Bayesian Method in Linear Model and Constant Time Series Model Using Non- Informative Prior Under Phenology

March 30, 2015

Bayesian Method in Linear Model and Constant Time Series Model Using Non- Informative Prior Under Phenology

Abstract:

Climate Change is very recent topic at global level for discussion for all of us. Phenology is one of the main bio- indicators to track climate change effects on ecosystem. The present study is devoted to derive results of coherent interest in the field of phenology from Bayesian point of view. In this paper we have developed the phenological probability models using linear model and constant time series model. The comparison of both the models has also been done using the concept of residual sum of square and Bayes’ factor.

Author(s):

Vijay Kumar Pandey, Research Scholar in Department of Statistics, University of Lucknow, India-226007
Rajeev Ppandey, Professor, Department of Statistics, University of Lucknow, India-226007
Mayank Trivedi, Research Scholar in Department of Statistics, University of Lucknow, India-226007

DOI:

https://doi.org/10.15415/mjis.2015.32016

Keywords:

Bayesian Analysis, Linear Model, Constant Time Series Model, Phenology, Non-Informative Prior

References:

Berliner, L.M., (2000): Bayesian Climate Change Assesment, Jour. Climate, [3805-3820, 13].

Dose, V. and Menzel, A. (2004): Bayesian Analysis of Climate Change Impacts in Phenology, Global Change Biol., [ 259-272, 17]. http://dx.doi.org/10.1111/j.1529-8817.2003.00731.x

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Leroy, S.S. (1998): Detecting Climate Signals: Some Bayesian Aspects, Jour. of Climate [640-651, 11]. http://dx.doi.org/10.1175/1520-0442(1998)011<0640:DCSSBA>2.0.CO;2

Menzel, A. (2002): Phenology: Its Importance to the Global Change Community, Jour. Climatic Change [379-385, 54].

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