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Strong laws for weighted sums of ψ-mixing random variables and applications in errors-in-variables regression models
HU, Di - Personal Name
CHEN, Pingyan - Personal Name
SUNG , Soo Hak - Personal Name
In this paper, we establish strong laws for weighted sums of identically
distributed ψ-mixing random variables without any conditions on mixing rate. The
classical Kolmogorov strong law of large numbers is extended to weighted sums of
ψ-mixing random variables. Two types of weights are considered for the weighted
sums. These results are applied to the least-squares estimators in the simple linear
errors-in-variables regression model when the errors are ψ-mixing random vectors.
distributed ψ-mixing random variables without any conditions on mixing rate. The
classical Kolmogorov strong law of large numbers is extended to weighted sums of
ψ-mixing random variables. Two types of weights are considered for the weighted
sums. These results are applied to the least-squares estimators in the simple linear
errors-in-variables regression model when the errors are ψ-mixing random vectors.
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| EB00000004355K | Available |
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Content Type
E-Jurnal
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Subject(s)
strong law of large numbers
Weighted sum
ψ-Mixing
Strong consistency
Simple linear errors-in-variables regression model
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Statement of Responsibility
Di Hu 1, Pingyan Chen 1, Soo Hak Sung 2
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