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Kernel regression estimates of growth curves using nonstationary correlated errors

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Author Info

  • Ferreira, Eva
  • Núñez-Antón, Vicente
  • Rodríguez-Póo, Juan

Abstract

We study the nonparametric estimation of the average growth curve under a very general parametric form of the covariance structure that allows for monotone transformation of the time scale. We also investigate the properties of optimal bandwidth selection methods and compare the results with those obtained under stationarity.

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Bibliographic Info

Article provided by Elsevier in its journal Statistics & Probability Letters.

Volume (Year): 34 (1997)
Issue (Month): 4 (June)
Pages: 413-423

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Handle: RePEc:eee:stapro:v:34:y:1997:i:4:p:413-423

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Related research

Keywords: Bandwidth selection Longitudinal data Nonstationary errors Semiparametric estimators;

References

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  1. Hart, Jeffrey D. & Wehrly, Thomas E., 1993. "Consistency of cross-validation when the data are curves," Stochastic Processes and their Applications, Elsevier, vol. 45(2), pages 351-361, April.
  2. Altman, Naomi Simone, 1993. "Estimating error correlation in nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 18(3), pages 213-218, October.
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Citations

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Cited by:
  1. Rodríguez Poo, Juan M. & Núñez Antón, Vicente Alfredo & Ferreira García, María Eva, 1999. "Two-Stage Nonparametric Regression for Longitudinal Data," BILTOKI 1999-01, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
  2. Karim Benhenni & Mustapha Rachdi & Yingcai Su, 2013. "The effect of the regularity of the error process on the performance of kernel regression estimators," Metrika, Springer, vol. 76(6), pages 765-781, August.
  3. Vicente Núñez-Antón & Juan Rodríguez-Póo & Philippe Vieu, 1999. "Longitudinal data with nonstationary errors: a nonparametric three-stage approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 8(1), pages 201-231, June.
  4. Benhenni, K. & Rachdi, M., 2006. "Nonparametric estimation of the regression function from quantized observations," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3067-3085, July.

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