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Comparing distributions with bootstrap techniques: An application to global solar radiation

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  • Mora, Juan
  • Mora-López, Llanos

Abstract

A statistic to test whether the distributions of two observable variables are similar is proposed, where two distributions are defined as similar if they are the same except for a change in location and/or scale. The test statistic for similarity that is proposed extends the Kolmogorov–Smirnov statistic that is used to test for homogeneity of two samples, but it requires the use of a smooth bootstrap procedure to compute critical values. The application of the similarity test to the analysis of global solar radiation data from various Spanish regions reveals that the vast majority of distributions that can be compared are not homogeneous, but in many case there is no evidence to reject that they are similar. In practice, this implies that the use of prediction and simulation models that depend on global solar radiation data can be generalized to a wide variety of regions with almost no cost.

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  • Mora, Juan & Mora-López, Llanos, 2010. "Comparing distributions with bootstrap techniques: An application to global solar radiation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 811-819.
    • Handle: RePEc:eee:matcom:v:81:y:2010:i:4:p:811-819
    DOI: 10.1016/j.matcom.2010.09.004
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    References listed on IDEAS

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    1. Yang, Zheng & Tian, Zheng & Yuan, Zixia, 2008. "Small sample improvements in the threshold cointegration test using residual-based moving block bootstrap," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(4), pages 507-513.
    2. Morimune, K. & Hoshino, Y., 2008. "Testing homogeneity of a large data set by bootstrapping," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 292-302.
    3. Melly, Blaise, 2005. "Decomposition of differences in distribution using quantile regression," Labour Economics, Elsevier, vol. 12(4), pages 577-590, August.
    4. Greiner, R, 2004. "Systems framework for regional-scale integrated modelling and assessment," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 41-51.
    5. Mora, Juan, 2005. "Comparing distribution functions of errors in linear models: A nonparametric approach," Statistics & Probability Letters, Elsevier, vol. 73(4), pages 425-432, July.
    6. Chan, W.S & Cheung, S.H & Wu, K.H, 2004. "Multiple forecasts with autoregressive time series models: case studies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(3), pages 421-430.
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    Cited by:

    1. Antonanzas-Torres, F. & Sanz-Garcia, A. & Martínez-de-Pisón, F.J. & Perpiñán-Lamigueiro, O., 2013. "Evaluation and improvement of empirical models of global solar irradiation: Case study northern Spain," Renewable Energy, Elsevier, vol. 60(C), pages 604-614.

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