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Sufficient conditions for the strong consistency of least squares estimator with α-stable errors

100%

Mexia J., da Silva J.

Discussiones Mathematicae Probability and Statistics

2007

tom 27

nr 1-2

27-45

Let $Y_{i} = x_{i}^{T}β + e_{i}$, 1 ≤ i ≤ n, n ≥ 1 be a linear regression model and suppose that the random errors e₁, e₂, ... are independent and α-stable. In this paper, we obtain sufficient conditions for the strong consistency of the least squares estimator β̃ of β under additional assumptions on the non-random sequence x₁, x₂,... of real vectors.

Least squares estimator consistency: a geometric approach

88%

Mexia J., da Silva J.

Discussiones Mathematicae Probability and Statistics

2006

tom 26

nr 1

19-45

Consistency of LSE estimator in linear models is studied assuming that the error vector has radial symmetry. Generalized polar coordinates and algebraic assumptions on the design matrix are considered in the results that are established.

Ograniczanie wyników

2 Discussiones Mathematicae Probability and Statistics

2 Mexia J.

2 da Silva J.

1 2007

1 2006

Wyniki wyszukiwania

Sufficient conditions for the strong consistency of least squares estimator with α-stable errors

Least squares estimator consistency: a geometric approach