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Smoothing dependent observations

Author

Listed:
  • Fraiman, R.
  • Meloche, J.

Abstract

We consider the problem of nonparametric regression when the error process has a continuous covariance structure. We show that smoothers are generally not consistent. If measurements from independent trajectories are available, smoothing the averaged trajectories can be detrimental asymptotically.

Suggested Citation

  • Fraiman, R. & Meloche, J., 1994. "Smoothing dependent observations," Statistics & Probability Letters, Elsevier, vol. 21(3), pages 203-214, October.
  • Handle: RePEc:eee:stapro:v:21:y:1994:i:3:p:203-214
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    Cited by:

    1. Dale Zimmerman & Vicente Núñez-Antón & Timothy Gregoire & Oliver Schabenberger & Jeffrey Hart & Michael Kenward & Geert Molenberghs & Geert Verbeke & Mohsen Pourahmadi & Philippe Vieu & Dela Zimmerman, 2001. "Parametric modelling of growth curve data: An overview," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(1), pages 1-73, June.
    2. Ferreira García, María Eva & Núñez Antón, Vicente Alfredo & Rodríguez Poo, Juan M., 1999. "Two-Stage Nonparametric Regression for Longitudinal Data," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    3. Mario Francisco-Fernandez & Juan Vilar-Fernandez, 2004. "Weighted Local Nonparametric Regression with Dependent Errors: Study of Real Private Residential Fixed Investment in the USA," Statistical Inference for Stochastic Processes, Springer, vol. 7(1), pages 69-93, March.

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