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1.
BAYESIAN SUBSET SELECTION AND MODEL AVERAGING USING A CENTRED AND DISPERSED PRIOR FOR THE ERROR VARIANCE 总被引:1,自引:0,他引:1
Edward Cripps Robert Kohn David Nott 《Australian & New Zealand Journal of Statistics》2006,48(2):237-252
This article proposes a new data‐based prior distribution for the error variance in a Gaussian linear regression model, when the model is used for Bayesian variable selection and model averaging. For a given subset of variables in the model, this prior has a mode that is an unbiased estimator of the error variance but is suitably dispersed to make it uninformative relative to the marginal likelihood. The advantage of this empirical Bayes prior for the error variance is that it is centred and dispersed sensibly and avoids the arbitrary specification of hyperparameters. The performance of the new prior is compared to that of a prior proposed previously in the literature using several simulated examples and two loss functions. For each example our paper also reports results for the model that orthogonalizes the predictor variables before performing subset selection. A real example is also investigated. The empirical results suggest that for both the simulated and real data, the performance of the estimators based on the prior proposed in our article compares favourably with that of a prior used previously in the literature. 相似文献
2.
先前经验、学习风格与创业能力的实证研究 总被引:3,自引:0,他引:3
如何提升创业者的创业能力是目前创业实践中亟待解决的问题。在将经验和能力区分开的基础上,从创业学习的视角出发,深入挖掘创业者先前经验与创业能力之间的作用关系机制,探讨创业者经验向创业能力转化的内在机理,采用探索性因子分析、验证性因子分析、层级回归分析等方法,对173家中国新创企业的调查问卷进行分析。研究结果表明,学习风格在创业者先前经验与创业能力的关系中发挥调节作用,不同类型的先前经验对不同类型的创业能力产生影响,创业者的学习风格也并非像以往研究所认为的那样存在优劣之分,而是存在一个匹配关系,当创业者的信息获取/转化方式与他的先前经验类型相匹配时,这一类型的经验才能更好的转化为创业能力。 相似文献
3.
Kontkanen P. Myllymäki P. Silander T. Tirri H. Grünwald P. 《Statistics and Computing》2000,10(1):39-54
In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used. 相似文献
4.
We consider the competing risks set-up. In many practical situations, the conditional probability of the cause of failure given the failure time is of direct interest. We propose to model the competing risks by the overall hazard rate and the conditional probabilities rather than the cause-specific hazards. We adopt a Bayesian smoothing approach for both quantities of interest. Illustrations are given at the end. 相似文献
5.
本文放松了Easley和O’Hara信息成本为0的假设,在他们的信息结构模型的理论框架下,构建了一个引入信息成本因素的信息结构模型。从信息结构的四个方面:信息成本、信息风险、信息披露的质量和先验信息质量研究了信息结构与股权融资成本之间的关系,得出了四个推论,从而拓展了信息结构模型。在进一步的实证研究中,选取市场微观结构理论中的逆向选择成本、知情交易概率-PIN分别作为信息成本和信息风险的衡量指标,研究发现:信息成本与股权融资成本之间呈倒‘U’型曲线关系;信息风险越高的股票股权融资成本越高;信息披露质量越高的公司,股权融资成本越低;先验信息质量越高,股权融资成本越低;从而对推论进行了有效验证。本文与Easley和O’Hara最大不同在于引入了信息成本因素,并且用实证方法对推论进行了验证,具有一定的开创性。 相似文献
6.
Ying-Ying Zhang Ze-Yu Wang Zheng-Min Duan Wen Mi 《Journal of Statistical Computation and Simulation》2019,89(16):3061-3074
For the hierarchical Poisson and gamma model, we calculate the Bayes posterior estimator of the parameter of the Poisson distribution under Stein's loss function which penalizes gross overestimation and gross underestimation equally and the corresponding Posterior Expected Stein's Loss (PESL). We also obtain the Bayes posterior estimator of the parameter under the squared error loss and the corresponding PESL. Moreover, we obtain the empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior by two methods. In numerical simulations, we have illustrated: The two inequalities of the Bayes posterior estimators and the PESLs; the moment estimators and the Maximum Likelihood Estimators (MLEs) are consistent estimators of the hyperparameters; the goodness-of-fit of the model to the simulated data. The numerical results indicate that the MLEs are better than the moment estimators when estimating the hyperparameters. Finally, we exploit the attendance data on 314 high school juniors from two urban high schools to illustrate our theoretical studies. 相似文献
7.
Frank Tuyl 《The American statistician》2019,73(2):151-158
In the context of an objective Bayesian approach to the multinomial model, Dirichlet(a, …, a) priors with a < 1 have previously been shown to be inadequate in the presence of zero counts, suggesting that the uniform prior (a = 1) is the preferred candidate. In the presence of many zero counts, however, this prior may not be satisfactory either. A model selection approach is proposed, allowing for the possibility of zero parameters corresponding to zero count categories. This approach results in a posterior mixture of Dirichlet distributions and marginal mixtures of beta distributions, which seem to avoid the problems that potentially result from the various proposed Dirichlet priors, in particular in the context of extreme data with zero counts. 相似文献
8.
Bayesian Conditional Mean Estimation in Log‐Normal Linear Regression Models with Finite Quadratic Expected Loss 下载免费PDF全文
Log‐normal linear regression models are popular in many fields of research. Bayesian estimation of the conditional mean of the dependent variable is problematic as many choices of the prior for the variance (on the log‐scale) lead to posterior distributions with no finite moments. We propose a generalized inverse Gaussian prior for this variance and derive the conditions on the prior parameters that yield posterior distributions of the conditional mean of the dependent variable with finite moments up to a pre‐specified order. The conditions depend on one of the three parameters of the suggested prior; the other two have an influence on inferences for small and medium sample sizes. A second goal of this paper is to discuss how to choose these parameters according to different criteria including the optimization of frequentist properties of posterior means. 相似文献
9.
目前遗传资源和传统知识法律保护机制国际探索的成就与不足——评CBD事先知情同意机制和FAO农民权机制 总被引:6,自引:0,他引:6
严永和 《贵州大学学报(社会科学版)》2006,24(3):31-36
传统知识是长期以来传统部族在长期的生产生活实践中创造出来的知识、技术、经验的总称,是一种知识形态和知识产品。CBD等国际组织对遗传资源及有关传统知识的保护进行了多年的探索,在保护机制上取得了一定的创新。CBD开创的事先知情同意机制和FAO开创的农民权机制为其代表。两种机制均在物权层面设计了遗传资源及有关传统知识的保护规则,但前者具有较强的保障力,后者基本上还是一种道义意义上的权利。但只有提供知识产权保护方能充分实现传统部族在传统知识上的利益。 相似文献
10.
Yang Yu Zhihong Zou Shanshan Wang 《Journal of Statistical Computation and Simulation》2019,89(17):3290-3312
This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant. 相似文献