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161.
In this paper, we are interested in the weighted distributions of a bivariate three parameter logarithmic series distribution studied by Kocherlakota and Kocherlakota (1990). The weighted versions of the model are derived with weight W(x,y) = x[r] y[s]. Explicit expressions for the probability mass function and probability generating functions are derived in the case r = s = l. The marginal and conditional distributions are derived in the general case. The maximum likelihood estimation of the parameters, in both two parameter and three parameter cases, is studied. A procedure for computer generation of bivariate data from a discrete distribution is described. This enables us to present two examples, in order to illustrate the methods developed, for finding the maximum likelihood estimates. 相似文献
162.
Hideyuki Douke 《统计学通讯:理论与方法》2013,42(12):3015-3029
In this study we discuss the group sequential procedures for comparing two treatments based on multivariate observations in clinical trials. Also we suppose that a response vector on each of two treatments has a multivariate normal distribution with unknown covariance matrix. Then we propose a group sequential x2 statistic in order to carry out repeated significance test for hypothesis of no difference between two population mean vectors. In order to realize the group sequential test where average sample number is reduced, we propose another modified group sequential x2 statistic by extension of Jennison and Turnbull ( 1991 ). After construction of repeated confidence boundaries for making the repeated significance test, we compare two group sequential procedures based on two statistics regarding the average sample number and the power of the test in the simulations. 相似文献
163.
Claudia Czado 《Statistical Papers》2000,41(3):281-304
Summary In panel studies binary outcome measures together with time stationary and time varying explanatory variables are collected
over time on the same individual. Therefore, a regression analysis for this type of data must allow for the correlation among
the outcomes of an individual. The multivariate probit model of Ashford and Sowden (1970) was the first regression model for
multivariate binary responses. However, a likelihood analysis of the multivariate probit model with general correlation structure
for higher dimensions is intractable due to the maximization over high dimensional integrals thus severely restricting ist
applicability so far. Czado (1996) developed a Markov Chain Monte Carlo (MCMC) algorithm to overcome this difficulty. In this
paper we present an application of this algorithm to unemployment data from the Panel Study of Income Dynamics involving 11
waves of the panel study. In addition we adapt Bayesian model checking techniques based on the posterior predictive distribution
(see for example Gelman et al. (1996)) for the multivariate probit model. These help to identify mean and correlation specification
which fit the data well.
C. Czado was supported by research grant OGP0089858 of the Natural Sciences and Engineering Research Council of Canada. 相似文献
164.
D. G. Kabe 《统计学通讯:理论与方法》2013,42(9):3497-3504
Given p×n X N(βY, ∑?I), β, ∑ unknown, the noncentral multivariate beta density of the matrix L = [(YY′)-1/2Y X′ (XX′)-1XY′ (YY′)-1/2] is desired. Khatri (1964) finds this density when β is of rank unity. The present paper derives the noncentral density of L and the density of the roots matrix of L for full rank β. The dual case density of L is also obtained. The derivations are based on generalized Sverdrup's lemma, Kabe (1965), and the relationship between primal and dual density of L is explicitly established. 相似文献
165.
Victor M. Guerrero 《统计学通讯:理论与方法》2013,42(21):4568-4585
We consider the problem of estimating a trend with different amounts of smoothness for segments of a time series subjected to different variability regimes. We propose using an unobserved components model to consider the existence of at least two data segments. We first fix some desired percentages of smoothness for the trend segments and deduce the corresponding smoothing parameters involved. Once the size of each segment is chosen, the smoothing formulas here derived produce trend estimates for all segments with the desired smoothness as well as their corresponding estimated variances. Empirical examples from demography and economics illustrate our proposal. 相似文献
166.
J.M. Muñoz Pichardo J. Muñoz García J.M. Fernández Ponce M.D. Jiménez Garnero 《统计学通讯:理论与方法》2013,42(3):529-547
In this paper we obtain several influence measures for the multivariate linear general model through the approach proposed by Muñoz-Pichardo et al. (1995), which is based on the concept of conditional bias. An interesting charasteristic of this approach is that it does not require any distributional hypothesis. Appling the obtained results to the multivariate regression model, we obtain some measures proposed by other authors. Nevertheless, on the results obtained in this paper, we emphasize two aspects. First, they provide a theoretical foundation for measures proposed by other authors for the mul¬tivariate regression model. Second, they can be applied to any linear model that can be formulated as a particular case of the multivariate linear general model. In particular, we carry out an application to the multivariate analysis of covariance. 相似文献
167.
Birgitta Törnkvist 《统计学通讯:模拟与计算》2013,42(4):1247-1264
In modelling a system structure it is often important to test the constancy of the structural parameters. If the hypothesis of constant parameters is rejected we wish to characterize the parameter variation. In this paper we examine the usefulness of the MOSUMSQ test statistics for locating and characterizing the parameter variation in linear regression models. The means and the variances of the test statistics are given for non-random parameter variation. The observed path of the MOSUMSQ test statistics provides valuable knowledge about the time of the onset of the parameter change and whether it is instantaneous or gradual. 相似文献
168.
Beiyao Zheng 《统计学通讯:理论与方法》2013,42(5-6):1405-1417
We propose Bayesian parameter estimation in a multidimensional item response theory model using the Gibbs sampling algorithm. We apply this approach to dichotomous responses to a questionnaire on sleep quality. The analysis helps determine the underlying dimensions. 相似文献
169.
An improved likelihood-based method based on Fraser et al. (1999) is proposed in this paper to test the significance of the second lag of the stationary AR(2) model. Compared with the test proposed by Fan and Yao (2003) and the signed log-likelihood ratio test, the proposed method has remarkable accuracy. Simulation studies are performed to illustrate the accuracy of the proposed method. Application of the proposed method on historical data is presented to demonstrate the implementation of this method. Furthermore, the method can be extended to the general AR(p) model. 相似文献
170.
The problem of selection of the best multivariate population is given a new formulation which does not involve reducing the populations to univariate quantities. This formulation's solution is developed for known, and (using the Heteroscedastic Method) also for unknown, variance-covariance matrices. Preference reversals and arbitrary nonlinear preference functions are explicitly allowed in this new theory 相似文献