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51.
半参数回归模型中小波估计的随机加权逼近速度 总被引:10,自引:1,他引:9
把小波光滑方法和随机加权方法结合在一起,获得了半参数回归模型中参数分量的小波估计的随机加权逼近速度为σ(n^-1/2)。因此,从大样本意义上说,小波光滑方法和随机加权方法对半参数回归模型是可用的。 相似文献
52.
Joanna Tarasińska 《Applications of Mathematics》2003,48(1):67-72
Two estimates of the regression coefficient in bivariate normal distribution are considered: the usual one based on a sample and a new one making use of additional observations of one of the variables. They are compared with respect to variance. The same is done for two regression lines. The conclusion is that the additional observations are worth using only when the sample is very small. 相似文献
53.
Efficiencies of the maximum pseudolikelihood estimator and a number of related estimators for the case-cohort sampling design in the proportional hazards regression model are studied. The asymptotic information and lower bound for estimating the parametric regression parameter are calculated based on the effective score, which is obtained by determining the component of the parametric score orthogonal to the space generated by the infinite-dimensional nuisance parameter. The asymptotic distributions of the maximum pseudolikelihood and related estimators in an i.i.d. setting show that these estimators do not achieve the computed asymptotic lower bound. Simple guidelines are provided to determine in which instances such estimators are close enough to efficient for practical purposes. 相似文献
54.
Sam Efromovich 《Annals of the Institute of Statistical Mathematics》2004,56(2):205-223
A blockwise shrinkage is a popular procedure of adaptation that has allowed the statisticians to establish an impressive bouquet
of asymptotic mathematical results and develop softwares for solving practical problems. Traditionally risks of the estimates
are studied via upper bounds that imply sufficient conditions for a blockwise shrinkage procedure to be minimax. This article
suggests to analyze the estimates via exact (non-asymptotic) lower bounds established for a no-signal setting. The approach
complements the familiar minimax, Bayesian and numerical analysis, it allows to find necessary conditions for a procedure
to attain desired rates, and it sheds a new light on popular choices of blocks and thresholds recommended in the literature.
Mathematical results are complemented by a numerical study.
Supported in part by NSF Grants DMS-9971051 and DMS-0243606. 相似文献
55.
This paper considers the estimation problem for a trigonometric regression model with the noise specified by the Ornstein–Uhlenbeck
process with unknown parameter. We propose a sequential procedure which ensures a prescribed mean square precision uniformly
in the nuisance parameter. The asymptotic behaviour of the procedure duration mean has been studied.
This revised version was published online in August 2006 with corrections to the Cover Date. 相似文献
56.
在多项选择题测验分数等于真实分数和猜测分数之和的假设模型下,本文得到了一个多项选择题测验信度的理论公式,并由此给出了测验信度的估计方法。最后,通过两个例子,说明了本文提出的方法在测验信度分析中的应用,并将这种方法与教育测量中常用的库德——理查逊方法(Kuder-Richardson)进行了比较 相似文献
57.
一类非线性算子方程的多重正解及其应用 总被引:18,自引:5,他引:13
本文利用不动点指数理论对一类非线性算子方程建立了多重正解的存在性定理,并将所获结果应用到Hammerstein积分方程和Sturm Liouvile两点边值问题,得到了新的结论,本质上改进和推广了[2,4,5,6,8-12]的工作. 相似文献
58.
Ro Jin Pak Ayanendranath Basu 《Annals of the Institute of Statistical Mathematics》1998,50(3):503-521
This paper deals with the minimum disparity estimation in linear regression models. The estimators are defined as statistical quantities which minimize the blended weight Hellinger distance between a weighted kernel density estimator of errors and a smoothed model density of errors. It is shown that the estimators of the regression parameters are asymptotic normally distributed and efficient at the model if the weights of the density estimators are appropriately chosen. 相似文献
59.
Miguel A. Arcones 《Annals of the Institute of Statistical Mathematics》1998,50(1):87-117
We consider exact weak and strong Bahadur-Kiefer representations of the least absolute deviation estimator for the linear regression model. The precise behavior of these representations is obtained under minimal conditions. 相似文献
60.
Véronique Cariou 《Computational Statistics》2006,21(2):325-341
Summary Several data can be presented as interval curves where intervals reflect a within variability. In particular, this representation
is well adapted for load profiles, which depict the electricity consumption of a class of customers. Electricity load profiling
consists in assigning a daily load curve to a customer based on their characteristics such as energy requirement. Within the
load profiling scope, this paper investigates the extension of multivariate regression trees to the case of interval dependent
(or response) variables. The tree method aims at setting up simultaneously load profiles and their assignment rules based
on independent variables. The extension of multivariate regression trees to interval responses is detailed and a global approach
is defined. It consists in a first stage of a dimension reduction of the interval response variables. Thereafter, the extension
of the tree method is applied to the first principal interval components. Outputs are the classes of the interval curves where
each class is characterized both by an interval load profile (e.g. the class prototype) and an assignment rule based on the
independent variables. 相似文献