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31.
Marco Bee 《Statistical Methods and Applications》2005,14(1):127-141
In this article we provide a rigorous treatment of one of the central statistical issues of credit risk management. GivenK-1 rating categories, the rating of a corporate bond over a certain horizon may either stay the same or change to one of the
remainingK-2 categories; in addition, it is usually the case that the rating of some bonds is withdrawn during the time interval considered
in the analysis. When estimating transition probabilities, we have thus to consider aK-th category, called withdrawal, which contains (partially) missing data. We show how maximum likelihood estimation can be
performed in this setup; whereas in discrete time our solution gives rigorous support to a solution often used in applications,
in continuous time the maximum likelihood estimator of the transition matrix computed by means of the EM algorithm represents
a significant improvement over existing methods. 相似文献
32.
Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely poor due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different applications: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. Implementation of such block algorithms is relatively easy using methods for fast sampling of Gaussian Markov random fields ( Rue, 2001 ). By comparison, Monte Carlo estimates based on single-site updating can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components. 相似文献
33.
基于需求不确定性的供应链库存控制研究 总被引:3,自引:0,他引:3
潘文安 《武汉理工大学学报(社会科学版)》2004,17(6):698-702
通过建立需求不确定性环境下供应链成员的独立存货成本模型,运用模拟退火法对该模型进行52周的仿真,求解存货最佳订购数量与最佳再订购点,从而比较出不同存货控制策略的供应链存货总成本及订单满足率的差异。 相似文献
34.
Paramjit S. Gill Tim B. Swartz 《Journal of the Royal Statistical Society. Series C, Applied statistics》2004,53(2):249-260
Summary. A fully Bayesian analysis of directed graphs, with particular emphasis on applica- tions in social networks, is explored. The model is capable of incorporating the effects of covariates, within and between block ties and multiple responses. Inference is straightforward by using software that is based on Markov chain Monte Carlo methods. Examples are provided which highlight the variety of data sets that can be entertained and the ease with which they can be analysed. 相似文献
35.
Michael Kohler 《AStA Advances in Statistical Analysis》2008,92(2):153-178
American options in discrete time can be priced by solving optimal stopping problems. This can be done by computing so-called
continuation values, which we represent as regression functions defined recursively by using the continuation values of the
next time step. We use Monte Carlo to generate data, and then we apply smoothing spline regression estimates to estimate the
continuation values from these data. All parameters of the estimate are chosen data dependent. We present results concerning
consistency and the estimates’ rate of convergence. 相似文献
36.
37.
从MDnte Carlo模拟的实现过程入手,首先通过对Monte Carlo方法原理的阐述来介绍该种方法。进一步结合具体的实例通过计算机进行模拟来解释Monte Carlo方法的具体实现过程。重点讨论在选择合理的数据生成过程的前提下,如何在Monte Carlo方法中减少模拟方差,从而提高估计精度,更好地应用这种方法来进行经济预测。 相似文献
38.
JØRUND GÅSEMYR 《Scandinavian Journal of Statistics》2003,30(1):159-173
In this paper, we present a general formulation of an algorithm, the adaptive independent chain (AIC), that was introduced in a special context in Gåsemyr et al . [ Methodol. Comput. Appl. Probab. 3 (2001)]. The algorithm aims at producing samples from a specific target distribution Π, and is an adaptive, non-Markovian version of the Metropolis–Hastings independent chain. A certain parametric class of possible proposal distributions is fixed, and the parameters of the proposal distribution are updated periodically on the basis of the recent history of the chain, thereby obtaining proposals that get ever closer to Π. We show that under certain conditions, the algorithm produces an exact sample from Π in a finite number of iterations, and hence that it converges to Π. We also present another adaptive algorithm, the componentwise adaptive independent chain (CAIC), which may be an alternative in particular in high dimensions. The CAIC may be regarded as an adaptive approximation to the Gibbs sampler updating parametric approximations to the conditionals of Π. 相似文献
39.
The authors provide an overview of optimal scaling results for the Metropolis algorithm with Gaussian proposal distribution. They address in more depth the case of high‐dimensional target distributions formed of independent, but not identically distributed components. They attempt to give an intuitive explanation as to why the well‐known optimal acceptance rate of 0.234 is not always suitable. They show how to find the asymptotically optimal acceptance rate when needed, and they explain why it is sometimes necessary to turn to inhomogeneous proposal distributions. Their results are illustrated with a simple example. 相似文献
40.
Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme‐value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set. 相似文献