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Jakob Runge, Vladimir Petoukhov, and Jürgen Kurths

). In a first step, the framework of graphical models is used to detect the existence of (Granger) causal interactions yielding the interaction time delays, while in a second step a certain partial correlation and a regression measure are introduced that allow one to specifically quantify the strength of an interaction mechanism in a well interpretable way. We will demonstrate that our approach goes beyond the pure graphical models analysis of Ebert-Uphoff and Deng (2012a) and enables us to

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Randall Mullen, Lucy Marshall, and Brian McGlynn

analysis described in this paper. To determine if subsetting was necessary for the beta regression model, all sites were analyzed with yearday converted into sine and cosine components. This eliminated the need for separate analyses for each season. Precipitation was entered as a continuous variable eliminating the need for separate analyses for wet and dry days. In this way, an entire dataset can be evaluated in one model. Total RMSE from the stratified models was compared to the RMSE for the combined

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Yulia R. Gel

temporally weighted spatial composition of recent observations over a “neighborhood” of weather observing sites while taking into account land use categorization. The CART–ACE method is based on the spatiotemporal analysis of bias using modern statistical nonparametric regression techniques such as alternative conditional expectation (ACE) and regression trees. Both approaches can be applied to any site of interest without any history of bias measurement at that particular site. Although in general the

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F. Chebana, C. Charron, T. B. M. J. Ouarda, and B. Martel

study of their homogeneity. However, much less attention has been dedicated to the development of new regional estimation methods. In the present study, canonical correlation analysis (CCA) is used to delineate homogenous regions. In GREHYS (1996b) , it was shown that this method produced the best performances in comparison to other ones. Among RFA estimation methods, regression models and index-flood models are commonly used. GREHYS (1996b) showed that their performances are equivalent and are

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Julie L. Demuth, Jeffrey K. Lazo, and Rebecca E. Morss

. Measuring where individuals fall along factor scales also provides information on respondent heterogeneity that we explore further in the regression analysis ( section 4 ). We performed 1 the factor analysis using the principle axis factoring extraction method with an orthogonal varimax rotation. To select criteria for the factor analysis, we followed guidance from Hatcher (2007) and Garson (2009a) with the goals of our study in mind. We retained factors that accounted for at least 10% of the

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Masahiro Ryo, Oliver C. Saavedra Valeriano, Shinjiro Kanae, and Tinh Dang Ngoc

mean the discharge simulated and observed, and the bar over Q indicates the average value over n . e. Multiple regression analysis for NSE Multiple linear regression analysis is finally applied for evaluating the simulation with the different precipitation datasets in terms of the NSE value. We aim to detect under which conditions 1) the performance of the discharge simulation is high during flood events, 2) the assumption of uniform distribution of precipitation ( P uni ) degrades the

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Luisito Bertinelli and Eric Strobl

remainder of the paper is organized as follows. In the next section we describe our datasets, and in section 3 we provide some summary statistics. In section 4 we provide our regression analysis. We give conclusions in section 5 . 2. Data and methods a. Nightlight data As a proxy for economic activity at the local level, we resort to data derived from satellite images of nightlight. To be more specific, in the early 1960s the U.S. military launched a weather satellite program that was intended to

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M. Tugrul Yilmaz and Wade T. Crow

matching techniques are perhaps the most common. A handful of studies have applied rescaling based on least squares regression techniques ( Crow et al. 2005 ; Crow and Zhan 2007 ) but failed to offer any clear rationale for this choice. Additionally, signal variance-based rescaling, typically applied as a preprocessing step in triple collocation analysis ( Stoffelen 1998 ), also provides a means to rescale datasets using three independent estimates of the same variable. However, this approach has not

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Adèle Révelard, Claude Frankignoul, and Young-Oh Kwon

and Greenland Seas and the Sea of Okhotsk, and decrease in the Labrador and Bering Seas) for late winter. Hence, only these PCs are included in the analysis discussed below. In total, 12 oceanic explanatory variables or regressors are considered. They are not independent and the associated SST anomalies extend much beyond their domain of definition. This is illustrated for August–October (ASO) in Fig. 3 (left), where the SST pattern a j associated with j th regressor is obtained from the

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Yuchuan Lai and David A. Dzombak

. Background—Evaluation of regional climate trends Among a range of statistical approaches to assess the time series of past climate records and differentiate the anthropogenic changes from the climate variability, often referred as “exploratory data analysis” ( Schneider and Held 2001 ), conventional linear regression remains as the mostly used approach. It is worth noting that the various statistical approaches may have different definitions of climate variability and the results for underlying trend are

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