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Are the temperature of Indian cities Increasing?: Some Insights Using Change Point Analysis with Functional Data

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  • Laha, A. K.
  • Rathi, Poonam

Abstract

In recent years there has been considerable concern expressed worldwide regarding increase in temperature popularly called the global warming problem. In this paper we examine monthly temperature data of nine Indian cities for the period 1961 to 2013. We introduce a new Gaussian process based method for change point detection with functional data and use it to investigate the existence of change point for the temperature data series of nine Indian cities. It is found that there has been a rise in the average temperature for eight of the nine cities during this period. The magnitude of warming is found not to be uniform but vary across cities located in different parts of India. The cities located in hilly areas is seen to have warmed more than those located in the plains. The estimated change points for the eight cities are not identical but most of them are in the period 1994 - 2001. The fi ndings suggest that immediate policy measures are required to ensure that no further warming happens in these cities.

Suggested Citation

  • Laha, A. K. & Rathi, Poonam, 2017. "Are the temperature of Indian cities Increasing?: Some Insights Using Change Point Analysis with Functional Data," IIMA Working Papers WP 2017-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    • Handle: RePEc:iim:iimawp:14577
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    References listed on IDEAS

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    1. István Berkes & Robertas Gabrys & Lajos Horváth & Piotr Kokoszka, 2009. "Detecting changes in the mean of functional observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 927-946, November.
    2. Hans-Georg Müller & Wenjing Yang, 2010. "Dynamic relations for sparsely sampled Gaussian processes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 1-29, May.
    3. Mullen, Katharine M., 2014. "Continuous Global Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i06).
    4. Hans-Georg Müller & Wenjing Yang, 2010. "Rejoinder on: dynamic relations for sparsely sampled Gaussian processes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 60-67, May.
    5. Meiring, Wendy, 2007. "Oscillations and Time Trends in Stratospheric Ozone Levels: A Functional Data Analysis Approach," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 788-802, September.
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