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81.
一般本科院校人才培养目标与培养模式研究 总被引:10,自引:6,他引:10
文章通过对高等学校分类、定位的介绍,提出了一般本科院校的人才培养目标应根据社会的需求和学生的差异而多元化,即以应用型、复合型为主体,以研究型为激励,以技能型为补充。按照强化基础、拓宽专业、突出能力、注重创新、提高素质、全面发展的模式实施人才培养。 相似文献
82.
根据自发脑电的特点,将HMM-AR模型算法运用到脑电状态的分类中,证明它是一种非常有用的分析脑-机接口方法。将Laplacian filter、ICA和HMM-AR方法相结合,用想象左右手运动的BCI数据进行识别,得到了很好的分类结果,有效地区分脑电中运动与非运动两种状态。该算法能够在运动开始后1 s内检验到脑电信号的变化,从而证明了该算法在BCI的实用性,达到了良好的识别效果。 相似文献
83.
Øystein Kravdal 《Population studies》2018,72(2):139-156
There is still considerable uncertainty about how reproductive factors affect child mortality. This study, based on Demographic and Health Survey data from 28 countries in sub-Saharan Africa, shows that mortality is highest for firstborn children with very young mothers. Other children with young mothers, or of high birth order, also experience high mortality. Net of maternal age and birth order, a short preceding birth interval is associated with above average mortality. These patterns change, however, if time-invariant unobserved mother-level characteristics of importance for both mortality and fertility are controlled for in a multilevel–multiprocess model. Most importantly, there are smaller advantages associated with longer birth intervals and being older at first birth. The implications of alternative reproductive ‘strategies’ are discussed, taking into account that if the mother is older at birth, the child will also be born in a later calendar year, when mortality may be lower. 相似文献
84.
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations 总被引:7,自引:0,他引:7
Håvard Rue Sara Martino Nicolas Chopin 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2009,71(2):319-392
Summary. Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models , where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged. 相似文献
85.
For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15–27, 2003) propose
a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models
and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223–229, 2005). The key is a second
order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values.
The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison
the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496–509, 1999) and also time-dependent solutions
of pseudo-value regression equations. 相似文献
86.
M. P. Wand 《Australian & New Zealand Journal of Statistics》2009,51(1):9-41
Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology. 相似文献
87.
David E. Tyler Frank Critchley Lutz Dümbgen Hannu Oja 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2009,71(3):549-592
Summary. A general method for exploring multivariate data by comparing different estimates of multivariate scatter is presented. The method is based on the eigenvalue–eigenvector decomposition of one scatter matrix relative to another. In particular, it is shown that the eigenvectors can be used to generate an affine invariant co-ordinate system for the multivariate data. Consequently, we view this method as a method for invariant co-ordinate selection . By plotting the data with respect to this new invariant co-ordinate system, various data structures can be revealed. For example, under certain independent components models, it is shown that the invariant co- ordinates correspond to the independent components. Another example pertains to mixtures of elliptical distributions. In this case, it is shown that a subset of the invariant co-ordinates corresponds to Fisher's linear discriminant subspace, even though the class identifications of the data points are unknown. Some illustrative examples are given. 相似文献
88.
Edwin M. M. Ortega Fernanda B. Rizzato Clarice G. B. Demétrio 《Statistical Methods and Applications》2009,18(3):305-331
In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death,
failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this
paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data.
The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of
the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction.
Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence
and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area
is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate
model.
The authors would like to thank the editor and referees for their helpful comments. This work was supported by CNPq, Brazil. 相似文献
89.
Gabriel Escarela Luis Carlos Pérez-Ruíz Russell J. Bowater 《Journal of applied statistics》2009,36(6):647-657
A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods. 相似文献
90.
J. Fredrik Lindström 《Journal of applied statistics》2009,36(12):1369-1384
When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.
In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings. 相似文献