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Small area estimation for semicontinuous data.

Hukum Chandra1, Ray Chambers2

  • 1Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi, 110012, India.

Biometrical Journal. Biometrische Zeitschrift
|June 26, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new small area estimation (SAE) method for semicontinuous survey data with excess zeros. The proposed two-part model improves estimation efficiency for these complex data types.

Keywords:
Mean squared errorParametric bootstrapSkewed dataSmall area estimationZero-inflated

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Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Survey data frequently include semicontinuous variables, characterized by a fixed value (often zero) and a skewed distribution for positive values.
  • Traditional small area estimation (SAE) methods using linear mixed models are often suboptimal for such data.
  • Addressing excess zeros and skewed distributions is crucial for accurate statistical inference.

Purpose of the Study:

  • To develop and evaluate novel small area estimation (SAE) techniques for semicontinuous variables.
  • To propose a two-part random effects model that effectively handles excess zeros and skewed positive values.
  • To provide a reliable method for estimating the mean squared error (MSE) of the proposed SAE estimator.

Main Methods:

  • A two-part random effects model is employed, combining a generalized linear mixed model for excess zeros and a linear mixed model on the log scale for positive values.
  • The probability of a non-zero observation is modeled first, followed by modeling the distribution of the positive values.
  • A parametric bootstrap approach is proposed for estimating the mean squared error (MSE) of the small area estimates.

Main Results:

  • The proposed SAE method demonstrates improved efficiency for semicontinuous data compared to standard approaches.
  • Empirical results confirm the effectiveness of the two-part model in capturing the data's characteristics.
  • The parametric bootstrap method provides accurate MSE estimates for the proposed small area estimator.

Conclusions:

  • The novel two-part random effects model offers an efficient approach for small area estimation (SAE) with semicontinuous data.
  • This method effectively addresses the challenges posed by excess zeros and skewed distributions in survey data.
  • The proposed technique enhances the reliability of small area estimates and provides a robust MSE estimation strategy.