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Nonparametric Mixture of Regression Models.

Mian Huang, Runze Li, Shaoli Wang

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    |December 24, 2013
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    Summary
    This summary is machine-generated.

    We introduce new regression models for analyzing US house price data. These nonparametric finite mixture models offer improved estimation and statistical properties for economic analysis.

    Keywords:
    EM algorithmKernel regressionMixture of regression modelsNonparametric regression

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    Area of Science:

    • Econometrics
    • Statistical Modeling
    • Data Analysis

    Background:

    • US house price index data presents complex patterns.
    • Existing regression models may not fully capture these complexities.

    Purpose of the Study:

    • To propose novel nonparametric finite mixture of regression models.
    • To address identifiability and estimation challenges in housing market analysis.
    • To provide a robust statistical framework for house price index data.

    Main Methods:

    • Development of nonparametric finite mixture of regression models.
    • Application of kernel regression for estimation.
    • Systematic study of estimator sampling properties, including asymptotic normality.
    • Implementation of a modified Expectation-Maximization (EM) algorithm.

    Main Results:

    • Established identifiability for the proposed models.
    • Developed and validated an estimation procedure using kernel regression.
    • Demonstrated asymptotic normality of the estimators.
    • Showcased the modified EM algorithm's ascent property preservation.
    • Validated the methodology through Monte Carlo simulations and US house price data analysis.

    Conclusions:

    • The proposed nonparametric finite mixture of regression models are suitable for analyzing US house price index data.
    • The developed estimation procedure and modified EM algorithm provide reliable and statistically sound results.
    • This methodology offers a valuable tool for understanding housing market dynamics.