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991.
    
We consider model selection for linear mixed-effects models with clustered structure, where conditional Kullback–Leibler (CKL) loss is applied to measure the efficiency of the selection. We estimate the CKL loss by substituting the empirical best linear unbiased predictors (EBLUPs) into random effects with model parameters estimated by maximum likelihood. Although the BLUP approach is commonly used in predicting random effects and future observations, selecting random effects to achieve asymptotic loss efficiency concerning CKL loss is challenging and has not been well studied. In this paper, we propose addressing this difficulty using a conditional generalized information criterion (CGIC) with two tuning parameters. We further consider a challenging but practically relevant situation where the number, m $$ m $$ , of clusters does not go to infinity with the sample size. Hence the random-effects variances are not consistently estimable. We show that via a novel decomposition of the CKL risk, the CGIC achieves consistency and asymptotic loss efficiency, whether m $$ m $$ is fixed or increases to infinity with the sample size. We also conduct numerical experiments to illustrate the theoretical findings.  相似文献   
992.
    
Missing data analysis requires assumptions about an outcome model or a response probability model to adjust for potential bias due to nonresponse. Doubly robust (DR) estimators are consistent if at least one of the models is correctly specified. Multiply robust (MR) estimators extend DR estimators by allowing for multiple models for both the outcome and/or response probability models and are consistent if at least one of the multiple models is correctly specified. We propose a robust quasi-randomization-based model approach to bring more protection against model misspecification than the existing DR and MR estimators, where any multiple semiparametric, nonparametric or machine learning models can be used for the outcome variable. The proposed estimator achieves unbiasedness by using a subsampling Rao–Blackwell method, given cell-homogenous response, regardless of any working models for the outcome. An unbiased variance estimation formula is proposed, which does not use any replicate jackknife or bootstrap methods. A simulation study shows that our proposed method outperforms the existing multiply robust estimators.  相似文献   
993.
为了应对在线定制订单在数量、质量、交货期等方面的需求以及生产线各阶段产出的随机性,生产商可基于阿米巴经营模式设计车间管理机制。将生产线各工序划分为n个相对独立的阿米巴组织,相邻阿米巴进行定价和交易。考虑承诺交货期对产出合格率及总成本的影响,分散决策下,基于产出不足惩罚机制构建包含计划投产量及承诺交货期决策的斯坦伯格博弈模型。集中决策下,基于资源共享机制构建包含n个决策变量的非线性优化模型,通过计算n阶HESSE矩阵的k阶主子式证明最优解的存在性及唯一性。基于惩罚成本及现货市场采购成本共担机制实现针对各个阿米巴的绩效分配及激励。在算例分析的基础主要得出如下结论:随着各阶段投产量的增大,各阿米巴及整体期望利润先上升后下降;给予客户的延期交货价格折扣越大,对于前n-1个阿米巴可能越有利,但对于最后一个阿米巴及整体可能越不利;合格率的波动性越大,各阿米巴及整体期望利润越低。基于阿米巴经营的车间管理机制设计、产出合格率影响因素的考虑、n级供应链的博弈协调等具有理论和实践价值。  相似文献   
994.
    
Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple times and the resulting estimates of the parameter(s) of interest are combined across the completed datasets, is becoming increasingly popular for handling missing data. However, MI can result in biased inference if not carried out appropriately or if the underlying assumptions are not justifiable. Despite this, there remains a scarcity of guidelines for carrying out MI. In this paper we provide a tutorial on the main issues involved in employing MI, as well as highlighting some common pitfalls and misconceptions, and areas requiring further development. When contemplating using MI we must first consider whether it is likely to offer gains (reduced bias or increased precision) over alternative methods of analysis. Once it has been decided to use MI, there are a number of decisions that must be made during the imputation process; we discuss the extent to which these decisions can be guided by the current literature. Finally we highlight the importance of checking the fit of the imputation model. This process is illustrated using a case study in which we impute missing outcome data in a five-wave longitudinal study that compared extremely preterm individuals with term-born controls.  相似文献   
995.
物流配送中心选址的随机数学模型   总被引:23,自引:5,他引:23       下载免费PDF全文
本文对照传统的物流配送中心选址问题提出了一个随机化的模型,并从数学角度对该模型进行了的一些分析,给出了单配送中心选址问题的一个量化的处理方法。  相似文献   
996.
    
The on‐farm research network concept enables a group of farmers to test new agricultural management practices under local conditions with support from local researchers or agronomists. Different on‐farm trials based on the same experimental design are conducted over several years and sites to test the effectiveness of different innovative management practices aimed at increasing crop productivity and profitability. As a larger amount of historical trial data are being accumulated, data of all the trials require analyses and summarization. Summaries of on‐farm trials are usually presented to farmers as individual field reports, which are not optimal for the dissemination of results and decision making. A more practical communication method is needed to enhance result communication and decision making. R Shiny is a new rapidly developing technology for turning R data analyses into interactive web applications. For the first time for on‐farm research networks, we developed and launched an interactive web tool called ISOFAST using R Shiny. ISOFAST simultaneously reports all trial results about the same management practice to simplify interpretation of multi‐site and multi‐year summaries. We used a random‐effects model to synthetize treatment differences at both the individual trial and network levels and generate new knowledge for farmers and agronomists. The friendly interface enables users to explore trial summaries, access model outputs, and perform economic analysis at their fingertips. This paper describes a case‐study to illustrate how to use the tool and make agronomic management decisions based on the on‐farm trial data. We also provided technical details and guidance for developing a similar interactive visualization tool customized for on‐farm research network. ISOFAST is currently available at https://analytics.iasoybeans.com/cool-apps/ISOFAST/ .  相似文献   
997.
Abstract

Over the last 80?years there has been much interest in the problem of finding an explicit formula for the probability density function of two zero mean correlated normal random variables. Motivated by this historical interest, we use a recent technique from the Stein’s method literature to obtain a simple new proof, which also serves as an exposition of a general method that may be useful in related problems.  相似文献   
998.
999.
The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.  相似文献   
1000.
A group’s resilience is often linked to its network structure. While decentralized network properties have been associated with resilience at the group-level, little is known about the individual-level factors that lead groups to adopt these structures. Criminal groups, consistently faced with unexpected external disruptions, provide an opportunity to examine individual decisions to collaborate across periods of increased risk. Using data on 118 terrorist offenders across six attacks we test whether individual decisions to collaborate are influenced by variation in law enforcement activity. Results from exponential random graph models demonstrate that many of the processes that drive collaboration between offenders differ when faced with greater risk. Connectivity was maximized in periods of decreased enforcement activity, with offenders more likely to collaborate in dense, local triads. Following an increase in interdictions, triad closure had no impact on co-offending.  相似文献   
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