of occurrence of an event with a proportional hazards specification for the time of occurrence of the event. The proposed model is a semiparametric generalization of a parametric model due to Farewell (1982). Estimates of the regression parameters are obtained by maximizing a Monte Carlo approximation of a marginal likelihood and the EM algorithm is used to…

利用辅助信息估计有限总体中位数AI 解读

SUMMARY This paper deals with the estimation, under simple random sampling, of a finite population median in the presence of an auxiliary variable. An estimator which is a simple modification of the ratio estimator for the mean is studied along with two other proposed estimators derived from a different approach. These estimators are applicable in situations where only the population median or a grouped frequency distribution of the auxiliary variable is known. On the basis…

摘要暂缺

不等概率抽样下分布函数与中位数的估计AI 解读

We consider sampling with probability proportional to size or aggregate size and derive a number of estimators of the finite population distribution function. The estimators are compared theoretically and empirically. By inverting the distribution function estimators, we obtain estimators of the finite population median. The performance of these median estimators are investigated.

比例风险模型中的全子集回归AI 解读

This paper shows that within the framework of the proportional hazards model all subsets regression can be performed with very little computational effort. A selection criterion based on a Wald statistic is motivated by an argument similar to crossvalidation in which the status of one observation is changed from uncensored to consored. This criterion is formally equivalent to Mallows's Cp and thus the problem is reduced to one readily handled by standard statistical…

比例风险模型中的全子集回归AI 解读

This paper shows that within the framework of the proportional hazards model all subsets regression can be performed with very little computational effort. A selection criterion based on a Wald statistic is motivated by an argument similar to crossvalidation in which the status of one observation is changed from uncensored to consored. This criterion is formally equivalent to Mallows's Cp and thus the problem is reduced to one readily handled by standard statistical…