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991.
准确的产量预测是油田勘探开发的重要研究任务,而常规的预测方法无法应用于开发初期。同一勘探区块,试油层产量与各影响因素存在一定的相关性。首先建立了产量与各静、动态参数的一元非线性回归模型,然后将其作为新的自变量,进行多元线性回归,最后获得了预测试油层产量的非线性回归模型。现场实际应用表明,建立的多元非线性模型比线性模型预测精度更高,适用性更好。为该勘探区块试油层措施改造决策提供了科学的指导,提高了试油经济效益。 相似文献
992.
提高企业绿色创新水平是实现高质量发展的重要途径。基于2010—2020年沪深A股上市公司数据,实证检验机构投资者退出威胁对企业绿色创新的影响。研究发现,机构投资者退出威胁能够推动企业绿色创新;而且,以机构投资者持股比例为门槛变量,机构投资者退出威胁对企业绿色创新的影响存在单门槛效应,当机构投资者持股比例超过门槛值后,退出威胁的正向作用得以显现。进一步的路径分析表明,机构投资者退出威胁能够促使企业加大研发投入、积极履行环境责任。此外,在机构投资者为抗压型、企业为非国有性质或重污染行业的情况下,机构投资者退出威胁的绿色创新赋能效应更显著。 相似文献
993.
The wild bootstrap is a nonparametric tool that can be used to estimate a sampling distribution in the presence of heteroscedastic errors. In particular, the wild bootstrap enables us to compute confidence regions for regression parameters under non-i.i.d. models. While the wild bootstrap may perform well in these settings, its obvious drawback is a lack of computational efficiency. The wild bootstrap requires a large number of bootstrap replications, making the use of this tool impractical when dealing with big data. We introduce the analytic wild bootstrap (ANWB), which provides a nonparametric alternative way of constructing confidence regions for regression parameters. The ANWB is superior to the wild bootstrap from a computational standpoint while exhibiting similar finite-sample performance. We report simulation results for both least squares and ridge regression. Additionally, we test the ANWB on a real dataset and compare its performance with that of other standard approaches. 相似文献
994.
Data are generated at an unprecedented rate and scale these days across many disciplines. The field of streaming data analysis has emerged as a result of new data collection and storage technologies in various areas, such as air pollution monitoring, detection of traffic congestion, disease surveillance, and recommendation systems. In this paper, we consider the problem of model estimation for data streams in reproducing kernel Hilbert spaces. We propose an adaptive supervised learning method with a data sparsity constraint that uses limited storage spaces and can handle nonstationary models. We demonstrate the competitive performance of the proposed method using simulations and analysis of the bike sharing dataset. 相似文献
995.
Raymond L. Chambers Enrico Fabrizi Maria Giovanna Ranalli Nicola Salvati Suojin Wang 《Wiley Interdisciplinary Reviews: Computational Statistics》2023,15(2):e1596
There is growing interest in a data integration approach to survey sampling, particularly where population registers are linked for sampling and subsequent analysis. The reason for doing this is simple: it is only by linking the same individuals in the different sources that it becomes possible to create a data set suitable for analysis. But data linkage is not error free. Many linkages are nondeterministic, based on how likely a linking decision corresponds to a correct match, that is, it brings together the same individual in all sources. High quality linking will ensure that the probability of this happening is high. Analysis of the linked data should take account of this additional source of error when this is not the case. This is especially true for secondary analysis carried out without access to the linking information, that is, the often confidential data that agencies use in their record matching. We describe an inferential framework that allows for linkage errors when sampling from linked registers. After first reviewing current research activity in this area, we focus on secondary analysis and linear regression modeling, including the important special case of estimation of subpopulation and small area means. In doing so we consider both robustness and efficiency of the resulting linked data inferences. This article is categorized under:
- Algorithms and Computational Methods > Maximum Likelihood Methods
- Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods
- Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
996.
Based on the theories of sliced inverse regression (SIR) and reproducing kernel Hilbert space (RKHS), a new approach RDSIR (RKHS-based Double SIR) to nonlinear dimension reduction for survival data is proposed. An isometric isomorphism is constructed based on the RKHS property, then the nonlinear function in the RKHS can be represented by the inner product of two elements that reside in the isomorphic feature space. Due to the censorship of survival data, double slicing is used to estimate the weight function to adjust for the censoring bias. The nonlinear sufficient dimension reduction (SDR) subspace is estimated by a generalized eigen-decomposition problem. The asymptotic property of the estimator is established based on the perturbation theory. Finally, the performance of RDSIR is illustrated on simulated and real data. The numerical results show that RDSIR is comparable with the linear SDR method. Most importantly, RDSIR can also effectively extract nonlinearity from survival data. 相似文献
997.
