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981.
Jean‐Yves Dauxois 《Revue canadienne de statistique》2000,28(3):615-620
L'auteur introduit un modèle d'intensité mutltiplicative généralisé pour les processus ponctuels à compensateur discontinu. Il en étudie l'inférence par les martingales et obtient un résultat de normalité asymptotique. Il applique ces résultats à l'estimateur de Nelson‐Aalen du taux de panne cumulé dans le cas de données de durée de vie censuées et de loi non nécessairement continues. 相似文献
982.
How should a network experiment be designed to achieve high statistical power? Experimental treatments on networks may spread. Randomizing assignment of treatment to nodes enhances learning about the counterfactual causal effects of a social network experiment and also requires new methodology (ex. Aronow and Samii, 2017a, Bowers et al., 2013, Toulis and Kao, 2013). In this paper we show that the way in which a treatment propagates across a social network affects the statistical power of an experimental design. As such, prior information regarding treatment propagation should be incorporated into the experimental design. Our findings justify reconsideration of standard practice in circumstances where units are presumed to be independent even in simple experiments: information about treatment effects is not maximized when we assign half the units to treatment and half to control. We also present an example in which statistical power depends on the extent to which the network degree of nodes is correlated with treatment assignment probability. We recommend that researchers think carefully about the underlying treatment propagation model motivating their study in designing an experiment on a network. 相似文献
983.
Steven N. Goodman 《The American statistician》2019,73(1):26-30
ABSTRACTThe current concerns about reproducibility have focused attention on proper use of statistics across the sciences. This gives statisticians an extraordinary opportunity to change what are widely regarded as statistical practices detrimental to the cause of good science. However, how that should be done is enormously complex, made more difficult by the balkanization of research methods and statistical traditions across scientific subdisciplines. Working within those sciences while also allying with science reform movements—operating simultaneously on the micro and macro levels—are the key to making lasting change in applied science. 相似文献
984.
提出了一种基于概率模糊逻辑来推断蛋白质信号网络的建模方法,能够同时处理蛋白质信号网络中的随机性和模糊性.采用概率模糊规则建立蛋白质之间的因果关系参数模型,并利用模糊后件分布函数族的差异程度来度量因果关系的强度,进而推断蛋白质之间的因果关系.在模拟数据和人体T细胞实验数据集上的测试结果表明,该方法为蛋白质信号网络建模提供了一种有效的方法. 相似文献
985.
Different strategies have been proposed to improve mixing and convergence properties of Markov Chain Monte Carlo algorithms.
These are mainly concerned with customizing the proposal density in the Metropolis–Hastings algorithm to the specific target
density and require a detailed exploratory analysis of the stationary distribution and/or some preliminary experiments to
determine an efficient proposal. Various Metropolis–Hastings algorithms have been suggested that make use of previously sampled
states in defining an adaptive proposal density. Here we propose a general class of adaptive Metropolis–Hastings algorithms
based on Metropolis–Hastings-within-Gibbs sampling. For the case of a one-dimensional target distribution, we present two
novel algorithms using mixtures of triangular and trapezoidal densities. These can also be seen as improved versions of the
all-purpose adaptive rejection Metropolis sampling (ARMS) algorithm to sample from non-logconcave univariate densities. Using
various different examples, we demonstrate their properties and efficiencies and point out their advantages over ARMS and
other adaptive alternatives such as the Normal Kernel Coupler. 相似文献
986.
Sanjay Chaudhuri Michael D. Perlman 《Journal of statistical planning and inference》2005,130(1-2):229-250
The inverse Gaussian family (IG) (μ,λ) is a versatile family for modelling nonnegative right-skewed data. In this paper, we propose robust methods for testing homogeneity of the scale-like parameters λi from k independent IG populations subject to order restrictions. Robustness of the procedures is examined for a variety of IG-symmetric alternatives including lognormal and the recently introduced contaminated inverse Gaussian populations. Our study shows that these inference procedures for the inverse Gaussian scale-like parameters and their properties exhibit striking similarities to those of the scale parameters of the normal distribution. 相似文献
987.
988.
Nikolaos Demiris Philip D. O'Neill 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2005,67(5):731-745
Summary. The paper is concerned with new methodology for statistical inference for final outcome infectious disease data using certain structured population stochastic epidemic models. A major obstacle to inference for such models is that the likelihood is both analytically and numerically intractable. The approach that is taken here is to impute missing information in the form of a random graph that describes the potential infectious contacts between individuals. This level of imputation overcomes various constraints of existing methodologies and yields more detailed information about the spread of disease. The methods are illustrated with both real and test data. 相似文献
989.
Eiji Minemura 《Journal of applied statistics》2006,33(10):1085-1104
In this paper, the author presents an efficient method of analyzing an interest-rate model using a new approach called 'data augmentation Bayesian forecasting.' First, a dynamic linear model estimation was constructed with a hierarchically-incorporated model. Next, an observational replication was generated based on the one-step forecast distribution derived from the model. A Markov-chain Monte Carlo sampling method was conducted on it as a new observation and unknown parameters were estimated. At that time, the EM algorithm was applied to establish initial values of unknown parameters while the 'quasi Bayes factor' was used to appreciate parameter candidates. 'Data augmentation Bayesian forecasting' is a method of evaluating the transition and history of 'future,' 'present' and 'past' of an arbitrary stochastic process by which an appropriate evaluation is conducted based on the probability measure that has been sequentially modified with additional information. It would be possible to use future prediction results for modifying the model to grasp the present state or re-evaluate the past state. It would be also possible to raise the degree of precision in predicting the future through the modification of the present and the past. Thus, 'data augmentation Bayesian forecasting' is applicable not only in the field of financial data analysis but also in forecasting and controlling the stochastic process. 相似文献
990.
认知语用学中的翻译观 总被引:3,自引:0,他引:3
凤群 《安徽农业大学学报(社会科学版)》2004,13(3):124-128
本文对传统翻译活动进行了回顾,指出其理论基础的不足,在此基础上,从认知语用学的角度,对翻译的理解与表达活动进行了新的阐述,认为翻译理解实际上是译者对自身认知语境的搜索过程,翻译的表达是基于对读者认知语境的预测的交际策略的选择,并受原作者认知语境的制约.本文对翻译中的基本问题如直译或意译问题也作了探讨. 相似文献