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1.
Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports. 相似文献
2.
汪青松 《郑州大学学报(哲学社会科学版)》2002,35(1):66-70
马克思主义经典作家的相关阐述 ,社会主义本质的实现和比较优势的获取 ,精神富裕和物质富裕特殊的关系均表明 ,精神富裕是建设社会主义的应有之义。而精神富裕的导向、认识、实践和凝聚作用 ,又反过来在促进社会主义事业快速、健康与持续发展中扮演着重要角色。同时 ,社会主义精神富裕还具有向度的特性 ,是一个动态开放的发展过程 相似文献
3.
宣满友 《绍兴文理学院学报》2002,22(9):20-24
讨论三维Minkowski空间R2 .1中一类特殊曲面在主曲率函数相等时的情形 ,给出了这类曲面在这一情形下的位置向量场 相似文献
4.
Gianfranco Lovison 《Statistical Papers》2005,46(4):555-574
The identity of the Rao score and PearsonX
2 statistics is well known in the areas where the latter was first introduced: goodness-of-fit in contingency tables and binary
responses. We show in this paper that the same identity holds when the two statistics are used for testing goodness-of-fit
of Generalized Linear Models. We also highlight the connections that exist between the two statistics when they are used for
the comparison of nested models. Finally, we discuss some merits of these unifying results.
Work financially supported by cofin. MIUR grants 2000 and 2002. 相似文献
5.
B股与H股及红筹股之间的溢出效应与信息流动 总被引:1,自引:0,他引:1
运用向量GARCH-M模型检验了B股与H股及红筹股之间的溢出效应与信息流动.实证结果表明:红筹股对沪深市B股、H股对沪市B股的收益和波动溢出效应均显著存在,而反向的溢出效应均不显著,表明信息是从红筹股向沪深市B股、从H股向沪市B股单向流动的;深市B股与H股之间相互的收益溢出效应均存在,而波动溢出效应均不存在,信息在H股和深市B股之间的流动情况不明显;在信息流动过程中,红筹股始终处于信息领先地位. 相似文献
6.
The Wald statistic is known to vary under reparameterization. This raises the question: which parameterization should be chosen, in order to optimize power of the Wald statistic? We specifically consider k-sample tests of generalized linear models (GLMs) and generalized estimating equations (GEEs) in which the alternative hypothesis contains only two parameters. An example is presented in which such an alternative hypothesis is of interest. Amongst a general class of parameterizations, we find the parameterization that maximizes power via analysis of the non-centrality parameter, and show how the effect on power of reparameterization depends on sampling design and the differences in variance across samples. There is no single parameterization with optimal power across all alternatives. The Wald statistic commonly used under the canonical parameterization is optimal in some instances but it performs very poorly in others. We demonstrate results by example and by simulation, and describe their implications for likelihood ratio statistics and score statistics. We conclude that due to poor power properties, the routine use of score statistics and Wald statistics under the canonical parameterization for GEEs is a questionable practice. 相似文献
7.
8.
国家财政对农业的投入是影响农业发展的重要因素。收集了建国以来国家财政对农业投入的数据,分析构造出状态空间模型,应用卡尔曼滤波估计出状态向量,挖掘出隐藏在数据内部的变化特征,分析国家财政对农业投入的实际情况,并对未来几年的农业投入进行预测。 相似文献
9.
在实现"碳达峰碳中和"目标的重要历史机遇期,推动实现农业集约化、绿色化和资源再生化,构建资源节约型、绿色低碳型、生态循环型的农业发展模式,对于农业可持续发展具有重要意义。以2000-2018年我国31个省份为研究对象,通过面板向量自回归模型(PVAR),刻画资源消耗、农业产出、农业碳排放之间的动态传导机制。结果表明:(1)东部农业"碳达峰"节点出现较早,东北、西部相对较晚;2018年农业碳排放居前三的省份为湖南、黑龙江、河南,居后三的为北京、天津、上海;(2)资源消耗和农业产出之间呈现"低投入高产出"的农业集约化生产模式,但影响力微弱;农业产出和农业碳排放之间"高产出高排放"的情形长期面临农业绿色化的严峻考验;农业碳排放与资源消耗之间呈现"低排放低投入"的资源再生化现象,影响力短期微弱长期较强;(3)推动实现农业集约化、绿色化、资源再生化的关键在于探索降低农业碳排放的多重路径。因此,应强化农业集约化生产,充分发展资源再生化模式,探索形成农业绿色化格局新路径,以此来加快实现碳减排目标,推动农业可持续发展。 相似文献
10.
Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising. 相似文献