It is known that linear regression models have immense applications in various areas such as engineering technology, economics and social sciences. In this paper, we investigate the asymptotic properties of M-estimator in multivariate linear regression model based on a class of random errors satisfying a generalised Bernstein-type inequality. By using the generalised Bernstein-type inequality, we obtain a general result on almost sure convergence for a class of random variables and then obtain the strong consistency for the M-estimator in multivariate linear regression models under some mild conditions. The result extends or improves some existing ones in the literature. Moreover, we also consider the case when the dimension $p$ tends to infinity by establishing the rate of almost sure convergence for a class of random variables satisfying generalised Bernstein-type inequality. Some numerical simulations are also provided to verify the validity of the theoretical results. 相似文献
998.
Cornelius Rosenbaum Qingzhao Yu Sarah Buzhardt Elizabeth Sutton Andrew G. Chapple 《Pharmaceutical statistics》2023,22(6):995-1015
We present a simulation study and application that shows inclusion of binary proxy variables related to binary unmeasured confounders improves the estimate of a related treatment effect in binary logistic regression. The simulation study included 60,000 randomly generated parameter scenarios of sample size 10,000 across six different simulation structures. We assessed bias by comparing the probability of finding the expected treatment effect relative to the modeled treatment effect with and without the proxy variable. Inclusion of a proxy variable in the logistic regression model significantly reduced the bias of the treatment or exposure effect when compared to logistic regression without the proxy variable. Including proxy variables in the logistic regression model improves the estimation of the treatment effect at weak, moderate, and strong association with unmeasured confounders and the outcome, treatment, or proxy variables. Comparative advantages held for weakly and strongly collapsible situations, as the number of unmeasured confounders increased, and as the number of proxy variables adjusted for increased. 相似文献
999.
王杰 《重庆邮电大学学报(社会科学版)》2023,35(6):107-118
农民工返乡创业有利于农民工承担教育和陪伴子女、赡养老人等因长期外出务工而缺失的家庭责任,促进了城乡融合,改善了农村的产业结构,带动了现代农业、旅游业以及三产融合发展,做实了乡村人才振兴,有效推动了乡村振兴战略的实施。以安徽省1 069名返乡农民工为研究对象,首先构建二元Logistic回归模型探讨人口学特征、务工经历、日常生活、发展规划四个方面对农民工返乡创业意愿的影响,并在此基础上通过ISM模型揭示了影响农民工返乡创业意愿的表层、中层、深层因素,最后基于这三层视角提出相应的政策建议。研究发现：代际、受教育程度、务工类型、收入增长情况、务工生活满意度、理想婚姻对象户籍来源、未来定居地点规划显著影响农民工返乡创业意愿;受教育程度、务工类型是农民工返乡创业意愿中的深层根源因素;收入增长情况、代际、理想婚姻对象户籍来源是影响农民工返乡创业意愿的中层间接因素;务工生活满意度、未来定居地点规划直接关系到农民工返乡创业意愿。因此,建议增强返乡创业农民工的创业学习意识,提高返乡创业技能培训的质量;加强对新生代农民工的工作生活调研,把握其需求特点;加强创业政策支持,提升流出地在公共服务、营商环境等方面的质量。 相似文献
1000.
In this paper, we investigate robust parameter estimation and variable selection for binary regression models with grouped data. We investigate estimation procedures based on the minimum-distance approach. In particular, we employ minimum Hellinger and minimum symmetric chi-squared distances criteria and propose regularized minimum-distance estimators. These estimators appear to possess a certain degree of automatic robustness against model misspecification and/or for potential outliers. We show that the proposed non-penalized and penalized minimum-distance estimators are efficient under the model and simultaneously have excellent robustness properties. We study their asymptotic properties such as consistency, asymptotic normality and oracle properties. Using Monte Carlo studies, we examine the small-sample and robustness properties of the proposed estimators and compare them with traditional likelihood estimators. We also study two real-data applications to illustrate our methods. The numerical studies indicate the satisfactory finite-sample performance of our procedures. 相似文献