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On a likelihood-based approach in nonparametric smoothing and cross-validation

Author

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  • Chaudhuri, Probal
  • Dewanji, Anup

Abstract

A likelihood-based generalization of usual kernel and nearest-neighbor-type smoothing techniques and a related extension of the least-squares leave-one-out cross-validation are explored in a generalized regression set up. Several attractive features of the procedure are discussed and asymptotic properties of the resulting nonparametric function estimate are derived under suitable regularity conditions. Large sample performance of likelihood-based leave-one-out cross validation is investigated by means of certain asymptotic expansions.

Suggested Citation

  • Chaudhuri, Probal & Dewanji, Anup, 1995. "On a likelihood-based approach in nonparametric smoothing and cross-validation," Statistics & Probability Letters, Elsevier, vol. 22(1), pages 7-15, January.
    • Handle: RePEc:eee:stapro:v:22:y:1995:i:1:p:7-15
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    Cited by:

    1. Jeff Racine, 2000. "Nonparametric Estimation of Conditional Distributions in the Presence of Continuous and Categorical Data," Econometric Society World Congress 2000 Contributed Papers 0713, Econometric Society.

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