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排序方式: 共有314条查询结果,搜索用时 31 毫秒
151.
In a model for rounded data suppose that the random sample X1,.,.,Xn,. i.i.d., is transformed into an observed random sample X1Δ,.,.,XnΔ, where XiΔ = 2vΔ if Xi, ∈ (2vΔ - Δ, 2vΔ + Δ), for i = 1,.,.,n. We show that the precision Δ of the observations has an important effect on the shape of the kernel density estimator, and we identify important points for the graphical display of this estimator. We examine the IMSE criteria to find the optimal window under the rounded-data model. 相似文献
152.
通过对桃红四物汤加减治疗母畜产后瘀血证的疗效观察,以及桃红四物汤煎剂对家兔血细胞免疫功能和对小白鼠腹腔巨噬细胞吞噬作用的影响的实验,表明桃红四物汤加减对母畜产后瘀血证疗效确实,并能增强动物机体的免疫功能。 相似文献
153.
Belkacem Abdous 《Revue canadienne de statistique》1995,23(1):21-27
Classes of higher-order kernels for estimation of a probability density are constructed by iterating the twicing procedure. Given a kernel K of order l, we build a family of kernels Km of orders l(m + 1) with the attractive property that their Fourier transforms are simply 1 — {1 —$(.)}m+1, where ? is the Fourier transform of K. These families of higher-order kernels are well suited when the fast Fourier transform is used to speed up the calculation of the kernel estimate or the least-squares cross-validation procedure for selection of the window width. We also compare the theoretical performance of the optimal polynomial-based kernels with that of the iterative twicing kernels constructed from some popular second-order kernels. 相似文献
154.
Young Kyung Lee 《Journal of the Korean Statistical Society》2013,42(4):565-571
In this note we discuss two-step kernel estimation of varying coefficient regression models that have a common smoothing variable. The method allows one to use different bandwidths for different coefficient functions. We consider local polynomial fitting and present explicit formulas for the asymptotic biases and variances of the estimators. 相似文献
155.
Juan M. Vilar-Fernández 《统计学通讯:模拟与计算》2013,42(2):311-334
The problem of predicting a future value of a time series is considered in this article. If the series follows a stationary Markov process, this can be done by nonparametric estimation of the autoregression function. Two forecasting algorithms are introduced. They only differ in the nonparametric kernel-type estimator used: the Nadaraya-Watson estimator and the local linear estimator. There are three major issues in the implementation of these algorithms: selection of the autoregressor variables, smoothing parameter selection, and computing prediction intervals. These have been tackled using recent techniques borrowed from the nonparametric regression estimation literature under dependence. The performance of these nonparametric algorithms has been studied by applying them to a collection of 43 well-known time series. Their results have been compared to those obtained using classical Box-Jenkins methods. Finally, the practical behavior of the methods is also illustrated by a detailed analysis of two data sets. 相似文献
156.
A method is proposed for shape-constrained density estimation under a variety of constraints, including but not limited to unimodality, monotonicity, symmetry, and constraints on the number of inflection points of the density or its derivative. The method involves computing an adjustment curve that is used to bring a pre-existing pilot estimate into conformance with the specified shape restrictions. The pilot estimate may be obtained using any preferred estimator, and the optimal adjustment can be computed using fast, readily-available quadratic programming routines. This makes the proposed procedure generic and easy to implement. 相似文献
157.
In this article, we study the asymptotic properties of the kernel estimator of the mode and density function when the data are twice censored. More specifically, we first establish a strong uniform consistency over a compact set with a rate of the kernel density estimator and then we give the consistency with rate and asymptotic normality for the kernel mode estimator. An application to confidence bands is given. 相似文献
158.
Frédéric Ferraty Ali Laksaci Amel Tadj Philippe Vieu 《Journal of statistical planning and inference》2010
In this paper we investigate nonparametric estimation of some functionals of the conditional distribution of a scalar response variable Y given a random variable X taking values in a semi-metric space. These functionals include the regression function, the conditional cumulative distribution, the conditional density and some other ones. The literature on nonparametric functional statistics is only concerning pointwise consistency results, and our main aim is to prove the uniform almost complete convergence (with rate) of the kernel estimators of these nonparametric models. Unlike in standard multivariate cases, the gap between pointwise and uniform results is not immediate. So, suitable topological considerations are needed, implying changes in the rates of convergence which are quantified by entropy considerations. These theoretical uniform consistency results are (or will be) key tools for many further developments in functional data analysis. 相似文献
159.
In this paper, we propose two new tests to test the symmetry of a distribution. These tests are built up on the asymptotic normality of the L1-distance to the symmetry of the Kernel and histogram density estimates. A simulation study is carried out to evaluate performances of the kernel based test. 相似文献
160.
S. Rao Jammalamadaka 《统计学通讯:模拟与计算》2013,42(9):2791-2811
ABSTRACTThis paper considers the problem of estimating the autoregressive parameter in discretely observed Ornstein–Uhlenbeck processes. Two consistent estimators are proposed: one obtained by maximizing a kernel-based likelihood function, and another by minimizing a Kolmogorov-type distance from independence. After establishing the consistency of these estimators, their finite-sample performance and possible normality in large samples, is investigated by means of extensive simulations. An illustrative example to credit rating is discussed. 相似文献