纵向数据

  • 网络longitudinal data
纵向数据纵向数据
  1. 含缺失数据的两值马氏链纵向数据的EM算法

    EM Algorithm for Binary Markov Chains of Longitudinal Data with Missing Data

  2. 纵向数据下部分线性EV模型的渐近性质

    Asymptotic Properties for the Partially Linear EV Models under Longitudinal Data

  3. 基于连续可分的广义非线性纵向数据模型偏离名义离差的score检验及其功效

    Score Tests and Powers for Departures from Nominal Dispersion in Separable and Continuous Generalized Nonlinear Models with Longitudinal Data

  4. 多重填补法MarkovchainMonteCarlo模型在有缺失值的妇幼卫生纵向数据中的应用

    Markov Chain Monte Carlo Method of Multiple Imputation for Longitudinal Data with Missing Values in the Survey of Maternal and Children Health

  5. 目的:采用多重填补法(multipleimputation,MI)和adhoc法分别对模拟的纵向数据集中的缺失值进行处理,比较两种方法的优劣并探讨其适用性。

    Objective : To explore the applicability of multiple imputation ( MI ) and Ad hoc methods in simulated data with missing values .

  6. 边际回归模型和与此有关的广义估计方程(GEE)在纵向数据分析中得到了广泛的应用。

    Marginal regression model and its associated generalized estimating equation ( GEE ) are becoming increasingly being used in longitudinal studies .

  7. 目前很少有关于颈动脉斑块的发生在CIMT中的预测作用的纵向数据。

    There exist few longitudinal data on the predictive role of CIMT in the occurrence of carotid plaque .

  8. 该算法使用了一种新颖的逻辑纵向数据划分方法来确保top-k频繁词集挖掘能够在各数据分区中并行执行。

    A novel logical vertical data partitioning method is used to make sure the top-k frequent term sets can be mined parallel at each mining node .

  9. 本文正是基于协方差结构的参数模型,用REML估计的方法对方差参数进行估计,分别研究了纵向数据线性回归模型均值参数和方差参数的假设检验。

    Just based on parametric models for covariance structure , estimating covariance parameters by means of REML estimation , this paper studies the hypothesis tests of mean parameters and covariance parameters in the longitudinal linear models .

  10. 纵向数据非参数混合效应模型的一个局部不变估计

    A local constant estimator for nonparametric mixed-effects models with longitudinal data

  11. 混合效应模型是研究纵向数据的一个常用工具。

    Mixed effects model is a popular tool for longitudinal data .

  12. 纵向数据模型均值参数和方差参数的影响分析

    Influence Analysis for Mean and Covariance Parameters in Longitudinal Data Model

  13. 纵向数据分析方法

    A Review on Longitudinal Data Analysis Method and It 's Development

  14. 纵向数据中线性混合模型的估计与检验

    Estimating and Checking for Linear Mixed Effects Models with Longitudinal Data

  15. 高维纵向数据群点的刚体结构检验方法

    A Test for Rigid Structure of the Grouped Points in High-Dimensional Data

  16. 一类纵向数据部分线性模型的估计理论

    Estimation Theory in a Partially Linear Model for Longitudinal Data

  17. 纵向数据分析已经有成熟完善的统计模型和统计推断方法。

    Longitudinal data analysis has sophisticated statistical models and statistical inference methods .

  18. 在纵向数据分析中,人们常常采用混合效应模型。

    The mixed-effects models are popular in the analysis of longitudinal data .

  19. 纵向数据混合效应模型参数估计的强相合性

    Strong consistency of parameter estimates in linear mixed model for longitudinal data

  20. 基于似然函数的纵向数据线性混合模型影响分析

    Likelihood-based influence analysis in linear mixed models for longitudinal data

  21. 线性纵向数据模型中多个个体的联合影响诊断

    Joint influence for multiple subjects in linear longitudinal models

  22. 纵向数据方差参数的检验

    Hypothesis Test of Covariance Parameters in the Longitudinal Model

  23. 纵向数据线性混合效应模型的统计分析

    Statistical Analysis of Linear Mixed Model for Longitudinal Data

  24. 在纵向数据分析中,模型方差的齐性是一个基本假定。

    In longitudinal data analysis , homogeneity of variance is a basic assumption .

  25. 离散型广义非线性纵向数据模型中偏离名义离差的检验及其功效模拟

    Testing for Departures from Nominal Dispersion in Discrete Generalized Nonlinear Models with Longitudinal Data

  26. 生活事件应激对心血管病危险因素影响的纵向数据分析

    Longitudinal data analysis of the effect of provoking life events on cardiovascular risk factors

  27. 由于在纵向数据分析中,对每个个体的观测数目是稀疏的。

    The number of observations for each individual is sparse in the longitudinal data analysis .

  28. 基于纵向数据与突变理论的边坡滑坡预测新方法及其应用

    New method for landslide forecast and its application based on longitudinal data and catastrophic theory

  29. 在第五章,我们提出了用于纵向数据的混合效应模型的矩估计方法。

    In Chapter 5 , we propose a moment estimation method for mixed effects models .

  30. 考虑纵向数据的一般线性混合效应模型。

    The linear mixed model for longitudinal data proposed by Laird and Ware is studied .