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Spiking problem in monotone regression: Penalized residual sum of squares

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  • Pal, Jayanta Kumar

Abstract

We consider the estimation of a monotone function at its end-point, where the least square estimate is inconsistent. The least square criterion is penalized to achieve consistency. The limit distribution for the residual sum of squares is derived, to construct confidence intervals.

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  • Pal, Jayanta Kumar, 2008. "Spiking problem in monotone regression: Penalized residual sum of squares," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1548-1556, September.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:12:p:1548-1556
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    Cited by:

    1. Ana Colubi & J. Santos Dominguez-Menchero & Gil Gonzalez-Rodriguez, 2018. "New designs to consistently estimate the isotonic regression," Computational Statistics, Springer, vol. 33(2), pages 639-658, June.

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