Nonparametric Estimation of the Measurement Error Model Using Multiple Indicators

Tong Li, Quang Vuong

    Research output: Contribution to journalArticle

    Abstract

    This paper considers the nonparametric estimation of the densities of the latent variable and the error term in the standard measurement error model when two or more measurements are available. Using an identification result due to Kotlarski we propose a two-step nonparametric procedure for estimating both densities based on their empirical characteristic functions. We distinguish four cases according to whether the underlying characteristic functions are ordinary smooth or supersmooth. Using the loglog Law and von Mises differentials we show that our nonparametric density estimators are uniformly convergent. We also characterize the rate of uniform convergence in each of the four cases.

    Original languageEnglish (US)
    Pages (from-to)139-165
    Number of pages27
    JournalJournal of Multivariate Analysis
    Volume65
    Issue number2
    DOIs
    StatePublished - May 1998

    Fingerprint

    Measurement Error Model
    Nonparametric Estimation
    Measurement errors
    Empirical Characteristic Function
    Density Estimator
    Nonparametric Estimator
    Latent Variables
    Uniform convergence
    Standard error
    Error term
    Characteristic Function
    Measurement error
    Nonparametric estimation

    Keywords

    • Fourier transformation
    • Measurement error model
    • Multiple indicators
    • Nonparametric density estimation
    • Uniform convergence rate

    ASJC Scopus subject areas

    • Statistics, Probability and Uncertainty
    • Numerical Analysis
    • Statistics and Probability

    Cite this

    Nonparametric Estimation of the Measurement Error Model Using Multiple Indicators. / Li, Tong; Vuong, Quang.

    In: Journal of Multivariate Analysis, Vol. 65, No. 2, 05.1998, p. 139-165.

    Research output: Contribution to journalArticle

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