堪培拉Essay代写:研究人员

2017-02-15 12:55

在过去的几十年中,由于软件拟合多元多级数据不可用的一些研究人员倾向于使用手工方法如EM算法(Kang et al .,1991)。由于开发技术环境、软件等占据,SAS和S +出现在统计领域,提供促进处理多级数据。但是这些包有一个拟合多元多级数据的能力。然而有证据表明在文献中,非线性多元多级模型可以安装使用包如GLLAMM(Rabe-Hesketh,泡菜和Skrondal,2001)和aML(里沃德-潘尼斯,2000)。但它不是灵活处理这个软件。因此MlwiN正在开发的软件已成为自1980年年末以来的修改在英国布里斯托尔大学的为了满足要求。然而,使用MlwiN拟合多元多级模型受到了挑战。戈尔茨坦,木匠和布朗(2014)认为MlwiN有用如果只有当没有冠之为缺失值拟合模型。然而REALCOM软件然后进入领域的统计和提供了灵活性,嫁祸于失踪的MLwiN环境中的值。 MLwiN修改版本的DOS MLn程序,使用一个命令驱动接口。MLwiN拟合提供了灵活性非常大而复杂的模型使用频率论者和贝叶斯估计随着缺失值归罪在一个用户友好的界面。一些特定的高级功能不可用在其他包都包含在这个软件。一般来说,经常收集数据在多个相关的结果。一个重大理论问题,主导了领域多年来建模的危险因素之间的关系和每个结果在一个单独的模型。它可能会导致低效的,因为它忽略了结果的统计学关联和共同预测效果(阿曼、卡马尔和Ambler)(未发表)

堪培拉Essay代写:研究人员

In the past decades, due to the unavailability of the software for fitting multivariate multilevel data some researchers tend to use manual methods such as EM Algorithm (Kang et al., 1991). As a result of developing the technical environment, the software such as STATA, SAS and S plus are emerged in to the Statistical field by providing facilitates to handle the multilevel data. But none of those packages have a capability of fitting multivariate multilevel data. However there is evidence in the literature that nonlinear multivariate multilevel model can be fitted using packages such as GLLAMM (Rabe-Hesketh, Pickles and Skrondal, 2001) and aML (Lillard and Panis, 2000). But it was not flexible to handle this software.

Therefore MlwiN software which has become the under development since late 1980’s was modified at the University of Bristol in UK in order to fulfill that requirement. However, the use of MlwiN for fitting multivariate multilevel models has been challenged by Goldstein, Carpenter and Browne (2014) who concluded that MlwiN was useful if only when fitting the model without imputing for the missing values. However REALCOM software was then came into the field of Statistics and provided the flexibility to impute the missing values in the MLwiN environment.

MLwiN is a modified version of DOS MLn program which uses a command driven interface. MLwiN provides flexibility to fitting very large and complex models using both frequentist and Bayesian estimation along with the missing value imputation in a user friendly interface. Some particular advanced features which are not available in the other packages are included in this software.

In general, data are often collected on multiple correlated outcomes. One major theoretical issue that has dominated the field for many years is modeling the association between risk factors and each outcome in a separate model. It may cause to statistically inefficient since it ignores outcome correlations and common predictor effects (Oman, Kamal and Ambler) (unpublished)

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