• Title/Summary/Keyword: Marma

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The Marma from Bangladesh: A 'de-Arakanized' Community in Chittagong Hill Tracts

  • Htin, Kyaw Minn
    • SUVANNABHUMI
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    • v.7 no.2
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    • pp.133-153
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    • 2015
  • The Rakhine (Arakanese) from present-day Rakhine State (Arakan) in Western Myanmar and the Marma from the Chittagong Hill Tracts (CHT) of Southeast Bangladesh originated from the same region, share the same culture and practice the same religion. However, the people from CHT have developed a distinctive identity and are individualized by a different name "Marma". This development raises a number of historical questions. This paper explores how the Arakanese descendants became "Marma" in Bangladesh.

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Performance Evaluation of $\bar{x}$ and EWMA Control Charts using Bootstrap Technique in the Presence of Correlation (상관관계의 존재하에서 붓스트랩 기법을 이용한 $\bar{x}$ 와 EWMA관리도의 수행도 평가)

  • Shon Han-Deak;Song Suh-Ill
    • Proceedings of the Society of Korea Industrial and System Engineering Conference
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    • 2002.05a
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    • pp.365-370
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    • 2002
  • In this study, according to MARMA(1,0) model which was suggested by Seppala, in case of existing autocorrelation in X control chart and EWMA control chart, the standard method and the non-parametric bootstrap method were compared and analysed using the bootstrap method which use the resampling prediction residual.

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Multivariate Time Series Simulation With Component Analysis (독립성분분석을 이용한 다변량 시계열 모의)

  • Lee, Tae-Sam;Salas, Jose D.;Karvanen, Juha;Noh, Jae-Kyoung
    • Proceedings of the Korea Water Resources Association Conference
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    • 2008.05a
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    • pp.694-698
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    • 2008
  • In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

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