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中山大学岭南学院高级计量经济学课件(II:Panel Data)CH2 The Seemingly Unrelated Regression Model
中山大学岭南学院 高级计量经济学 课件(II:Panel Data) CH2 The Seemingly Unrelated Regression Model
2017/6/14
中山大学岭南学院高级计量经济学课件(II:Panel Data)CH2 The Seemingly Unrelated Regression Model。
中山大学岭南学院高级计量经济学课件(I)CH4 Large Sample Distribution Theory and Large Sample Properties of the Classical Regression Model
中山大学岭南学院 高级计量经济学 课件(I) CH4 Large Sample Distribution Theory and Large Sample Properties of the Classical Regression Model
2017/6/14
中山大学岭南学院高级计量经济学课件(I)CH4 Large Sample Distribution Theory and Large Sample Properties of the Classical Regression Model。
中山大学岭南学院高级计量经济学课件(I)CH1 The Classical Multiple Regression Model
中山大学岭南学院 高级计量经济学 课件(I) CH1 The Classical Multiple Regression Model
2017/6/14
中山大学岭南学院高级计量经济学课件(I)CH1 The Classical Multiple Regression Model。
An Exact Test for Multiple Inequality and Equality Constraints in the Linear Regression Model
Multiple Inequality Equality Constraints
2015/7/31
In this article we consider the linear regression model y = X,B + a,
where e is N(O, a21). In this context we derive exact tests of the form
H: Rft ? r versus K: f E R K for the case in which a2 i...
Minimax lower bound for kink location estimators in a nonparametric regression model with long-range dependence
nonparametric regression long-range dependence kink minimax
2011/9/22
Abstract: In this paper, a lower bound is determined in the minimax sense for change point estimators of the first derivative of a regression function in the fractional white noise model. Similar mini...
On estimating parameters of censored generalized Poisson regression model
Generalized Poisson regression Mixture model
2010/9/21
When the sampling variance of a count variable Y is significantly greater or less than that predicted by an expected probability distribution, Y is said to be over- or underdispersed, respectively. A ...
Regression, model misspecification and causation, with pedagogical demonstration
Regression variable omission model incomplete bias
2010/9/20
This paper shows, by a proposition and a numerical example, how a classic simple or multiple normal regression can achieve with 0.99 probability a near perfect fit to a random sample of any size but d...