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Nonparametric detection of correlated errors

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  • Tae Yoon Kim

Abstract

In regression problems it is hard to detect correlated errors since the errors are not observed. In this paper, a nonparametric method is proposed for the detection of correlated errors when the design points are equally spaced. It turns out that the first-order sample autocovariance of the residuals from the kernel regression estimates provides essential information about correlated errors and its bootstrap is quite effective in implementing such information. Copyright Biometrika Trust 2004, Oxford University Press.

Suggested Citation

  • Tae Yoon Kim, 2004. "Nonparametric detection of correlated errors," Biometrika, Biometrika Trust, vol. 91(2), pages 491-496, June.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:2:p:491-496
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    Cited by:

    1. Liu, Sisheng & Kong, Xiaoli, 2022. "A generalized correlated Cp criterion for derivative estimation with dependent errors," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    2. Kim, Tae Yoon & Park, Byeong U. & Moon, Myung Sang & Kim, Chiho, 2009. "Using bimodal kernel for inference in nonparametric regression with correlated errors," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1487-1497, August.
    3. Tae Yoon Kim & Zhi‐Ming Luo, 2010. "Central limit theorems for nonparametric estimators with real‐time random variables," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 337-347, September.
    4. K De Brabanter & F Cao & I Gijbels & J Opsomer, 2018. "Local polynomial regression with correlated errors in random design and unknown correlation structure," Biometrika, Biometrika Trust, vol. 105(3), pages 681-690.

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