The Influence On The Statistical Results Of Model Conditions In Liner Model

Linear model is a kind of statistical model and have a long history and wide applications. The inference results interpretation is an important aspect in model statistical inference. In this paper, the updates of parameter estimation which come from the changes of model components are explored. And it is benefit to result interpretation. On the base of effect analysis of model components, we emphasis the use of additional information introduced by unknown linear parameter. Furthermore, linear inequality restriction of linear parameter is discussed, and the difference between additional information and pure restriction in inference procedure and strategy is clarified.In this paper, linear zero function (LZF) of linear model and projection operator of linear space are main tools. The conclusions are:additional information in the form of linear equality maintain estimator unbiased and decrease the variance of estimator; additional information in the form of inequality, nonlinear equality and contradicting equality decrease the variance of estimator, but bring bias to it.