Statistics Essay代写:回归分析
Keywords:Statistics Essay代写
在回归分析的应用,经常的不寻常的观察数据集包含异常值(噪音)或有影响力的观察。这些观察残差,影响较大的参数回归系数和回归分析,成为误导和解释结果的来源。因此是非常重要的考虑这些疑似仔细观察和做了一个决定,这些观察应包括或从分析中删除。 在回归分析中,基本步骤是确定一个或多个观察可以影响分析的结果和解释。如果回归分析有一个独立的变量,那么很容易检测观察依赖和独立变量,利用散点图、箱线图和残余情节等等。但图解法来识别异常和/或有影响力的观察是一个主观的方法。也是众所周知,在多个异常值的存在可以屏蔽或淹没的效果。掩蔽(假阴性)发生在一个偏远的子集仍未被发现的另一个的存在,通常相邻的子集。淹没(假阳性)发生在通常的观察是错误地认定为异常值通常在另一个远程观测的子集。 在目前的研究中,一些众所周知的诊断与识别多个有影响力的观察。为此,首先,稳健回归方法用于识别影响力的观察泊松回归,然后遵循稳健回归方法发现的观察是真正有影响力的观察,一些诊断措施基于单一情况下删除方法像皮尔逊卡方检验,异常残余,帽子矩阵,可能残留试验,库克的距离,差异,β平方差异被认为是但在掩蔽的存在和淹没诊断基于单一情况下删除无法识别异常和有影响力的观察。因此消除或者减少屏蔽和淹没现象一些组删除方法;广义标准化皮尔逊残留,广义的差异,广义平方不同β。
Statistics Essay代写:回归分析
In the application of regression analysis, often the data set consist of unusual observations which are either outliers (noise) or influential observations. These observations may have large residuals and affect the parameters of the regression co-efficient and the whole regression analysis and become the source of misleading results and interpretations. Therefore it is very important to consider these suspected observations very carefully and made a decision that either these observations should be included or removed from the analysis.
In regression analysis, the basic step is to determine whether one or more observations can influence the results and interpretations of the analysis. If the regression analysis have one independent variable, then it is easy to detect observations in dependent and independent variables by using scatter plot, box plot and residual plot etc. But graphical method to identify outlier and/or influential observation is a subjective approach. It is also well known that in the presence of multiple outliers there can be a masking or swamping effect. Masking (false negative) occurs when an outlying subset remains undetected due the presence of another, usually adjacent subset. Swamping (false positive) occurs when usual observation is incorrectly identified as outlier in the presence of another usually remote subset of observations.
In the present study, some well known diagnostics are compared to identify multiple influential observations. For this purpose, first, robust regression methods are used to identify influential observation in Poisson regression, then to conform that the observations identified by robust regression method are genuine influential observations, some diagnostic measures based on single case deletion approach like Pearson chi-square, deviance residual, hat matrix, likelihood residual test, cook’s distance, difference of fits, squared difference in beta are considered but in the presence of masking and swamping diagnostics based on single case deletion fail to identify outlier and influential observations. Therefore to remove or minimize the masking and swamping phenomena some group deletion approaches; generalized standardized Pearson residual, generalized difference of fits, generalized squared difference in beta are taken.
