A statistical assessment on abrupt change and trend analysis of rice production
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The most common method for studying historical data is to use regression methods and predictive modeling on time series data. The parametric methodology for time series data analysis is a customary method when the data are available on a continuous scale. However, most of the time, the data availability may be on a categorical or ordinal scale. Hence, the nonparametric methodology is more rational in handling time series data. This study considers two prominent non-parametric methods, namely Pettitt’s test and Buishand’s range test. In particular, we examine an abrupt change in the annual data of rice production during the period 1980-2020 by these methods. The study continued to assess the performance of rice production with the presence and absence of trend as performed by the Mann-Kendall test and the trend measured by Sen’s slope estimator. According to the findings, the second time period’s average growth rate has improved slightly but not as significantly as the first time period’s.
keywordsChange Point Detection, Trend Analysis, Non-parametric Methods, Statistical SignificanceAuthors biographyDepartment of Statistics - Vignan’s Foundation for Science, Technology & Research, VADLAMUDI - 522213 - Guntur, Andhra Pradesh (India) (e-mail: drkalpanastat@gmail.com).Area of Decision Sciences - Indian Institute of Management Sirmaur - PAONTA SAHIB, 173025 - Sirmaur, Himachal Pradesh (India) (e-mail: kkpaidipati@iimsirmaur.ac.in). Department of Mathematics - Université de Caen-Normandie - CAEN 14000 (France) (e-mail: christophe.chesneau@unicaen.fr). |
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