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Aneesh Goly, Ramesh S. V. Teegavarapu, and Arpita Mondal

variable. Thus, MLR is used to predict the values of a hydrologic variable Y given a set of p predictor variables ( ). Regression analysis is performed using principal components and membership values obtained from the fuzzy c -means clustering method. The following equations involving the seasonal components are used for regression analysis ( Ghosh and Mujumdar 2006 ): where pc is the principal components; μ represents the membership values in each cluster; t is the serial number of the data

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Ashok K. Mishra and Vijay P. Singh

in Texas considering PHDI as the hydrological drought index and precipitation and temperature as meteorological variables. The length of burn-in period is 10 000 and the number of iteration was chosen as 50 000 during the sampling process of the Bayesian regression analysis. The simulation of PHDI was carried out considering 1900–55 as training period and 1956–2000 as testing period ( Figure 3 ). It was observed that the wavelet–Bayesian regression approach was able to better match the pattern

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Mark R. Jury

) Regression of detrended Agulhas SST onto continuous monthly vegetation fraction anomalies from 1981 to 2006. State labels are given in (a); shaded area refers to lower ship data density. The scale is in mm month −1 °C −1 in (a)–(e) and the fraction is °C −1 in (f). Since most research has underscored an apparent positive influence of the Agulhas Current on South African rainfall anomalies, the statistical analysis is extended to consider vegetation anomalies 1981–2006 ( Figure 3f ). Again a widespread

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Shouraseni Sen Roy

the subcontinent’s winter rainfall are dominated by the relative location of the ascending arm of the Hadley cell above the adjacent Indian Ocean near Malaysia and Indonesia ( Das 1986 ). Winter precipitation over the subcontinent is a result of the movement of western disturbances in the form of low pressure systems, especially in northwestern India. The results of the regression analysis of the impact of NAO on peak winter rainfall (PWR), which includes the months of January and February

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Kwang-Yul Kim, James J. O'Brien, and Albert I. Barcilon

modes of variability associated with the ENSO system from the depth of the ocean to the tropopause. To this end, physically consistent patterns of several key variables are derived via a regression analysis. Based on the results in this study, inferences are made regarding the evolution of the general circulation structures during El Niños and La Niñas. This work provides a physical and dynamical interpretation of the biennial oscillation over the tropical Pacific, and offers a likely reason for the

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Yaqian He and Eungul Lee

the multivariate linear regression analysis. 2.2.2. Multivariate linear regression analysis To examine the roles of SST and land surface (i.e., NDVI) variables in summer rainfall variability over the Sahel, we implement the multivariate linear regression analysis. We first apply the multivariate linear regression model with SST alone and then integrate the NDVI information. In the multivariate regression model, the area-averaged mean summer precipitation over the Sahel is used as the dependent

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Mohammad H. Mokhtari, Ibrahim Busu, Hossein Mokhtari, Gholamreza Zahedi, Leila Sheikhattar, and Mohammad A. Movahed

estimating broadband surface inherent albedos from satellite data without atmospheric information ( Liang et al. 1999 ). Linear regression analysis is the most commonly used method to convert satellite-derived narrowband albedo into broadband albedo ( Key 1996 ; Stroeve et al. 1997 ; Valiente et al. 1995 ). Several models with different band combinations for various satellite data have been developed via a regression analysis of the surface spectra reflectance and reflective spectral region of

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Souleymane Fall, Dev Niyogi, and Fredrick H. M. Semazzi

estimated using linear regression analysis and interpolation maps for the slopes. According to the t test, no statistically significant trend is shown for both rainfall and the number of rainy days. The spatial distribution of mean annual temperatures is driven by oceanic and continental influences. Temperatures generally decrease eastward, but during the wet season, a N–S gradient is observed. Based on the coefficient of variation, the greatest variability occurs in the western and northern regions

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Lei Meng and Yanjun Shen

SM conditions in previous 6 months. The SPI was calculated at each station and then spatially averaged to represent soil moisture conditions in each region. 2.4. Quantile regression In statistical analysis, the ordinary least squares (OLS) regression is the most commonly used method to estimate rates of change in the mean of the response variable distribution as some function of a set of predictor variables. However, the OLS is not suitable for regression models with heterogeneous variance

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Ellen Jasinski, Douglas Morton, Ruth DeFries, Yosio Shimabukuro, Liana Anderson, and Matthew Hansen

or absence of mechanized agriculture is set as the dependent variable such that a Y = 1 outcome refers to a pixel classified as cropland, and Y = 0 indicates a different land-cover class. Two logistic regressions were conducted. The first was a “snapshot” analysis for understanding how physical landscape characteristics are correlated with the general spatial distribution of mechanized agriculture as shown in the 2003 land-cover classification. The second was an analysis to examine how

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