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Complex data binding linear model under statistical inference

Author: YangXuJia
Tutor: WeiChuanHua
School: Central University for Nationalities
Course: Probability Theory and Mathematical Statistics
Keywords: Return Measurement error Variables with missing values Linear constraint Chi-square distribution
CLC: O212.1
Type: Master's thesis
Year: 2010
Downloads: 33
Quote: 0
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Abstract


This article first linear regression model development and gives an overview of the main properties of introduced regression coefficients with constraints linear regression model statistical inference . On this basis, the measurement error model ( independent variables including errors , can be accurately observed dependent variable linear model ) and variables with missing values ??linear model ( the dependent variable with missing values ??, since the variables can be fully observed linear model ) were studied . Describes the development of these two models are given two complex data model parameter estimation , the parameters to be estimated with the case under the constraints through Lagrange multipliers derived constrained minimum parameters to be estimated squares estimate, respectively, based on the constraints of the original hypothesis and it is assumed that the residual sum of squares of the difference between thinking and Wald methods to construct a test statistic to test for constraints , obtained in the constraint condition is satisfied next we construct the test statistic follows chi-square distribution , and investigated by a computer simulation of the proposed test method. Finally, in the full text of this article summarizes the work done , and the regression model for future development were discussed.

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CLC: > Mathematical sciences and chemical > Mathematics > Probability Theory and Mathematical Statistics > Mathematical Statistics > General mathematical statistics
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