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861.
Consider estimation of a population mean of a response variable when the observations are missing at random with respect to
the covariate. Two common approaches to imputing the missing values are the nonparametric regression weighting method and
the Horvitz-Thompson (HT) inverse weighting approach. The regression approach includes the kernel regression imputation and
the nearest neighbor imputation. The HT approach, employing inverse kernel-estimated weights, includes the basic estimator,
the ratio estimator and the estimator using inverse kernel-weighted residuals. Asymptotic normality of the nearest neighbor
imputation estimators is derived and compared to kernel regression imputation estimator under standard regularity conditions
of the regression function and the missing pattern function. A comprehensive simulation study shows that the basic HT estimator
is most sensitive to discontinuity in the missing data patterns, and the nearest neighbors estimators can be insensitive to
missing data patterns unbalanced with respect to the distribution of the covariate. Empirical studies show that the nearest
neighbor imputation method is most effective among these imputation methods for estimating a finite population mean and for
classifying the species of the iris flower data. 相似文献
862.
This paper introduces and applies an EM algorithm for the maximum-likelihood estimation of a latent class version of the grouped-data regression model. This new model is applied to examine the effects of college athletic participation of females on incomes. No evidence for an “athlete” effect in the case of females has been found in the previous work by Long and Caudill [12], Henderson et al. [10], and Caudill and Long [5]. Our study is the first to find evidence of a lower wage for female athletes. This effect is present in a regime characterizing 42% of the sample. Further analysis indicates that female athletes in many otherwise low-paying jobs actually get paid less than non-athletes. 相似文献
863.
Edwin M.M. Ortega Gauss M. Cordeiro Michael W. Kattan 《Journal of applied statistics》2012,39(6):1191-1210
In this article, for the first time, we propose the negative binomial–beta Weibull (BW) regression model for studying the recurrence of prostate cancer and to predict the cure fraction for patients with clinically localized prostate cancer treated by open radical prostatectomy. The cure model considers that a fraction of the survivors are cured of the disease. The survival function for the population of patients can be modeled by a cure parametric model using the BW distribution. We derive an explicit expansion for the moments of the recurrence time distribution for the uncured individuals. The proposed distribution can be used to model survival data when the hazard rate function is increasing, decreasing, unimodal and bathtub shaped. Another advantage is that the proposed model includes as special sub-models some of the well-known cure rate models discussed in the literature. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We analyze a real data set for localized prostate cancer patients after open radical prostatectomy. 相似文献
864.
Thall PF Nguyen HQ Wang X Wolff JE 《Journal of statistical planning and inference》2012,142(4):944-955
The problem of comparing several experimental treatments to a standard arises frequently in medical research. Various multi-stage randomized phase II/III designs have been proposed that select one or more promising experimental treatments and compare them to the standard while controlling overall Type I and Type II error rates. This paper addresses phase II/III settings where the joint goals are to increase the average time to treatment failure and control the probability of toxicity while accounting for patient heterogeneity. We are motivated by the desire to construct a feasible design for a trial of four chemotherapy combinations for treating a family of rare pediatric brain tumors. We present a hybrid two-stage design based on two-dimensional treatment effect parameters. A targeted parameter set is constructed from elicited parameter pairs considered to be equally desirable. Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets. Decisions at each stage of the trial are based on the ratio of posterior probabilities of the alternative and null covariate-adjusted parameter sets. Design parameters are chosen to minimize expected sample size subject to frequentist error constraints. The design is illustrated by application to the brain tumor trial. 相似文献
865.
P. AdamsD.E. Bryant A.D. ForbesT.S. Griggs 《Journal of statistical planning and inference》2012,142(5):1040-1046
We prove that the complete graph Kv can be decomposed into dodecahedra if and only if v≡ 1, 16, 25 or 40 (mod 60), v≠16. 相似文献
866.
In this work, we develop modeling and estimation approach for the analysis of cross-sectional clustered data with multimodal conditional distributions where the main interest is in analysis of subpopulations. It is proposed to model such data in a hierarchical model with conditional distributions viewed as finite mixtures of normal components. With a large number of observations in the lowest level clusters, a two-stage estimation approach is used. In the first stage, the normal mixture parameters in each lowest level cluster are estimated using robust methods. Robust alternatives to the maximum likelihood estimation are used to provide stable results even for data with conditional distributions such that their components may not quite meet normality assumptions. Then the lowest level cluster-specific means and standard deviations are modeled in a mixed effects model in the second stage. A small simulation study was conducted to compare performance of finite normal mixture population parameter estimates based on robust and maximum likelihood estimation in stage 1. The proposed modeling approach is illustrated through the analysis of mice tendon fibril diameters data. Analyses results address genotype differences between corresponding components in the mixtures and demonstrate advantages of robust estimation in stage 1. 相似文献
867.
868.
We investigate the properties of several statistical tests for comparing treatment groups with respect to multivariate survival data, based on the marginal analysis approach introduced by Wei, Lin and Weissfeld [Regression Analysis of multivariate incomplete failure time data by modelling marginal distributians, JASA vol. 84 pp. 1065–1073]. We consider two types of directional tests, based on a constrained maximization and on linear combinations of the unconstrained maximizer of the working likelihood function, and the omnibus test arising from the same working likelihood. The directional tests are members of a larger class of tests, from which an asymptotically optimal test can be found. We compare the asymptotic powers of the tests under general contiguous alternatives for a variety of settings, and also consider the choice of the number of survival times to include in the multivariate outcome. We illustrate the results with simulations and with the results from a clinical trial examining recurring opportunistic infections in persons with HIV. 相似文献
869.
Recurrent events are frequently observed in biomedical studies, and often more than one type of event is of interest. Follow-up time may be censored due to loss to follow-up or administrative censoring. We propose a class of semi-parametric marginal means/rates models, with a general relative risk form, for assessing the effect of covariates on the censored event processes of interest. We formulate estimating equations for the model parameters, and examine asymptotic properties of the parameter estimators. Finite sample properties of the regression coefficients are examined through simulations. The proposed methods are applied to a retrospective cohort study of risk factors for preschool asthma. 相似文献
870.
S. Landau I. C. Ellison-Wright E. T. Bullmore 《Journal of the Royal Statistical Society. Series C, Applied statistics》2004,53(1):63-82
Summary. Functional magnetic resonance imaging (FMRI) measures the physiological response of the human brain to experimentally controlled stimulation. In a periodically designed experiment it is of interest to test for a difference in the timing (phase shift) of the response between two anatomically distinct brain regions. We suggest two tests for an interregional difference in phase shift: one based on asymptotic theory and one based on bootstrapping. Whilst the two procedures differ in some of their assumptions, both tests rely on employing the large number of voxels (three-dimensional pixels) in non-activated brain regions to take account of spatial autocorrelation between voxelwise phase shift observations within the activated regions of interest. As an example we apply both tests, and their counterparts assuming spatial independence, to FMRI phase shift data that were acquired from a normal young woman during performance of a periodically designed covert verbal fluency task. We conclude that it is necessary to take account of spatial autocovariance between voxelwise FMRI time series parameter estimates such as the phase shift, and that the most promising way of achieving this is by modelling the spatial autocorrelation structure from a suitably defined base region of the image slice. 相似文献