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Martin Durocher, Fateh Chebana, and Taha B. M. J. Ouarda

are linear combinations of basin characteristics. For instance, principal component regression corresponds to multiple regression that is performed on the outputs of a principal component analysis ( Hastie et al. 2009 ). In this method, the outputs of principal component analysis are the intermediate predictors, and the purpose of this substitution is to overcome multicollinearity problems. Other examples that consider intermediate predictors in RFFA are spatial methods ( Archfield et al. 2013

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Paul E. Roundy and William M. Frank

. 3. Methodology a. Independent verification of regression model The companion paper ( RF ) analyzes interactions between ISOe and ISOw anomalies using the regression model described above. Their Fig. 1 shows their composite analysis of these interactions in the longitude– time lag domain. In the current paper we wish to demonstrate that the regression model of RF produces results that are representative of physical processes. We do this by comparing the results of the RF regression

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A. Msilini, P. Masselot, and T. B. M. J. Ouarda

1. Introduction and literature review The main objective of regional frequency analysis (RFA) is the estimation of the return period of extreme hydrological events at target sites where little or no hydrological data are available. Examples of these events include floods and low-flow quantiles which are crucial for infrastructure design and management. In general, RFA comprises two main steps: (i) the delineation of homogenous region (DHR) to determine gauged sites similar to the target one and

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Scott J. Richter and Robert H. Stavn

Rohlf 1995 ; Warton et al. 2006 ). The literature is substantial regarding estimating a linear functional relationship, dating back to at least Pearson (1901) , and several different methods have been proposed. A model II analysis was applied by Sverdrup (1916) in analyzing meteorological variables as early as 1916. Ricker (1973) proposed the use of model II regression in fishery studies. Laws and Archie (1981) asserted the advisability of using model II regression for various field studies

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Samuel S. P. Shen, Nancy Tafolla, Thomas M. Smith, and Phillip A. Arkin

that contribute to the reconstruction skills. The rest of the paper is arranged as follows: section 2 shows the multivariate regression version of the Smith method, section 3 describes the data used in the analysis and the EOF results, section 4 includes results, and section 5 contains conclusions and discussion. 2. Multivariate regression of the Smith reconstruction method a. Smith reconstruction, regression, and error bounds Smith et al. (1996) introduced a reconstruction method for sea

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Pablo Rozas-Larraondo, Iñaki Inza, and Jose A. Lozano

paper, a simple form of kernel nonparametric regression is used to improve forecasts of wind speed coming from the NWP, considering wind direction and wind speed variables to filter out data. This form of regression is particularly suitable for real-time forecasting, because it can be updated to include the most recent data, which makes it a perfect candidate for operational on-demand applications. Wind statistical analysis cannot be performed directly by applying out-of-the-box machine learning or

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Richard I. Cullather and Sophie M. J. Nowicki

identify how the conditions vary regionally across the ice sheet. In this study, the daily MEaSUREs dataset is examined in relation to concurrent conditions using a simple linear regression analysis. The conditions are examined regionally by using defined GrIS basins. Atmospheric general circulation and cloud conditions are described using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Section 2 provides a description of the datasets used and the regression

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Steven C. Chan, Elizabeth J. Kendon, Nigel Roberts, Stephen Blenkinsop, and Hayley J. Fowler

detailed measures of vertical stability and synoptic circulation (e.g., vertical stability at every model grid point for every hour) for regression analysis is self-defeating and amounts to overfitting. The goal is to build simple and elegant relationships that are predictive but not complicated ones that appear to fit well but have low predictive skill and difficult interpretation. Hence we seek general proxies and diagnostics that summarize the overall vertical stability and synoptic weather

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Timothy DelSole and Xiaosong Yang

distribution under the null hypothesis of independence are invariant to reversing x and y . It follows that testing the hypothesis in model (1) is equivalent to testing the hypothesis in the model Thus, testing multivariate independence of x and y by multivariate regression or by canonical correlation analysis yields precisely the same decision rule, regardless of which variables are identified as predictors and predictands. These equivalences are somewhat surprising, given that the regression

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Shuyu Zhang, Thian Yew Gan, and Andrew B. G. Bush

described by statistical measures such as the mean, standard deviation, skewness, and kurtosis. Quantiles, which are widely used in hydrologic frequency analysis, represent the relative magnitudes of particular values in the historical records. For example, in this study an extremely low ice cover is represented by a small quantile, while an extremely high ice cover is represented by a large quantile. Past studies about changes in sea ice are more based on a linear regression that describes the average

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