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981.
A Bayesian multi-category kernel classification method is proposed. The algorithm performs the classification of the projections
of the data to the principal axes of the feature space. The advantage of this approach is that the regression coefficients
are identifiable and sparse, leading to large computational savings and improved classification performance. The degree of
sparsity is regulated in a novel framework based on Bayesian decision theory. The Gibbs sampler is implemented to find the
posterior distributions of the parameters, thus probability distributions of prediction can be obtained for new data points,
which gives a more complete picture of classification. The algorithm is aimed at high dimensional data sets where the dimension
of measurements exceeds the number of observations. The applications considered in this paper are microarray, image processing
and near-infrared spectroscopy data. 相似文献
982.
There is a wide variety of stochastic ordering problems where K groups (typically ordered with respect to time) are observed along with a (continuous) response. The interest of the study may be on finding the change-point group, i.e. the group where an inversion of trend of the variable under study is observed. A change point is not merely a maximum (or a minimum) of the time-series function, but a further requirement is that the trend of the time-series is monotonically increasing before that point, and monotonically decreasing afterwards. A suitable solution can be provided within a conditional approach, i.e. by considering some suitable nonparametric combination of dependent tests for simple stochastic ordering problems. The proposed procedure is very flexible and can be extended to trend and/or repeated measure problems. Some comparisons through simulations and examples with the well known Mack & Wolfe test for umbrella alternative and with Page’s test for trend problems with correlated data are investigated. 相似文献
983.
984.
Simple nonparametric estimates of the conditional distribution of a response variable given a covariate are often useful for
data exploration purposes or to help with the specification or validation of a parametric or semi-parametric regression model.
In this paper we propose such an estimator in the case where the response variable is interval-censored and the covariate
is continuous. Our approach consists in adding weights that depend on the covariate value in the self-consistency equation
proposed by Turnbull (J R Stat Soc Ser B 38:290–295, 1976), which results in an estimator that is no more difficult to implement
than Turnbull’s estimator itself. We show the convergence of our algorithm and that our estimator reduces to the generalized
Kaplan–Meier estimator (Beran, Nonparametric regression with randomly censored survival data, 1981) when the data are either
complete or right-censored. We demonstrate by simulation that the estimator, bootstrap variance estimation and bandwidth selection
(by rule of thumb or cross-validation) all perform well in finite samples. We illustrate the method by applying it to a dataset
from a study on the incidence of HIV in a group of female sex workers from Kinshasa. 相似文献
985.
This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics.
We take on the viewpoint of a patient who is interested in an individual migraine management strategy. Since factors influencing
migraine can differ between patients in number and magnitude, we show how a patient’s headache calendar reporting the severity
measurements on an ordinal scale can be used to determine the dominating factors for this special patient. One also has to
account for dependencies among the measurements. For this the autoregressive ordinal probit (AOP) model of Müller and Czado
(J Comput Graph Stat 14: 320–338, 2005) is utilized and fitted to a single patient’s migraine data by a grouped move multigrid Monte Carlo (GM-MGMC) Gibbs sampler.
Initially, covariates are selected using proportional odds models. Model fit and model comparison are discussed. A comparison
with proportional odds specifications shows that the AOP models are preferred. 相似文献
986.
Rainer Schlittgen 《AStA Advances in Statistical Analysis》2011,95(2):205-217
Clusterwise regression aims to cluster data sets where the clusters are characterized by their specific regression coefficients
in a linear regression model. In this paper, we propose a method for determining a partition which uses an idea of robust
regression. We start with some random weighting to determine a start partition and continue in the spirit of M-estimators.
The residuals for all regressions are used to assign the observations to the different groups. As target function we use the
determination coefficient R2wR^{2}_{w} for the overall model. This coefficient is suitably defined for weighted regression. 相似文献
987.
Most approaches to applying knowledge-based techniques for data analyses concentrate on the context-independent statistical support. EXPLORA however is developed for the subject-specific interpretation with regard to the contents of the data to be analyzed (i.e. content interpretation). Therefore its knowledge base includes also the objects and semantic relations of the real system that produces the data. In this paper we describe the functional model representing the process of content interpretation, summarize the software architecture of the system and give some examples of its applications by pilot-users in survey analysis. EXPLORA addresses applications with data produced regularly which have to be analyzed in a routine way. The system systematically searches for statistical results (facts) to detect relations which possibly could be overlooked by a human analyst. On the other hand EXPLORA will help overcome the large bulk of information which today is usually still produced when presenting the data. Therefore a second knowledge process of content interpretation consists in discovering messages about the data by condensing the facts. Approaches for inductive generalization which have been developed for machine learning are utilized to identify common values of attributes of the objects to which the facts relate. At a later stage the system searches for interesting facts by applying redundancy rules and domaindependent selection rules. EXPLORA formulates the messages in terms of the domain, groups and orders them and even provides flexible navigations in the fact spaces. 相似文献
988.
van den Hout A Böckenholt U van der Heijden PG 《Journal of the Royal Statistical Society. Series C, Applied statistics》2010,59(4):723-736
Randomized response is a misclassification design to estimate the prevalence of sensitive behaviour. Respondents who do not follow the instructions of the design are considered to be cheating. A mixture model is proposed to estimate the prevalence of sensitive behaviour and cheating in the case of a dual sampling scheme with direct questioning and randomized response. The mixing weight is the probability of cheating, where cheating is modelled separately for direct questioning and randomized response. For Bayesian inference, Markov chain Monte Carlo sampling is applied to sample parameter values from the posterior. The model makes it possible to analyse dual sample scheme data in a unified way and to assess cheating for direct questions as well as for randomized response questions. The research is illustrated with randomized response data concerning violations of regulations for social benefit. 相似文献
989.
Friedrich Leisch 《Statistics and Computing》2010,20(4):457-469
Centroid-based partitioning cluster analysis is a popular method for segmenting data into more homogeneous subgroups. Visualization
can help tremendously to understand the positions of these subgroups relative to each other in higher dimensional spaces and
to assess the quality of partitions. In this paper we present several improvements on existing cluster displays using neighborhood
graphs with edge weights based on cluster separation and convex hulls of inner and outer cluster regions. A new display called
shadow-stars can be used to diagnose pairwise cluster separation with respect to the distribution of the original data. Artificial
data and two case studies with real data are used to demonstrate the techniques. 相似文献
990.
We present a new semi-parametric model for the prediction of implied volatility surfaces that can be estimated using machine
learning algorithms. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes
generalized residuals computed as differences between observed and estimated implied volatilities. To overcome the poor predictive
power of existing models, we include a grid in the region of interest, and implement a cross-validation strategy to find an
optimal stopping value for the boosting procedure. Back testing the out-of-sample performance on a large data set of implied
volatilities from S&P 500 options, we provide empirical evidence of the strong predictive power of our model. 相似文献