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11.
半参数模型约束条件下估计量的统计诊断 总被引:1,自引:0,他引:1
利用最小惩罚二乘原理构造加权惩罚平方和,导出了约束条件下,半参数模型中正规化矩阵正定时半参数平差的计算方法,用直接法得到了参数和半参数的估计量,并将此方法引入测绘领域,给出了相应的公式。 相似文献
13.
Klaus Ziegler 《Journal of multivariate analysis》1997,62(2):233-272
Functional central limit theorems for triangular arrays of rowwise independent stochastic processes are established by a method replacing tail probabilities by expectations throughout. The main tool is a maximal inequality based on a preliminary version proved by P. Gaenssler and Th. Schlumprecht. Its essential refinement used here is achieved by an additional inequality due to M. Ledoux and M. Talagrand. The entropy condition emerging in our theorems was introduced by K. S. Alexander, whose functional central limit theorem for so-calledmeasure-like processeswill be also regained. Applications concern, in particular, so-calledrandom measure processeswhich include function-indexed empirical processes and partial-sum processes (with random or fixed locations). In this context, we obtain generalizations of results due to K. S. Alexander, M. A. Arcones, P. Gaenssler, and K. Ziegler. Further examples include nonparametric regression and intensity estimation for spatial Poisson processes. 相似文献
14.
15.
The computation ofL
1 smoothing splines on large data sets is often desirable, but computationally infeasible. A locally weighted, LAD smoothing spline based smoother is suggested, and preliminary results will be discussed. Specifically, one can seek smoothing splines in the spacesW
m
(D), with [0, 1]
n
D. We assume data of the formy
i
=f(t
i
)+
i
,i=1,..., N with {t
i
}
i=1
N
D, the
i
are errors withE(
i
)=0, andf is assumed to be inW
m
. An LAD smoothing spline is the solution,s
, of the following optimization problem
相似文献
16.
无初始状态统计知识时多变量系统的最优平滑与预报 总被引:1,自引:0,他引:1
刘轩黄 《海南大学学报(自然科学版)》1998,16(3):210-214
在无初始状态统计知识的情况下,我们分别就固定点平滑、固定滞后平滑与预报3种情况给出了动态系统的最优线性最小偏差估计(BLIMBE),同时还给出了完全(i,j,Ti)-可重构性的概念及系统完全(i,j,Ti)-可重构的充要条件 相似文献
17.
核医学图像的平滑及自适应增强算法 总被引:1,自引:0,他引:1
针对核医学图像的特点,提出核医学图像的邻域加权平滑和自适应增强算法,用于去除图像噪声和增强图像,以改善图像的视觉效果,提高对核医学图像判读的正确性 相似文献
18.
A note on smoothed estimating functions 总被引:1,自引:0,他引:1
A. Thavaneswaran Jagbir Singh 《Annals of the Institute of Statistical Mathematics》1993,45(4):721-729
The kernel estimate of regression function in likelihood based models has been studied in Staniswalis (1989,J. Amer. Statist. Assoc.,84, 276–283). The notion of optimal estimation for the nonparametric kernel estimation of semimartingale intensity (t) is proposed. The goal is to arrive at a nonparametric estimate
of 0=(t
0) for a fixed pointt
0 [0, 1]. We consider the estimator that is a solution of the smoothed optimal estimating equation
is the optimal estimating function as in Thavaneswaran and Thompson (1986,J. Appl. Probab.,23, 409–417). 相似文献
19.
We propose an algorithm for the computation ofL
1 (LAD) smoothing splines in the spacesW
M
(D), with
. We assume one is given data of the formy
i
=(f(t
i
) +
i
, i=1,...,N with {itti}
i=1
N D
, the
i
are errors withE(
i
)=0, andf is assumed to be inW
M
. The LAD smoothing spline, for fixed smoothing parameter0, is defined as the solution,s
, of the optimization problem
(1/N)
i=1
N
¦y
i
–g(t
i
¦+J
M
(g), whereJ
M
(g) is the seminorm consisting of the sum of the squaredL
2 norms of theMth partial derivatives ofg. Such an LAD smoothing spline,s
, would be expected to give robust smoothed estimates off in situations where the
i
are from a distribution with heavy tails. The solution to such a problem is a thin plate spline of known form. An algorithm for computings
is given which is based on considering a sequence of quadratic programming problems whose structure is guided by the optimality conditions for the above convex minimization problem, and which are solved readily, if a good initial point is available. The data driven selection of the smoothing parameter is achieved by minimizing aCV() score of the form
.The combined LAD-CV smoothing spline algorithm is a continuation scheme in 0 taken on the above SQPs parametrized in, with the optimal smoothing parameter taken to be that value of at which theCV() score first begins to increase. The feasibility of constructing the LAD-CV smoothing spline is illustrated by an application to a problem in environment data interpretation. 相似文献
20.
The problem of minimizing a nondifferential functionx f(x) (subject, possibly, to nondifferential constraints) is considered. Conventional algorithms are employed for minimizing a differential approximationf
off (subject to differentiable approximations ofg). The parameter is adaptively reduced in such a way as to ensure convergence to points satisfying necessary conditions of optimality for the original problem.This research was supported by the UK Science and Engineering Research Council, the National Science Foundation under Grant No. ECS-8121149, and the Joint Services Electronics Program, Contract No. F49620-79-C-0178. 相似文献
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