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911.
Flexible Class of Skew-Symmetric Distributions 总被引:2,自引:0,他引:2
Abstract. We propose a flexible class of skew-symmetric distributions for which the probability density function has the form of a product of a symmetric density and a skewing function. By constructing an enumerable dense subset of skewing functions on a compact set, we are able to consider a family of distributions, which can capture skewness, heavy tails and multimodality systematically. We present three illustrative examples for the fibreglass data, the simulated data from a mixture of two normal distributions and the Swiss bills data. 相似文献
912.
Testing between alternative models of choice under uncertainty: Some initial results 总被引:8,自引:6,他引:2
Raymond C. Battalio John H. Kagel Komain Jiranyakul 《Journal of Risk and Uncertainty》1990,3(1):25-50
Experiments have identified a number of well-known violations of expected utility theory, giving rise to alternative models of choice under uncertainty, all of which are able to explain these violations. In this article, predictions of several prominent rival formulations are examined. No single alternative consistently organizes choices. Among the more important inconsistencies, we identify conditions generating systematic fanning in of indifference curves in the unit probability triangle, and find risk-loving over a number of gambles with all positive payoffs, in cases where prospect theory predicts risk aversion. 相似文献
913.
Simple boundary correction for kernel density estimation 总被引:8,自引:0,他引:8
M. C. Jones 《Statistics and Computing》1993,3(3):135-146
If a probability density function has bounded support, kernel density estimates often overspill the boundaries and are consequently especially biased at and near these edges. In this paper, we consider the alleviation of this boundary problem. A simple unified framework is provided which covers a number of straightforward methods and allows for their comparison: generalized jackknifing generates a variety of simple boundary kernel formulae. A well-known method of Rice (1984) is a special case. A popular linear correction method is another: it has close connections with the boundary properties of local linear fitting (Fan and Gijbels, 1992). Links with the optimal boundary kernels of Müller (1991) are investigated. Novel boundary kernels involving kernel derivatives and generalized reflection arise too. In comparisons, various generalized jackknifing methods perform rather similarly, so this, together with its existing popularity, make linear correction as good a method as any. In an as yet unsuccessful attempt to improve on generalized jackknifing, a variety of alternative approaches is considered. A further contribution is to consider generalized jackknife boundary correction for density derivative estimation. En route to all this, a natural analogue of local polynomial regression for density estimation is defined and discussed. 相似文献
914.
We consider the signed linear rank statistics of the form where the cNi's are known real numbers, Δ∈[0,1] is an unknown real parameter,RΔNi is the rank of |YΔNi| among |YΔNj|, 1≤j≤N, ø is a score generating function, sgn y=1 or -1 according as y≥0 or <0, and YΔNj, 1≤j ≤N, are independent random variables with continuous cumulative distribution functions F(y?ΔdNj), 1≤ j≤N, respectively where the dfNi's are known real numbers. Under suitable assumptions on the c's, d's, φ and F, it is proved that the random process {SΔN?S0N?ESΔN, 0≤Δ≤1}, properly normalized, converges weakly to a Gaussian process, and this result is also true if ESΔN is replaced by ΔbN, where . As an application, we derive the asymptotic distribution of the properly normalized length of a confidence interval for Δ. 相似文献
915.
Exponential dispersion models, which are linear exponential families with a dispersion parameter, are the prototype response
distributions for generalized linear models. The Tweedie family comprises those exponential dispersion models with power mean-variance
relationships. The normal, Poisson, gamma and inverse Gaussian distributions belong to theTweedie family. Apart from these
special cases, Tweedie distributions do not have density functions which can be written in closed form. Instead, the densities
can be represented as infinite summations derived from series expansions. This article describes how the series expansions
can be summed in an numerically efficient fashion. The usefulness of the approach is demonstrated, but full machine accuracy
is shown not to be obtainable using the series expansion method for all parameter values. Derivatives of the density with
respect to the dispersion parameter are also derived to facilitate maximum likelihood estimation. The methods are demonstrated
on two data examples and compared with with Box-Cox transformations and extended quasi-likelihoood. 相似文献
916.
Accelerated Degradation Models for Failure Based on Geometric Brownian Motion and Gamma Processes 总被引:6,自引:0,他引:6
Based on a generalized cumulative damage approach with a stochastic process describing degradation, new accelerated life test models are presented in which both observed failures and degradation measures can be considered
for parametric inference of system lifetime. Incorporating an accelerated test variable, we provide several new accelerated
degradation models for failure based on the geometric Brownian motion or gamma process. It is shown that in most cases, our
models for failure can be approximated closely by accelerated test versions of Birnbaum–Saunders and inverse Gaussian distributions.
Estimation of model parameters and a model selection procedure are discussed, and two illustrative examples using real data
for carbon-film resistors and fatigue crack size are presented. 相似文献
917.
918.
In this paper we consider a binary, monotone system whose component states are dependent through the possible occurrence of independent common shocks, i.e. shocks that destroy several components at once. The individual failure of a component is also thought of as a shock. Such systems can be used to model common cause failures in reliability analysis. The system may be a technological one, or a human being. It is observed until it fails or dies. At this instant, the set of failed components and the failure time of the system are noted. The failure times of the components are not known. These are the so-called autopsy data of the system. For the case of independent components, i.e. no common shocks, Meilijson (1981), Nowik (1990), Antoine et al . (1993) and GTsemyr (1998) discuss the corresponding identifiability problem, i.e. whether the component life distributions can be determined from the distribution of the observed data. Assuming a model where autopsy data is known to be enough for identifia bility, Meilijson (1994) goes beyond the identifiability question and into maximum likelihood estimation of the parameters of the component lifetime distributions based on empirical autopsy data from a sample of several systems. He also considers life-monitoring of some components and conditional life-monitoring of some other. Here a corresponding Bayesian approach is presented for the shock model. Due to prior information one advantage of this approach is that the identifiability problem represents no obstacle. The motivation for introducing the shock model is that the autopsy model is of special importance when components can not be tested separately because it is difficult to reproduce the conditions prevailing in the functioning system. In Gåsemyr & Natvig (1997) we treat the Bayesian approach to life-monitoring and conditional life- monitoring of components 相似文献
919.
We consider the problem of estimating a partially linear panel data model whenthe error follows an one-way error components structure. We propose a feasiblesemiparametric generalized least squares (GLS) type estimator for estimating the coefficient of the linear component and show that it is asymptotically more efficient than a semiparametric ordinary least squares (OLS) type estimator. We also discussed the case when the regressor of the parametric component is correlated with the error, and propose an instrumental variable GLS-type semiparametric estimator. 相似文献
920.
Current status data arise in studies where the target measurement is the time of occurrence of some event, but observations
are limited to indicators of whether or not the event has occurred at the time the sample is collected - only the current
status of each individual with respect to event occurrence is observed. Examples of such data arise in several fields, including
demography, epidemiology, econometrics and bioassay. Although estimation of the marginal distribution of times of event occurrence
is well understood, techniques for incorporating covariate information are not well developed. This paper proposes a semiparametric
approach to estimation for regression models of current status data, using techniques from generalized additive modeling and
isotonic regression. This procedure provides simultaneous estimates of the baseline distribution of event times and covariate
effects. No parametric assumptions about the form of the baseline distribution are required. The results are illustrated using
data from a demographic survey of breastfeeding practices in developing countries, and from an epidemiological study of heterosexual
Human Immunodeficiency Virus (HIV) transmission.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献