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排序方式: 共有194条查询结果,搜索用时 31 毫秒
51.
The purpose of this note is to gain insight on the performance of two well known operational Ridge Regression estimators by deriving the moments of their stochastic shrinkage parameters. We also show that, under certain conditions, one of them has bounded moments. 相似文献
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53.
This paper compares several Stein-like estimation methods for estimating regression parameters. The criterion function was the mean-squared error of prediction and the parameter of interest was the mean of the response variable at the sampled values of the control variables. Large sample simulation techniques were used to evaluate the mean-squared error of the predictions. The parameters of interest were varied systematically over wide ranges. 相似文献
54.
In discrete event simulation, the method of control variates is often used to reduce the variance of estimation for the mean of the output response. In the present paper, it is shown that when three or more control variates are used, the usual linear regression estimator of the mean response is one of a large class of unbiased estimators, many of which have smaller variance than the usual estimator. In simulation studies using control variates, a confidence interval for the mean response is typically reported as well. Intervals with shorter width have been proposed using control variates in the literature. The present paper however develops confidence intervals which not only have shorter width but also have higher coverage probability than the usual confidence interval 相似文献
55.
The general procedure of two stage shrinkage testimation formulated in Adlce and Gokhale [Commun. Statist.- Theory Meth. 18, 633-627 (1989)] k further generalized and extended to the multiparameter case. Local optimal-ity result of that paper, in the restricted set up of univariate location families and scalar families, is generalized without any restriction on the parametric family. The local unbiasedness result of that paper is also generalized and in addition local risk- unbiasedness is considered. The optimality and risk-unbiasedness results are proved for the usual matrix loss and their validity for an arbitrary quadratic loss deduced as corollaries. 相似文献
56.
《Journal of Statistical Computation and Simulation》2012,82(7):1412-1426
In the multinomial regression model, we consider the methodology for simultaneous model selection and parameter estimation by using the shrinkage and LASSO (least absolute shrinkage and selection operation) [R. Tibshirani, Regression shrinkage and selection via the LASSO, J. R. Statist. Soc. Ser. B 58 (1996), pp. 267–288] strategies. The shrinkage estimators (SEs) provide significant improvement over their classical counterparts in the case where some of the predictors may or may not be active for the response of interest. The asymptotic properties of the SEs are developed using the notion of asymptotic distributional risk. We then compare the relative performance of the LASSO estimator with two SEs in terms of simulated relative efficiency. A simulation study shows that the shrinkage and LASSO estimators dominate the full model estimator. Further, both SEs perform better than the LASSO estimators when there are many inactive predictors in the model. A real-life data set is used to illustrate the suggested shrinkage and LASSO estimators. 相似文献
57.
In the problem of selecting the best of k populations, Olkin, Sobel, and Tong (1976) have introduced the idea of estimating the probability of correct selection. In an attempt to improve on their estimator we consider anempirical Bayes approach. We compare the two estimators via analytic results and a simulation study. 相似文献
58.
《Journal of Statistical Computation and Simulation》2012,82(10):1501-1516
In this study, we consider the application of the James–Stein estimator for population means from a class of arbitrary populations based on ranked set sample (RSS). We consider a basis for optimally combining sample information from several data sources. We succinctly develop the asymptotic theory of simultaneous estimation of several means for differing replications based on the well-defined shrinkage principle. We showcase that a shrinkage-type estimator will have, under quadratic loss, a substantial risk reduction relative to the classical estimator based on simple random sample and RSS. Asymptotic distributional quadratic biases and risks of the shrinkage estimators are derived and compared with those of the classical estimator. A simulation study is used to support the asymptotic result. An over-riding theme of this study is that the shrinkage estimation method provides a powerful extension of its traditional counterpart for non-normal populations. Finally, we will use a real data set to illustrate the computation of the proposed estimators. 相似文献
59.
In Hong Chang 《Statistics》2015,49(5):1095-1103
With a view to predicting a scalar-valued future observation on the basis of past observations, we explore predictive sets having frequentist as well as Bayesian validity for arbitrary priors in a higher-order asymptotic sense. It is found that a connection with locally unbiased tests is useful for this purpose. Illustrative examples are given. Computation and simulation studies lend support to our asymptotic results in finite samples. The issue of expected lengths of our predictive sets is also discussed. 相似文献
60.